image string | id string | family_id string | domain string | question string | answer string | completion string | completion_mode string | task_family string | task_subtype string | chart_type string | chart_title string | x_label string | y_label string | unit string | visual_difficulty string | difficulty_factors list | operation string | evidence list | referenced_labels list | labels list | values list | axis_y_min float64 | axis_y_max float64 | near_tie_delta float64 | prompt string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
images/finance-0000-clean.png | dv-0000-clean-00 | finance-0000 | finance | According to the chart, which quarter has the highest operating expenses? | Q4 2025 | Answer: Q4 2025
Evidence: Q1 2025: 20; Q2 2025: 80; Q3 2025: 191; Q4 2025: 193
Reasoning: Compare every category and take the largest. | structured | ranking | highest | grouped_bar | Operating expenses by quarter | Quarter | Operating expenses | million USD | easy | [] | argmax | [
"Q1 2025: 20",
"Q2 2025: 80",
"Q3 2025: 191",
"Q4 2025: 193"
] | [
"Q1 2025",
"Q2 2025",
"Q3 2025",
"Q4 2025"
] | [
"Q1 2025",
"Q2 2025",
"Q3 2025",
"Q4 2025"
] | [
20,
80,
191,
193
] | 0 | 262.5 | 2 | grouped bar chart: Operating expenses by quarter
Horizontal axis: Quarter
Vertical axis: Operating expenses (million USD)
Quarter | Operating expenses | Benchmark
----------------------------------------
Q1 2025 | 20 | 250
Q2 2025 | 80 | 192
Q3 2025 | 191 | 127
Q4 2025 | 193 | 45
According to the chart, which quarter... |
images/finance-0000-clean.png | dv-0000-clean-01 | finance-0000 | finance | According to the chart, which quarter has the lowest operating expenses? | Q1 2025 | Q1 2025. The relevant evidence is Q1 2025: 20, Q2 2025: 80, Q3 2025: 191, Q4 2025: 193. Compare every category and take the smallest. | detailed | ranking | lowest | grouped_bar | Operating expenses by quarter | Quarter | Operating expenses | million USD | easy | [] | argmin | [
"Q1 2025: 20",
"Q2 2025: 80",
"Q3 2025: 191",
"Q4 2025: 193"
] | [
"Q1 2025",
"Q2 2025",
"Q3 2025",
"Q4 2025"
] | [
"Q1 2025",
"Q2 2025",
"Q3 2025",
"Q4 2025"
] | [
20,
80,
191,
193
] | 0 | 262.5 | 2 | grouped bar chart: Operating expenses by quarter
Horizontal axis: Quarter
Vertical axis: Operating expenses (million USD)
Quarter | Operating expenses | Benchmark
----------------------------------------
Q1 2025 | 20 | 250
Q2 2025 | 80 | 192
Q3 2025 | 191 | 127
Q4 2025 | 193 | 45
According to the chart, which quarter... |
images/finance-0000-clean.png | dv-0000-clean-02 | finance-0000 | finance | How much higher was the operating expenses in Q3 2025 than in Q1 2025? | 171 | 171. The relevant evidence is Q3 2025: 191, Q1 2025: 20. Read both values and subtract the second from the first. | detailed | quantitative_reasoning | difference | grouped_bar | Operating expenses by quarter | Quarter | Operating expenses | million USD | easy | [] | subtraction | [
"Q3 2025: 191",
"Q1 2025: 20"
] | [
"Q3 2025",
"Q1 2025"
] | [
"Q1 2025",
"Q2 2025",
"Q3 2025",
"Q4 2025"
] | [
20,
80,
191,
193
] | 0 | 262.5 | 2 | grouped bar chart: Operating expenses by quarter
Horizontal axis: Quarter
Vertical axis: Operating expenses (million USD)
Quarter | Operating expenses | Benchmark
----------------------------------------
Q1 2025 | 20 | 250
Q2 2025 | 80 | 192
Q3 2025 | 191 | 127
Q4 2025 | 193 | 45
How much higher was the operating exp... |
images/finance-0000-clean.png | dv-0000-clean-03 | finance-0000 | finance | How does the operating expenses in Q1 2025 compare with Q3 2025? | Q1 2025 is lower than Q3 2025 | Q1 2025 is lower than Q3 2025. Relevant values: Q1 2025: 20, Q3 2025: 191. | evidence | comparison | greater_less_equal | grouped_bar | Operating expenses by quarter | Quarter | Operating expenses | million USD | easy | [] | comparison | [
"Q1 2025: 20",
"Q3 2025: 191"
] | [
"Q1 2025",
"Q3 2025"
] | [
"Q1 2025",
"Q2 2025",
"Q3 2025",
"Q4 2025"
] | [
20,
80,
191,
193
] | 0 | 262.5 | 2 | grouped bar chart: Operating expenses by quarter
Horizontal axis: Quarter
Vertical axis: Operating expenses (million USD)
Quarter | Operating expenses | Benchmark
----------------------------------------
Q1 2025 | 20 | 250
Q2 2025 | 80 | 192
Q3 2025 | 191 | 127
Q4 2025 | 193 | 45
How does the operating expenses in Q1... |
images/finance-0000-clean.png | dv-0000-clean-04 | finance-0000 | finance | Between Q1 2025 and Q4 2025, did the operating expenses rise, fall, or stay the same? | increased | Answer: increased
Evidence: Q1 2025: 20; Q4 2025: 193
Reasoning: Compare the last value with the first. | structured | trend | overall_direction | grouped_bar | Operating expenses by quarter | Quarter | Operating expenses | million USD | easy | [] | increasing | [
"Q1 2025: 20",
"Q4 2025: 193"
] | [
"Q1 2025",
"Q4 2025"
] | [
"Q1 2025",
"Q2 2025",
"Q3 2025",
"Q4 2025"
] | [
20,
80,
191,
193
] | 0 | 262.5 | 2 | grouped bar chart: Operating expenses by quarter
Horizontal axis: Quarter
Vertical axis: Operating expenses (million USD)
Quarter | Operating expenses | Benchmark
----------------------------------------
Q1 2025 | 20 | 250
Q2 2025 | 80 | 192
Q3 2025 | 191 | 127
Q4 2025 | 193 | 45
Between Q1 2025 and Q4 2025, did the ... |
images/agriculture-0001-clean.png | dv-0001-clean-00 | agriculture-0001 | agriculture | According to the chart, which district has the highest crop yield? | Central | Answer: Central
Evidence: Central: 9; Northern: 9; Coastal: 8; Western: 8; Eastern: 8
Reasoning: Compare every category and take the largest. | structured | ranking | highest | grouped_bar | Crop yield by district | District | Crop yield | tonnes per hectare | easy | [] | argmax | [
"Central: 9",
"Northern: 9",
"Coastal: 8",
"Western: 8",
"Eastern: 8"
] | [
"Central",
"Northern",
"Coastal",
"Western",
"Eastern"
] | [
"Central",
"Northern",
"Coastal",
"Western",
"Eastern"
] | [
9,
9,
8,
8,
8
] | 0 | 13.65 | null | grouped bar chart: Crop yield by district
Horizontal axis: District
Vertical axis: Crop yield (tonnes per hectare)
District | Crop yield | Forecast
--------------------------------
Central | 9 | 8
Northern | 9 | 13
Coastal | 8 | 11
Western | 8 | 5
Eastern | 8 | 3
According to the chart, which district has the highest... |
images/agriculture-0001-clean.png | dv-0001-clean-01 | agriculture-0001 | agriculture | Which district had the lowest crop yield? | Coastal | Coastal had the lowest value among the categories shown. | moderate | ranking | lowest | grouped_bar | Crop yield by district | District | Crop yield | tonnes per hectare | easy | [] | argmin | [
"Central: 9",
"Northern: 9",
"Coastal: 8",
"Western: 8",
"Eastern: 8"
] | [
"Central",
"Northern",
"Coastal",
"Western",
"Eastern"
] | [
"Central",
"Northern",
"Coastal",
"Western",
"Eastern"
] | [
9,
9,
8,
8,
8
] | 0 | 13.65 | null | grouped bar chart: Crop yield by district
Horizontal axis: District
Vertical axis: Crop yield (tonnes per hectare)
District | Crop yield | Forecast
--------------------------------
Central | 9 | 8
Northern | 9 | 13
Coastal | 8 | 11
Western | 8 | 5
Eastern | 8 | 3
Which district had the lowest crop yield? |
images/agriculture-0001-clean.png | dv-0001-clean-02 | agriculture-0001 | agriculture | By how much does the crop yield in Northern exceed Western? | 1 | 1. The relevant evidence is Northern: 9, Western: 8. Read both values and subtract the second from the first. | detailed | quantitative_reasoning | difference | grouped_bar | Crop yield by district | District | Crop yield | tonnes per hectare | easy | [] | subtraction | [
"Northern: 9",
"Western: 8"
] | [
"Northern",
"Western"
] | [
"Central",
"Northern",
"Coastal",
"Western",
"Eastern"
] | [
9,
9,
8,
8,
8
] | 0 | 13.65 | null | grouped bar chart: Crop yield by district
Horizontal axis: District
Vertical axis: Crop yield (tonnes per hectare)
District | Crop yield | Forecast
--------------------------------
Central | 9 | 8
Northern | 9 | 13
Coastal | 8 | 11
Western | 8 | 5
Eastern | 8 | 3
By how much does the crop yield in Northern exceed Wes... |
images/agriculture-0001-clean.png | dv-0001-clean-03 | agriculture-0001 | agriculture | How does the crop yield in Western compare with Northern? | Western is lower than Northern | Answer: Western is lower than Northern
Evidence: Western: 8; Northern: 9
Reasoning: Read both values and compare them directly. | structured | comparison | greater_less_equal | grouped_bar | Crop yield by district | District | Crop yield | tonnes per hectare | easy | [] | comparison | [
"Western: 8",
"Northern: 9"
] | [
"Western",
"Northern"
] | [
"Central",
"Northern",
"Coastal",
"Western",
"Eastern"
] | [
9,
9,
8,
8,
8
] | 0 | 13.65 | null | grouped bar chart: Crop yield by district
Horizontal axis: District
Vertical axis: Crop yield (tonnes per hectare)
District | Crop yield | Forecast
--------------------------------
Central | 9 | 8
Northern | 9 | 13
Coastal | 8 | 11
Western | 8 | 5
Eastern | 8 | 3
How does the crop yield in Western compare with Northe... |
images/marketing-0002-clean.png | dv-0002-clean-00 | marketing-0002 | marketing | According to the chart, which channel has the highest email open rate? | Email | Email had the highest value among the categories shown. | moderate | ranking | highest | line | Email open rate by channel | Channel | Email open rate | percent | easy | [] | argmax | [
"Email: 50",
"Organic: 37",
"Social: 36",
"Paid search: 45",
"Affiliate: 50",
"Display: 35"
] | [
"Email",
"Organic",
"Social",
"Paid search",
"Affiliate",
"Display"
] | [
"Email",
"Organic",
"Social",
"Paid search",
"Affiliate",
"Display"
] | [
50,
37,
36,
45,
50,
35
] | 34.25 | 50.75 | null | line chart: Email open rate by channel
Horizontal axis: Channel
Vertical axis: Email open rate (percent)
Channel | Email open rate
-------------------------
Email | 50
Organic | 37
Social | 36
Paid search | 45
Affiliate | 50
Display | 35
According to the chart, which channel has the highest email open rate? |
images/marketing-0002-clean.png | dv-0002-clean-01 | marketing-0002 | marketing | Which channel had the lowest email open rate? | Display | Display. Relevant values: Email: 50, Organic: 37, Social: 36, Paid search: 45, Affiliate: 50, Display: 35. | evidence | ranking | lowest | line | Email open rate by channel | Channel | Email open rate | percent | easy | [] | argmin | [
"Email: 50",
"Organic: 37",
"Social: 36",
"Paid search: 45",
"Affiliate: 50",
"Display: 35"
] | [
"Email",
"Organic",
"Social",
"Paid search",
"Affiliate",
"Display"
] | [
"Email",
"Organic",
"Social",
"Paid search",
"Affiliate",
"Display"
] | [
50,
37,
36,
45,
50,
35
] | 34.25 | 50.75 | null | line chart: Email open rate by channel
Horizontal axis: Channel
Vertical axis: Email open rate (percent)
Channel | Email open rate
-------------------------
Email | 50
Organic | 37
Social | 36
Paid search | 45
Affiliate | 50
Display | 35
Which channel had the lowest email open rate? |
images/marketing-0002-clean.png | dv-0002-clean-02 | marketing-0002 | marketing | What is the difference in email open rate between Social and Display? | 1 | Answer: 1
Evidence: Social: 36; Display: 35
Calculation: 36 - 35
Reasoning: Read both values and subtract the second from the first. | structured | quantitative_reasoning | difference | line | Email open rate by channel | Channel | Email open rate | percent | easy | [] | subtraction | [
"Social: 36",
"Display: 35"
] | [
"Social",
"Display"
] | [
"Email",
"Organic",
"Social",
"Paid search",
"Affiliate",
"Display"
] | [
50,
37,
36,
45,
50,
35
] | 34.25 | 50.75 | null | line chart: Email open rate by channel
Horizontal axis: Channel
Vertical axis: Email open rate (percent)
Channel | Email open rate
-------------------------
Email | 50
Organic | 37
Social | 36
Paid search | 45
Affiliate | 50
Display | 35
What is the difference in email open rate between Social and Display? |
images/marketing-0002-clean.png | dv-0002-clean-03 | marketing-0002 | marketing | Comparing Display and Social, which has the greater email open rate? | Display is lower than Social | Answer: Display is lower than Social
Evidence: Display: 35; Social: 36
Reasoning: Read both values and compare them directly. | structured | comparison | greater_less_equal | line | Email open rate by channel | Channel | Email open rate | percent | easy | [] | comparison | [
"Display: 35",
"Social: 36"
] | [
"Display",
"Social"
] | [
"Email",
"Organic",
"Social",
"Paid search",
"Affiliate",
"Display"
] | [
50,
37,
36,
45,
50,
35
] | 34.25 | 50.75 | null | line chart: Email open rate by channel
Horizontal axis: Channel
Vertical axis: Email open rate (percent)
Channel | Email open rate
-------------------------
Email | 50
Organic | 37
Social | 36
Paid search | 45
Affiliate | 50
Display | 35
Comparing Display and Social, which has the greater email open rate? |
images/operations-0003-clean.png | dv-0003-clean-00 | operations-0003 | operations | Which facility recorded the largest on-time delivery? | Plant 2 | Plant 2. The relevant evidence is Plant 1: 83, Plant 3: 69, Plant 2: 86, Plant 4: 77. Compare every category and take the largest. | detailed | ranking | highest | line | On-time delivery by facility | Facility | On-time delivery | percent | easy | [] | argmax | [
"Plant 1: 83",
"Plant 3: 69",
"Plant 2: 86",
"Plant 4: 77"
] | [
"Plant 1",
"Plant 3",
"Plant 2",
"Plant 4"
] | [
"Plant 1",
"Plant 3",
"Plant 2",
"Plant 4"
] | [
83,
69,
86,
77
] | 68.15 | 86.85 | null | line chart: On-time delivery by facility
Horizontal axis: Facility
Vertical axis: On-time delivery (percent)
Facility | On-time delivery
---------------------------
Plant 1 | 83
Plant 3 | 69
Plant 2 | 86
Plant 4 | 77
Which facility recorded the largest on-time delivery? |
images/operations-0003-clean.png | dv-0003-clean-01 | operations-0003 | operations | Which facility had the lowest on-time delivery? | Plant 3 | Answer: Plant 3
Evidence: Plant 1: 83; Plant 3: 69; Plant 2: 86; Plant 4: 77
Reasoning: Compare every category and take the smallest. | structured | ranking | lowest | line | On-time delivery by facility | Facility | On-time delivery | percent | easy | [] | argmin | [
"Plant 1: 83",
"Plant 3: 69",
"Plant 2: 86",
"Plant 4: 77"
] | [
"Plant 1",
"Plant 3",
"Plant 2",
"Plant 4"
] | [
"Plant 1",
"Plant 3",
"Plant 2",
"Plant 4"
] | [
83,
69,
86,
77
] | 68.15 | 86.85 | null | line chart: On-time delivery by facility
Horizontal axis: Facility
Vertical axis: On-time delivery (percent)
Facility | On-time delivery
---------------------------
Plant 1 | 83
Plant 3 | 69
Plant 2 | 86
Plant 4 | 77
Which facility had the lowest on-time delivery? |
images/operations-0003-clean.png | dv-0003-clean-02 | operations-0003 | operations | What is the difference in on-time delivery between Plant 2 and Plant 1? | 3 | Answer: 3
Evidence: Plant 2: 86; Plant 1: 83
Calculation: 86 - 83
Reasoning: Read both values and subtract the second from the first. | structured | quantitative_reasoning | difference | line | On-time delivery by facility | Facility | On-time delivery | percent | easy | [] | subtraction | [
"Plant 2: 86",
"Plant 1: 83"
] | [
"Plant 2",
"Plant 1"
] | [
"Plant 1",
"Plant 3",
"Plant 2",
"Plant 4"
] | [
83,
69,
86,
77
] | 68.15 | 86.85 | null | line chart: On-time delivery by facility
Horizontal axis: Facility
Vertical axis: On-time delivery (percent)
Facility | On-time delivery
---------------------------
Plant 1 | 83
Plant 3 | 69
Plant 2 | 86
Plant 4 | 77
What is the difference in on-time delivery between Plant 2 and Plant 1? |
images/operations-0003-clean.png | dv-0003-clean-03 | operations-0003 | operations | Is the on-time delivery in Plant 1 higher or lower than in Plant 2? | Plant 1 is lower than Plant 2 | Answer: Plant 1 is lower than Plant 2
Evidence: Plant 1: 83; Plant 2: 86
Reasoning: Read both values and compare them directly. | structured | comparison | greater_less_equal | line | On-time delivery by facility | Facility | On-time delivery | percent | easy | [] | comparison | [
"Plant 1: 83",
"Plant 2: 86"
] | [
"Plant 1",
"Plant 2"
] | [
"Plant 1",
"Plant 3",
"Plant 2",
"Plant 4"
] | [
83,
69,
86,
77
] | 68.15 | 86.85 | null | line chart: On-time delivery by facility
Horizontal axis: Facility
Vertical axis: On-time delivery (percent)
Facility | On-time delivery
---------------------------
Plant 1 | 83
Plant 3 | 69
Plant 2 | 86
Plant 4 | 77
Is the on-time delivery in Plant 1 higher or lower than in Plant 2? |
images/energy-0004-clean.png | dv-0004-clean-00 | energy-0004 | energy | According to the chart, which year has the highest generation? | 2022 | Answer: 2022
Evidence: 2022: 2477; 2023: 2467; 2024: 1504; 2025: 1168
Reasoning: Compare every category and take the largest. | structured | ranking | highest | line | Generation by year | Year | Generation | gigawatt hours | easy | [] | argmax | [
"2022: 2477",
"2023: 2467",
"2024: 1504",
"2025: 1168"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
2477,
2467,
1504,
1168
] | 1,102.55 | 2,542.45 | 10 | line chart: Generation by year
Horizontal axis: Year
Vertical axis: Generation (gigawatt hours)
Year | Generation
-----------------
2022 | 2477
2023 | 2467
2024 | 1504
2025 | 1168
According to the chart, which year has the highest generation? |
images/energy-0004-clean.png | dv-0004-clean-01 | energy-0004 | energy | Which year had the lowest generation? | 2025 | Answer: 2025
Evidence: 2022: 2477; 2023: 2467; 2024: 1504; 2025: 1168
Reasoning: Compare every category and take the smallest. | structured | ranking | lowest | line | Generation by year | Year | Generation | gigawatt hours | easy | [] | argmin | [
"2022: 2477",
"2023: 2467",
"2024: 1504",
"2025: 1168"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
2477,
2467,
1504,
1168
] | 1,102.55 | 2,542.45 | 10 | line chart: Generation by year
Horizontal axis: Year
Vertical axis: Generation (gigawatt hours)
Year | Generation
-----------------
2022 | 2477
2023 | 2467
2024 | 1504
2025 | 1168
Which year had the lowest generation? |
images/energy-0004-clean.png | dv-0004-clean-02 | energy-0004 | energy | By how much does the generation in 2022 exceed 2024? | 973 | 973. Relevant values: 2022: 2477, 2024: 1504. | evidence | quantitative_reasoning | difference | line | Generation by year | Year | Generation | gigawatt hours | easy | [] | subtraction | [
"2022: 2477",
"2024: 1504"
] | [
"2022",
"2024"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
2477,
2467,
1504,
1168
] | 1,102.55 | 2,542.45 | 10 | line chart: Generation by year
Horizontal axis: Year
Vertical axis: Generation (gigawatt hours)
Year | Generation
-----------------
2022 | 2477
2023 | 2467
2024 | 1504
2025 | 1168
By how much does the generation in 2022 exceed 2024? |
images/energy-0004-clean.png | dv-0004-clean-03 | energy-0004 | energy | Comparing 2024 and 2022, which has the greater generation? | 2024 is lower than 2022 | 2024 is lower than 2022. Relevant values: 2024: 1504, 2022: 2477. | evidence | comparison | greater_less_equal | line | Generation by year | Year | Generation | gigawatt hours | easy | [] | comparison | [
"2024: 1504",
"2022: 2477"
] | [
"2024",
"2022"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
2477,
2467,
1504,
1168
] | 1,102.55 | 2,542.45 | 10 | line chart: Generation by year
Horizontal axis: Year
Vertical axis: Generation (gigawatt hours)
Year | Generation
-----------------
2022 | 2477
2023 | 2467
2024 | 1504
2025 | 1168
Comparing 2024 and 2022, which has the greater generation? |
images/energy-0004-clean.png | dv-0004-clean-04 | energy-0004 | energy | Between 2022 and 2025, did the generation rise, fall, or stay the same? | decreased | decreased. Relevant values: 2022: 2477, 2025: 1168. | evidence | trend | overall_direction | line | Generation by year | Year | Generation | gigawatt hours | easy | [] | decreasing | [
"2022: 2477",
"2025: 1168"
] | [
"2022",
"2025"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
2477,
2467,
1504,
1168
] | 1,102.55 | 2,542.45 | 10 | line chart: Generation by year
Horizontal axis: Year
Vertical axis: Generation (gigawatt hours)
Year | Generation
-----------------
2022 | 2477
2023 | 2467
2024 | 1504
2025 | 1168
Between 2022 and 2025, did the generation rise, fall, or stay the same? |
images/health-0005-clean.png | dv-0005-clean-00 | health-0005 | health | According to the chart, which month has the highest referrals? | Nov | Nov had the highest value among the categories shown. | moderate | ranking | highest | line | Referrals by month | Month | Referrals | referrals | easy | [] | argmax | [
"Jul: 54",
"Aug: 185",
"Sep: 251",
"Oct: 380",
"Nov: 712"
] | [
"Jul",
"Aug",
"Sep",
"Oct",
"Nov"
] | [
"Jul",
"Aug",
"Sep",
"Oct",
"Nov"
] | [
54,
185,
251,
380,
712
] | 21.1 | 744.9 | null | line chart: Referrals by month
Horizontal axis: Month
Vertical axis: Referrals (referrals)
Month | Referrals
-----------------
Jul | 54
Aug | 185
Sep | 251
Oct | 380
Nov | 712
According to the chart, which month has the highest referrals? |
images/health-0005-clean.png | dv-0005-clean-01 | health-0005 | health | According to the chart, which month has the lowest referrals? | Jul | Answer: Jul
Evidence: Jul: 54; Aug: 185; Sep: 251; Oct: 380; Nov: 712
Reasoning: Compare every category and take the smallest. | structured | ranking | lowest | line | Referrals by month | Month | Referrals | referrals | easy | [] | argmin | [
"Jul: 54",
"Aug: 185",
"Sep: 251",
"Oct: 380",
"Nov: 712"
] | [
"Jul",
"Aug",
"Sep",
"Oct",
"Nov"
] | [
"Jul",
"Aug",
"Sep",
"Oct",
"Nov"
] | [
54,
185,
251,
380,
712
] | 21.1 | 744.9 | null | line chart: Referrals by month
Horizontal axis: Month
Vertical axis: Referrals (referrals)
Month | Referrals
-----------------
Jul | 54
Aug | 185
Sep | 251
Oct | 380
Nov | 712
According to the chart, which month has the lowest referrals? |
images/health-0005-clean.png | dv-0005-clean-02 | health-0005 | health | What is the difference in referrals between Sep and Aug? | 66 | 66. Relevant values: Sep: 251, Aug: 185. | evidence | quantitative_reasoning | difference | line | Referrals by month | Month | Referrals | referrals | easy | [] | subtraction | [
"Sep: 251",
"Aug: 185"
] | [
"Sep",
"Aug"
] | [
"Jul",
"Aug",
"Sep",
"Oct",
"Nov"
] | [
54,
185,
251,
380,
712
] | 21.1 | 744.9 | null | line chart: Referrals by month
Horizontal axis: Month
Vertical axis: Referrals (referrals)
Month | Referrals
-----------------
Jul | 54
Aug | 185
Sep | 251
Oct | 380
Nov | 712
What is the difference in referrals between Sep and Aug? |
images/health-0005-clean.png | dv-0005-clean-03 | health-0005 | health | Is the referrals in Sep higher or lower than in Aug? | Sep is higher than Aug | Answer: Sep is higher than Aug
Evidence: Sep: 251; Aug: 185
Reasoning: Read both values and compare them directly. | structured | comparison | greater_less_equal | line | Referrals by month | Month | Referrals | referrals | easy | [] | comparison | [
"Sep: 251",
"Aug: 185"
] | [
"Sep",
"Aug"
] | [
"Jul",
"Aug",
"Sep",
"Oct",
"Nov"
] | [
54,
185,
251,
380,
712
] | 21.1 | 744.9 | null | line chart: Referrals by month
Horizontal axis: Month
Vertical axis: Referrals (referrals)
Month | Referrals
-----------------
Jul | 54
Aug | 185
Sep | 251
Oct | 380
Nov | 712
Is the referrals in Sep higher or lower than in Aug? |
images/health-0005-clean.png | dv-0005-clean-04 | health-0005 | health | Between Jul and Nov, did the referrals rise, fall, or stay the same? | increased | Answer: increased
Evidence: Jul: 54; Nov: 712
Reasoning: Compare the last value with the first. | structured | trend | overall_direction | line | Referrals by month | Month | Referrals | referrals | easy | [] | increasing | [
"Jul: 54",
"Nov: 712"
] | [
"Jul",
"Nov"
] | [
"Jul",
"Aug",
"Sep",
"Oct",
"Nov"
] | [
54,
185,
251,
380,
712
] | 21.1 | 744.9 | null | line chart: Referrals by month
Horizontal axis: Month
Vertical axis: Referrals (referrals)
Month | Referrals
-----------------
Jul | 54
Aug | 185
Sep | 251
Oct | 380
Nov | 712
Between Jul and Nov, did the referrals rise, fall, or stay the same? |
images/education-0006-clean.png | dv-0006-clean-00 | education-0006 | education | Which school had the highest completion rate? | School D | School D had the highest value among the categories shown. | moderate | ranking | highest | line | Completion rate by school | School | Completion rate | percent | easy | [] | argmax | [
"School A: 44",
"School E: 94",
"School C: 35",
"School D: 95"
] | [
"School A",
"School E",
"School C",
"School D"
] | [
"School A",
"School E",
"School C",
"School D"
] | [
44,
94,
35,
95
] | 32 | 98 | 1 | line chart: Completion rate by school
Horizontal axis: School
Vertical axis: Completion rate (percent)
School | Completion rate
------------------------
School A | 44
School E | 94
School C | 35
School D | 95
Which school had the highest completion rate? |
images/education-0006-clean.png | dv-0006-clean-01 | education-0006 | education | Which school had the lowest completion rate? | School C | Answer: School C
Evidence: School A: 44; School E: 94; School C: 35; School D: 95
Reasoning: Compare every category and take the smallest. | structured | ranking | lowest | line | Completion rate by school | School | Completion rate | percent | easy | [] | argmin | [
"School A: 44",
"School E: 94",
"School C: 35",
"School D: 95"
] | [
"School A",
"School E",
"School C",
"School D"
] | [
"School A",
"School E",
"School C",
"School D"
] | [
44,
94,
35,
95
] | 32 | 98 | 1 | line chart: Completion rate by school
Horizontal axis: School
Vertical axis: Completion rate (percent)
School | Completion rate
------------------------
School A | 44
School E | 94
School C | 35
School D | 95
Which school had the lowest completion rate? |
images/education-0006-clean.png | dv-0006-clean-02 | education-0006 | education | How much higher was the completion rate in School E than in School A? | 50 | Answer: 50
Evidence: School E: 94; School A: 44
Calculation: 94 - 44
Reasoning: Read both values and subtract the second from the first. | structured | quantitative_reasoning | difference | line | Completion rate by school | School | Completion rate | percent | easy | [] | subtraction | [
"School E: 94",
"School A: 44"
] | [
"School E",
"School A"
] | [
"School A",
"School E",
"School C",
"School D"
] | [
44,
94,
35,
95
] | 32 | 98 | 1 | line chart: Completion rate by school
Horizontal axis: School
Vertical axis: Completion rate (percent)
School | Completion rate
------------------------
School A | 44
School E | 94
School C | 35
School D | 95
How much higher was the completion rate in School E than in School A? |
images/education-0006-clean.png | dv-0006-clean-03 | education-0006 | education | Comparing School E and School A, which has the greater completion rate? | School E is higher than School A | Answer: School E is higher than School A
Evidence: School E: 94; School A: 44
Reasoning: Read both values and compare them directly. | structured | comparison | greater_less_equal | line | Completion rate by school | School | Completion rate | percent | easy | [] | comparison | [
"School E: 94",
"School A: 44"
] | [
"School E",
"School A"
] | [
"School A",
"School E",
"School C",
"School D"
] | [
44,
94,
35,
95
] | 32 | 98 | 1 | line chart: Completion rate by school
Horizontal axis: School
Vertical axis: Completion rate (percent)
School | Completion rate
------------------------
School A | 44
School E | 94
School C | 35
School D | 95
Comparing School E and School A, which has the greater completion rate? |
images/transport-0007-clean.png | dv-0007-clean-00 | transport-0007 | transport | According to the chart, which route has the highest fuel consumption? | Route 58 | Answer: Route 58
Evidence: Route 1: 25.4; Route 58: 26.2; Route 27: 25.5; Route 40: 24.7; Route 12: 23.3
Reasoning: Compare every category and take the largest. | structured | ranking | highest | grouped_bar | Fuel consumption by route | Route | Fuel consumption | litres per 100 km | easy | [] | argmax | [
"Route 1: 25.4",
"Route 58: 26.2",
"Route 27: 25.5",
"Route 40: 24.7",
"Route 12: 23.3"
] | [
"Route 1",
"Route 58",
"Route 27",
"Route 40",
"Route 12"
] | [
"Route 1",
"Route 58",
"Route 27",
"Route 40",
"Route 12"
] | [
25.4,
26.2,
25.5,
24.7,
23.3
] | 0 | 32.025 | null | grouped bar chart: Fuel consumption by route
Horizontal axis: Route
Vertical axis: Fuel consumption (litres per 100 km)
Route | Fuel consumption | Target
---------------------------------
Route 1 | 25.4 | 29.1
Route 58 | 26.2 | 30.5
Route 27 | 25.5 | 28.4
Route 40 | 24.7 | 28
Route 12 | 23.3 | 27.8
According to the c... |
images/transport-0007-clean.png | dv-0007-clean-01 | transport-0007 | transport | According to the chart, which route has the lowest fuel consumption? | Route 12 | Answer: Route 12
Evidence: Route 1: 25.4; Route 58: 26.2; Route 27: 25.5; Route 40: 24.7; Route 12: 23.3
Reasoning: Compare every category and take the smallest. | structured | ranking | lowest | grouped_bar | Fuel consumption by route | Route | Fuel consumption | litres per 100 km | easy | [] | argmin | [
"Route 1: 25.4",
"Route 58: 26.2",
"Route 27: 25.5",
"Route 40: 24.7",
"Route 12: 23.3"
] | [
"Route 1",
"Route 58",
"Route 27",
"Route 40",
"Route 12"
] | [
"Route 1",
"Route 58",
"Route 27",
"Route 40",
"Route 12"
] | [
25.4,
26.2,
25.5,
24.7,
23.3
] | 0 | 32.025 | null | grouped bar chart: Fuel consumption by route
Horizontal axis: Route
Vertical axis: Fuel consumption (litres per 100 km)
Route | Fuel consumption | Target
---------------------------------
Route 1 | 25.4 | 29.1
Route 58 | 26.2 | 30.5
Route 27 | 25.5 | 28.4
Route 40 | 24.7 | 28
Route 12 | 23.3 | 27.8
According to the c... |
images/transport-0007-clean.png | dv-0007-clean-02 | transport-0007 | transport | By how much does the fuel consumption in Route 27 exceed Route 1? | 0.1 | Answer: 0.1
Evidence: Route 27: 25.5; Route 1: 25.4
Calculation: 25.5 - 25.4
Reasoning: Read both values and subtract the second from the first. | structured | quantitative_reasoning | difference | grouped_bar | Fuel consumption by route | Route | Fuel consumption | litres per 100 km | easy | [] | subtraction | [
"Route 27: 25.5",
"Route 1: 25.4"
] | [
"Route 27",
"Route 1"
] | [
"Route 1",
"Route 58",
"Route 27",
"Route 40",
"Route 12"
] | [
25.4,
26.2,
25.5,
24.7,
23.3
] | 0 | 32.025 | null | grouped bar chart: Fuel consumption by route
Horizontal axis: Route
Vertical axis: Fuel consumption (litres per 100 km)
Route | Fuel consumption | Target
---------------------------------
Route 1 | 25.4 | 29.1
Route 58 | 26.2 | 30.5
Route 27 | 25.5 | 28.4
Route 40 | 24.7 | 28
Route 12 | 23.3 | 27.8
By how much does t... |
images/transport-0007-clean.png | dv-0007-clean-03 | transport-0007 | transport | How does the fuel consumption in Route 27 compare with Route 1? | Route 27 is higher than Route 1 | Route 27 is higher than Route 1. Relevant values: Route 27: 25.5, Route 1: 25.4. | evidence | comparison | greater_less_equal | grouped_bar | Fuel consumption by route | Route | Fuel consumption | litres per 100 km | easy | [] | comparison | [
"Route 27: 25.5",
"Route 1: 25.4"
] | [
"Route 27",
"Route 1"
] | [
"Route 1",
"Route 58",
"Route 27",
"Route 40",
"Route 12"
] | [
25.4,
26.2,
25.5,
24.7,
23.3
] | 0 | 32.025 | null | grouped bar chart: Fuel consumption by route
Horizontal axis: Route
Vertical axis: Fuel consumption (litres per 100 km)
Route | Fuel consumption | Target
---------------------------------
Route 1 | 25.4 | 29.1
Route 58 | 26.2 | 30.5
Route 27 | 25.5 | 28.4
Route 40 | 24.7 | 28
Route 12 | 23.3 | 27.8
How does the fuel ... |
images/retail-0008-clean.png | dv-0008-clean-00 | retail-0008 | retail | Which category recorded the largest return rate? | Grocery | Answer: Grocery
Evidence: Electronics: 21; Home: 2; Grocery: 24; Beauty: 5
Reasoning: Compare every category and take the largest. | structured | ranking | highest | bar | Return rate by category | Category | Return rate | percent | easy | [] | argmax | [
"Electronics: 21",
"Home: 2",
"Grocery: 24",
"Beauty: 5"
] | [
"Electronics",
"Home",
"Grocery",
"Beauty"
] | [
"Electronics",
"Home",
"Grocery",
"Beauty"
] | [
21,
2,
24,
5
] | 0 | 25.2 | null | bar chart: Return rate by category
Horizontal axis: Category
Vertical axis: Return rate (percent)
Category | Return rate
----------------------
Electronics | 21
Home | 2
Grocery | 24
Beauty | 5
Which category recorded the largest return rate? |
images/retail-0008-clean.png | dv-0008-clean-01 | retail-0008 | retail | Which category had the lowest return rate? | Home | Home. Relevant values: Electronics: 21, Home: 2, Grocery: 24, Beauty: 5. | evidence | ranking | lowest | bar | Return rate by category | Category | Return rate | percent | easy | [] | argmin | [
"Electronics: 21",
"Home: 2",
"Grocery: 24",
"Beauty: 5"
] | [
"Electronics",
"Home",
"Grocery",
"Beauty"
] | [
"Electronics",
"Home",
"Grocery",
"Beauty"
] | [
21,
2,
24,
5
] | 0 | 25.2 | null | bar chart: Return rate by category
Horizontal axis: Category
Vertical axis: Return rate (percent)
Category | Return rate
----------------------
Electronics | 21
Home | 2
Grocery | 24
Beauty | 5
Which category had the lowest return rate? |
images/retail-0008-clean.png | dv-0008-clean-02 | retail-0008 | retail | How much higher was the return rate in Grocery than in Home? | 22 | Answer: 22
Evidence: Grocery: 24; Home: 2
Calculation: 24 - 2
Reasoning: Read both values and subtract the second from the first. | structured | quantitative_reasoning | difference | bar | Return rate by category | Category | Return rate | percent | easy | [] | subtraction | [
"Grocery: 24",
"Home: 2"
] | [
"Grocery",
"Home"
] | [
"Electronics",
"Home",
"Grocery",
"Beauty"
] | [
21,
2,
24,
5
] | 0 | 25.2 | null | bar chart: Return rate by category
Horizontal axis: Category
Vertical axis: Return rate (percent)
Category | Return rate
----------------------
Electronics | 21
Home | 2
Grocery | 24
Beauty | 5
How much higher was the return rate in Grocery than in Home? |
images/retail-0008-clean.png | dv-0008-clean-03 | retail-0008 | retail | Is the return rate in Grocery higher or lower than in Home? | Grocery is higher than Home | Answer: Grocery is higher than Home
Evidence: Grocery: 24; Home: 2
Reasoning: Read both values and compare them directly. | structured | comparison | greater_less_equal | bar | Return rate by category | Category | Return rate | percent | easy | [] | comparison | [
"Grocery: 24",
"Home: 2"
] | [
"Grocery",
"Home"
] | [
"Electronics",
"Home",
"Grocery",
"Beauty"
] | [
21,
2,
24,
5
] | 0 | 25.2 | null | bar chart: Return rate by category
Horizontal axis: Category
Vertical axis: Return rate (percent)
Category | Return rate
----------------------
Electronics | 21
Home | 2
Grocery | 24
Beauty | 5
Is the return rate in Grocery higher or lower than in Home? |
images/climate-0009-clean.png | dv-0009-clean-00 | climate-0009 | climate | Which month had the highest mean temperature? | June | June. The relevant evidence is January: 17.4, February: 13.1, March: 15.5, April: 15.1, May: 16.9, June: 17.6. Compare every category and take the largest. | detailed | ranking | highest | line | Mean temperature by month | Month | Mean temperature | degrees Celsius | easy | [] | argmax | [
"January: 17.4",
"February: 13.1",
"March: 15.5",
"April: 15.1",
"May: 16.9",
"June: 17.6"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
17.4,
13.1,
15.5,
15.1,
16.9,
17.6
] | 12.875 | 17.825 | null | line chart: Mean temperature by month
Horizontal axis: Month
Vertical axis: Mean temperature (degrees Celsius)
Month | Mean temperature
------------------------
January | 17.4
February | 13.1
March | 15.5
April | 15.1
May | 16.9
June | 17.6
Which month had the highest mean temperature? |
images/climate-0009-clean.png | dv-0009-clean-01 | climate-0009 | climate | According to the chart, which month has the lowest mean temperature? | February | February. The relevant evidence is January: 17.4, February: 13.1, March: 15.5, April: 15.1, May: 16.9, June: 17.6. Compare every category and take the smallest. | detailed | ranking | lowest | line | Mean temperature by month | Month | Mean temperature | degrees Celsius | easy | [] | argmin | [
"January: 17.4",
"February: 13.1",
"March: 15.5",
"April: 15.1",
"May: 16.9",
"June: 17.6"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
17.4,
13.1,
15.5,
15.1,
16.9,
17.6
] | 12.875 | 17.825 | null | line chart: Mean temperature by month
Horizontal axis: Month
Vertical axis: Mean temperature (degrees Celsius)
Month | Mean temperature
------------------------
January | 17.4
February | 13.1
March | 15.5
April | 15.1
May | 16.9
June | 17.6
According to the chart, which month has the lowest mean temperature? |
images/climate-0009-clean.png | dv-0009-clean-02 | climate-0009 | climate | By how much does the mean temperature in May exceed February? | 3.8 | 3.8. 16.9 - 13.1 gives the difference. | moderate | quantitative_reasoning | difference | line | Mean temperature by month | Month | Mean temperature | degrees Celsius | easy | [] | subtraction | [
"May: 16.9",
"February: 13.1"
] | [
"May",
"February"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
17.4,
13.1,
15.5,
15.1,
16.9,
17.6
] | 12.875 | 17.825 | null | line chart: Mean temperature by month
Horizontal axis: Month
Vertical axis: Mean temperature (degrees Celsius)
Month | Mean temperature
------------------------
January | 17.4
February | 13.1
March | 15.5
April | 15.1
May | 16.9
June | 17.6
By how much does the mean temperature in May exceed February? |
images/climate-0009-clean.png | dv-0009-clean-03 | climate-0009 | climate | Comparing May and February, which has the greater mean temperature? | May is higher than February | May is higher than February. The relevant evidence is May: 16.9, February: 13.1. Read both values and compare them directly. | detailed | comparison | greater_less_equal | line | Mean temperature by month | Month | Mean temperature | degrees Celsius | easy | [] | comparison | [
"May: 16.9",
"February: 13.1"
] | [
"May",
"February"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
17.4,
13.1,
15.5,
15.1,
16.9,
17.6
] | 12.875 | 17.825 | null | line chart: Mean temperature by month
Horizontal axis: Month
Vertical axis: Mean temperature (degrees Celsius)
Month | Mean temperature
------------------------
January | 17.4
February | 13.1
March | 15.5
April | 15.1
May | 16.9
June | 17.6
Comparing May and February, which has the greater mean temperature? |
images/climate-0009-clean.png | dv-0009-clean-04 | climate-0009 | climate | What is the overall direction of mean temperature from January to June? | increased | Answer: increased
Evidence: January: 17.4; June: 17.6
Reasoning: Compare the last value with the first. | structured | trend | overall_direction | line | Mean temperature by month | Month | Mean temperature | degrees Celsius | easy | [] | increasing | [
"January: 17.4",
"June: 17.6"
] | [
"January",
"June"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
17.4,
13.1,
15.5,
15.1,
16.9,
17.6
] | 12.875 | 17.825 | null | line chart: Mean temperature by month
Horizontal axis: Month
Vertical axis: Mean temperature (degrees Celsius)
Month | Mean temperature
------------------------
January | 17.4
February | 13.1
March | 15.5
April | 15.1
May | 16.9
June | 17.6
What is the overall direction of mean temperature from January to June? |
images/science_temp_rate-0010-clean.png | dv-0010-clean-00 | science_temp_rate-0010 | science | Does the chart show a positive, negative, or weak linear relationship between temperature and reaction rate? | positive | positive. The relevant evidence is correlation coefficient: 0.995. Assess whether the points trend upward, downward, or neither. | detailed | statistical_interpretation | correlation | scatter | Reaction rate against temperature | Temperature | Reaction rate | units per minute | easy | [] | pearson_correlation | [
"correlation coefficient: 0.995"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14"
] | [
10,
10,
16,
44,
39,
56,
84,
101,
105,
109,
119,
133,
159,
152
] | 2.55 | 166.45 | null | scatter plot: Reaction rate against temperature
Horizontal axis: Temperature
Vertical axis: Reaction rate (units per minute)
Temperature | Reaction rate
---------------------------
9 | 10
11 | 10
15 | 16
23 | 44
29 | 39
29 | 56
50 | 84
56 | 101
59 | 105
61 | 109
67 | 119
76 | 133
89 | 159
91 | 152
Does the chart show... |
images/science_dose_response-0011-clean.png | dv-0011-clean-00 | science_dose_response-0011 | science | What type of linear relationship is visible between dose and response? | positive | positive. Relevant values: correlation coefficient: 0.994. | evidence | statistical_interpretation | correlation | scatter | Response against dose | Dose | Response | percent | easy | [] | pearson_correlation | [
"correlation coefficient: 0.994"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10"
] | [
3,
9,
18,
29,
44,
53,
70,
70,
79,
81
] | -0.9 | 84.9 | null | scatter plot: Response against dose
Horizontal axis: Dose
Vertical axis: Response (percent)
Dose | Response
---------------
4 | 3
12 | 9
18 | 18
25 | 29
48 | 44
48 | 53
61 | 70
65 | 70
75 | 79
77 | 81
What type of linear relationship is visible between dose and response? |
images/econ_price_demand-0012-clean.png | dv-0012-clean-00 | econ_price_demand-0012 | economics | What type of linear relationship is visible between price and units demanded? | negative | Answer: negative
Evidence: correlation coefficient: -0.992
Calculation: r = -0.992
Reasoning: Assess whether the points trend upward, downward, or neither. | structured | statistical_interpretation | correlation | scatter | Units demanded against price | Price | Units demanded | units | easy | [] | pearson_correlation | [
"correlation coefficient: -0.992"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12"
] | [
900,
805,
729,
564,
564,
563,
470,
408,
266,
239,
70,
31
] | -12.45 | 943.45 | null | scatter plot: Units demanded against price
Horizontal axis: Price
Vertical axis: Units demanded (units)
Price | Units demanded
----------------------
3 | 900
11 | 805
24 | 729
30 | 564
30 | 564
33 | 563
43 | 470
46 | 408
59 | 266
62 | 239
82 | 70
90 | 31
What type of linear relationship is visible between price and u... |
images/transport_speed_delay-0013-clean.png | dv-0013-clean-00 | transport_speed_delay-0013 | transport | How are average speed and delay related in this chart? | negative | Answer: negative
Evidence: correlation coefficient: -0.990
Calculation: r = -0.990
Reasoning: Assess whether the points trend upward, downward, or neither. | structured | statistical_interpretation | correlation | scatter | Delay against average speed | Average speed | Delay | minutes | easy | [] | pearson_correlation | [
"correlation coefficient: -0.990"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20"
] | [
54,
55,
49,
47,
42,
36,
26,
20,
20,
19,
17,
13,
10,
16,
12,
4,
6,
7,
8,
5
] | 1.45 | 57.55 | null | scatter plot: Delay against average speed
Horizontal axis: Average speed
Vertical axis: Delay (minutes)
Average speed | Delay
---------------------
8 | 54
10 | 55
14 | 49
16 | 47
28 | 42
40 | 36
48 | 26
56 | 20
64 | 20
67 | 19
67 | 17
72 | 13
72 | 10
72 | 16
78 | 12
80 | 4
86 | 6
87 | 7
88 | 8
89 | 5
How are average ... |
images/ops_staffing_backlog-0014-clean.png | dv-0014-clean-00 | ops_staffing_backlog-0014 | operations | Does the chart show a positive, negative, or weak linear relationship between staff on shift and backlog? | negative | Answer: negative
Evidence: correlation coefficient: -0.984
Calculation: r = -0.984
Reasoning: Assess whether the points trend upward, downward, or neither. | structured | statistical_interpretation | correlation | scatter | Backlog against staff on shift | Staff on shift | Backlog | orders | easy | [] | pearson_correlation | [
"correlation coefficient: -0.984"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16"
] | [
382,
370,
401,
271,
263,
244,
274,
199,
250,
231,
165,
203,
181,
72,
45,
24
] | 5.15 | 419.85 | null | scatter plot: Backlog against staff on shift
Horizontal axis: Staff on shift
Vertical axis: Backlog (orders)
Staff on shift | Backlog
------------------------
5 | 382
8 | 370
8 | 401
18 | 271
21 | 263
22 | 244
23 | 274
24 | 199
24 | 250
24 | 231
28 | 165
29 | 203
31 | 181
39 | 72
43 | 45
48 | 24
Does the chart show a... |
images/health_age_recovery-0015-clean.png | dv-0015-clean-00 | health_age_recovery-0015 | health | Does the chart show a positive, negative, or weak linear relationship between age and recovery time? | positive | Answer: positive
Evidence: correlation coefficient: 0.976
Calculation: r = 0.976
Reasoning: Assess whether the points trend upward, downward, or neither. | structured | statistical_interpretation | correlation | scatter | Recovery time against age | Age | Recovery time | days | easy | [] | pearson_correlation | [
"correlation coefficient: 0.976"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16"
] | [
7,
25,
25,
22,
32,
24,
35,
41,
45,
44,
42,
47,
45,
44,
47,
59
] | 4.4 | 61.6 | null | scatter plot: Recovery time against age
Horizontal axis: Age
Vertical axis: Recovery time (days)
Age | Recovery time
-------------------
23 | 7
41 | 25
43 | 25
44 | 22
49 | 32
51 | 24
57 | 35
64 | 41
66 | 45
67 | 44
69 | 42
70 | 47
71 | 45
71 | 44
77 | 47
82 | 59
Does the chart show a positive, negative, or weak line... |
images/noise_control-0016-clean.png | dv-0016-clean-00 | noise_control-0016 | science | What type of linear relationship is visible between sample index and measured value? | weak or no linear | The relationship is weak or no linear. The calculated correlation is r = -0.216. | moderate | statistical_interpretation | correlation | scatter | Measured value against sample index | Sample index | Measured value | units | easy | [] | pearson_correlation | [
"correlation coefficient: -0.216"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12"
] | [
106,
90,
122,
143,
90,
133,
136,
196,
95,
179,
39,
22
] | 13.3 | 204.7 | null | scatter plot: Measured value against sample index
Horizontal axis: Sample index
Vertical axis: Measured value (units)
Sample index | Measured value
-----------------------------
4 | 106
18 | 90
23 | 122
29 | 143
34 | 90
44 | 133
53 | 136
64 | 196
66 | 95
75 | 179
85 | 39
92 | 22
What type of linear relationship is vi... |
images/science_measurements-0017-clean.png | dv-0017-clean-00 | science_measurements-0017 | science | Which bar in this histogram is the tallest? | 64.52 to 67.75 | Answer: 64.52 to 67.75
Evidence: 51.6 to 54.83: 10; 54.83 to 58.06: 9; 58.06 to 61.29: 15; 61.29 to 64.52: 12; 64.52 to 67.75: 21; 67.75 to 70.98: 13; 70.98 to 74.21: 9; 74.21 to 77.44: 4; 77.44 to 80.67: 4; 80.67 to 83.9: 3
Calculation: count = 21 | structured | statistical_interpretation | modal_bin | histogram | Distribution of measurement value (units) | Measurement value | Frequency | observations | easy | [] | modal_bin | [
"51.6 to 54.83: 10",
"54.83 to 58.06: 9",
"58.06 to 61.29: 15",
"61.29 to 64.52: 12",
"64.52 to 67.75: 21",
"67.75 to 70.98: 13",
"70.98 to 74.21: 9",
"74.21 to 77.44: 4",
"77.44 to 80.67: 4",
"80.67 to 83.9: 3"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
67.5,
72.6,
81.6,
83.2,
70.7,
66.2,
62.7,
63.2,
69.1,
65.5,
79.2,
61.8,
60.5,
71.7,
73.3,
64.3,
59.2,
68.2,
67.5,
52.9,
56.9,
57.8,
66.3,
58.6,
53.3,
79.6,
67.7,
52.3,
67.9,
60.8,
74.1,
59.1,
59.1,
60.4,
72.1,
62,
79.4,
56,
75.2,
65.1,
... | 0 | 22.05 | null | histogram: Distribution of measurement value (units)
Horizontal axis: Measurement value
Vertical axis: Frequency (observations)
Bin | Count
-----------
51.6 to 54.83 | 10
54.83 to 58.06 | 9
58.06 to 61.29 | 15
61.29 to 64.52 | 12
64.52 to 67.75 | 21
67.75 to 70.98 | 13
70.98 to 74.21 | 9
74.21 to 77.44 | 4
77.44 to 80... |
images/science_measurements-0017-clean.png | dv-0017-clean-01 | science_measurements-0017 | science | What shape does this distribution have? | roughly symmetric | Answer: roughly symmetric
Evidence: skewness index: -0.001
Calculation: (mean - median) / sd = -0.001 | structured | statistical_interpretation | skew | histogram | Distribution of measurement value (units) | Measurement value | Frequency | observations | easy | [] | distribution_shape | [
"skewness index: -0.001"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
67.5,
72.6,
81.6,
83.2,
70.7,
66.2,
62.7,
63.2,
69.1,
65.5,
79.2,
61.8,
60.5,
71.7,
73.3,
64.3,
59.2,
68.2,
67.5,
52.9,
56.9,
57.8,
66.3,
58.6,
53.3,
79.6,
67.7,
52.3,
67.9,
60.8,
74.1,
59.1,
59.1,
60.4,
72.1,
62,
79.4,
56,
75.2,
65.1,
... | 0 | 22.05 | null | histogram: Distribution of measurement value (units)
Horizontal axis: Measurement value
Vertical axis: Frequency (observations)
Bin | Count
-----------
51.6 to 54.83 | 10
54.83 to 58.06 | 9
58.06 to 61.29 | 15
61.29 to 64.52 | 12
64.52 to 67.75 | 21
67.75 to 70.98 | 13
70.98 to 74.21 | 9
74.21 to 77.44 | 4
77.44 to 80... |
images/science_measurements-0017-clean.png | dv-0017-clean-02 | science_measurements-0017 | science | Roughly where does the median measurement value fall? | 64.52 to 67.75 | Answer: 64.52 to 67.75
Evidence: median observation: 65.1
Calculation: median = 65.1 | structured | statistical_interpretation | central_tendency | histogram | Distribution of measurement value (units) | Measurement value | Frequency | observations | easy | [] | median_bin | [
"median observation: 65.1"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
67.5,
72.6,
81.6,
83.2,
70.7,
66.2,
62.7,
63.2,
69.1,
65.5,
79.2,
61.8,
60.5,
71.7,
73.3,
64.3,
59.2,
68.2,
67.5,
52.9,
56.9,
57.8,
66.3,
58.6,
53.3,
79.6,
67.7,
52.3,
67.9,
60.8,
74.1,
59.1,
59.1,
60.4,
72.1,
62,
79.4,
56,
75.2,
65.1,
... | 0 | 22.05 | null | histogram: Distribution of measurement value (units)
Horizontal axis: Measurement value
Vertical axis: Frequency (observations)
Bin | Count
-----------
51.6 to 54.83 | 10
54.83 to 58.06 | 9
58.06 to 61.29 | 15
61.29 to 64.52 | 12
64.52 to 67.75 | 21
67.75 to 70.98 | 13
70.98 to 74.21 | 9
74.21 to 77.44 | 4
77.44 to 80... |
images/health_wait_times-0018-clean.png | dv-0018-clean-00 | health_wait_times-0018 | health | Which range of wait time contains the most observations? | 54.64 to 66.7 | Answer: 54.64 to 66.7
Evidence: 6.4 to 18.46: 2; 18.46 to 30.52: 1; 30.52 to 42.58: 4; 42.58 to 54.64: 6; 54.64 to 66.7: 16; 66.7 to 78.76: 12; 78.76 to 90.82: 11; 90.82 to 102.88: 5; 102.88 to 114.94: 2; 114.94 to 127: 1
Calculation: count = 16 | structured | statistical_interpretation | modal_bin | histogram | Distribution of wait time (minutes) | Wait time | Frequency | observations | easy | [] | modal_bin | [
"6.4 to 18.46: 2",
"18.46 to 30.52: 1",
"30.52 to 42.58: 4",
"42.58 to 54.64: 6",
"54.64 to 66.7: 16",
"66.7 to 78.76: 12",
"78.76 to 90.82: 11",
"90.82 to 102.88: 5",
"102.88 to 114.94: 2",
"114.94 to 127: 1"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
6.4,
64.1,
49.4,
59.3,
55.8,
49.7,
35.3,
54.7,
90,
55.1,
73.2,
68,
44,
103.9,
70.5,
37.8,
59.1,
74.4,
40.7,
77,
85.6,
93.5,
88.1,
65.1,
58.8,
58.1,
71.4,
79.8,
67.5,
55.8,
55.7,
71.9,
97.1,
15,
101.7,
52.6,
83,
56.9,
88.9,
87,
72.2,
8... | 0 | 16.8 | null | histogram: Distribution of wait time (minutes)
Horizontal axis: Wait time
Vertical axis: Frequency (observations)
Bin | Count
-----------
6.4 to 18.46 | 2
18.46 to 30.52 | 1
30.52 to 42.58 | 4
42.58 to 54.64 | 6
54.64 to 66.7 | 16
66.7 to 78.76 | 12
78.76 to 90.82 | 11
90.82 to 102.88 | 5
102.88 to 114.94 | 2
114.94 t... |
images/health_wait_times-0018-clean.png | dv-0018-clean-01 | health_wait_times-0018 | health | What shape does this distribution have? | roughly symmetric | Answer: roughly symmetric
Evidence: skewness index: 0.018
Calculation: (mean - median) / sd = 0.018 | structured | statistical_interpretation | skew | histogram | Distribution of wait time (minutes) | Wait time | Frequency | observations | easy | [] | distribution_shape | [
"skewness index: 0.018"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
6.4,
64.1,
49.4,
59.3,
55.8,
49.7,
35.3,
54.7,
90,
55.1,
73.2,
68,
44,
103.9,
70.5,
37.8,
59.1,
74.4,
40.7,
77,
85.6,
93.5,
88.1,
65.1,
58.8,
58.1,
71.4,
79.8,
67.5,
55.8,
55.7,
71.9,
97.1,
15,
101.7,
52.6,
83,
56.9,
88.9,
87,
72.2,
8... | 0 | 16.8 | null | histogram: Distribution of wait time (minutes)
Horizontal axis: Wait time
Vertical axis: Frequency (observations)
Bin | Count
-----------
6.4 to 18.46 | 2
18.46 to 30.52 | 1
30.52 to 42.58 | 4
42.58 to 54.64 | 6
54.64 to 66.7 | 16
66.7 to 78.76 | 12
78.76 to 90.82 | 11
90.82 to 102.88 | 5
102.88 to 114.94 | 2
114.94 t... |
images/health_wait_times-0018-clean.png | dv-0018-clean-02 | health_wait_times-0018 | health | What is the difference between the largest and smallest wait time? | 120.6 | 120.6. The relevant evidence is smallest: 6.4, largest: 127. | detailed | statistical_interpretation | spread | histogram | Distribution of wait time (minutes) | Wait time | Frequency | observations | easy | [] | spread | [
"smallest: 6.4",
"largest: 127"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
6.4,
64.1,
49.4,
59.3,
55.8,
49.7,
35.3,
54.7,
90,
55.1,
73.2,
68,
44,
103.9,
70.5,
37.8,
59.1,
74.4,
40.7,
77,
85.6,
93.5,
88.1,
65.1,
58.8,
58.1,
71.4,
79.8,
67.5,
55.8,
55.7,
71.9,
97.1,
15,
101.7,
52.6,
83,
56.9,
88.9,
87,
72.2,
8... | 0 | 16.8 | null | histogram: Distribution of wait time (minutes)
Horizontal axis: Wait time
Vertical axis: Frequency (observations)
Bin | Count
-----------
6.4 to 18.46 | 2
18.46 to 30.52 | 1
30.52 to 42.58 | 4
42.58 to 54.64 | 6
54.64 to 66.7 | 16
66.7 to 78.76 | 12
78.76 to 90.82 | 11
90.82 to 102.88 | 5
102.88 to 114.94 | 2
114.94 t... |
images/retail_basket-0019-clean.png | dv-0019-clean-00 | retail_basket-0019 | retail | Which range of basket value contains the most observations? | 75.06 to 92.55 | 75.06 to 92.55. The relevant evidence is 5.1 to 22.59: 1, 22.59 to 40.08: 7, 40.08 to 57.57: 8, 57.57 to 75.06: 6, 75.06 to 92.55: 10, 92.55 to 110.04: 9, 110.04 to 127.53: 6, 127.53 to 145.02: 5, 145.02 to 162.51: 6, 162.51 to 180: 2. | detailed | statistical_interpretation | modal_bin | histogram | Distribution of basket value (USD) | Basket value | Frequency | observations | easy | [] | modal_bin | [
"5.1 to 22.59: 1",
"22.59 to 40.08: 7",
"40.08 to 57.57: 8",
"57.57 to 75.06: 6",
"75.06 to 92.55: 10",
"92.55 to 110.04: 9",
"110.04 to 127.53: 6",
"127.53 to 145.02: 5",
"145.02 to 162.51: 6",
"162.51 to 180: 2"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
102,
97.1,
53.1,
84.2,
150.5,
115.4,
180,
87.9,
107.7,
29.5,
95.9,
143.5,
26.7,
54.7,
58,
45.7,
150.8,
74.5,
69.5,
138.2,
34.5,
77,
74.3,
41.7,
117,
86.4,
112,
37.8,
50,
79.2,
86,
73.6,
91.5,
26.1,
5.1,
33.6,
130.3,
53.9,
147.5,
99.2,
1... | 0 | 10.5 | null | histogram: Distribution of basket value (USD)
Horizontal axis: Basket value
Vertical axis: Frequency (observations)
Bin | Count
-----------
5.1 to 22.59 | 1
22.59 to 40.08 | 7
40.08 to 57.57 | 8
57.57 to 75.06 | 6
75.06 to 92.55 | 10
92.55 to 110.04 | 9
110.04 to 127.53 | 6
127.53 to 145.02 | 5
145.02 to 162.51 | 6
16... |
images/retail_basket-0019-clean.png | dv-0019-clean-01 | retail_basket-0019 | retail | Is the distribution of basket value roughly symmetric, left-skewed, or right-skewed? | roughly symmetric | Answer: roughly symmetric
Evidence: skewness index: 0.054
Calculation: (mean - median) / sd = 0.054 | structured | statistical_interpretation | skew | histogram | Distribution of basket value (USD) | Basket value | Frequency | observations | easy | [] | distribution_shape | [
"skewness index: 0.054"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
102,
97.1,
53.1,
84.2,
150.5,
115.4,
180,
87.9,
107.7,
29.5,
95.9,
143.5,
26.7,
54.7,
58,
45.7,
150.8,
74.5,
69.5,
138.2,
34.5,
77,
74.3,
41.7,
117,
86.4,
112,
37.8,
50,
79.2,
86,
73.6,
91.5,
26.1,
5.1,
33.6,
130.3,
53.9,
147.5,
99.2,
1... | 0 | 10.5 | null | histogram: Distribution of basket value (USD)
Horizontal axis: Basket value
Vertical axis: Frequency (observations)
Bin | Count
-----------
5.1 to 22.59 | 1
22.59 to 40.08 | 7
40.08 to 57.57 | 8
57.57 to 75.06 | 6
75.06 to 92.55 | 10
92.55 to 110.04 | 9
110.04 to 127.53 | 6
127.53 to 145.02 | 5
145.02 to 162.51 | 6
16... |
images/retail_basket-0019-clean.png | dv-0019-clean-02 | retail_basket-0019 | retail | Which bin contains the median value? | 75.06 to 92.55 | Answer: 75.06 to 92.55
Evidence: median observation: 88.6
Calculation: median = 88.6 | structured | statistical_interpretation | central_tendency | histogram | Distribution of basket value (USD) | Basket value | Frequency | observations | easy | [] | median_bin | [
"median observation: 88.6"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
102,
97.1,
53.1,
84.2,
150.5,
115.4,
180,
87.9,
107.7,
29.5,
95.9,
143.5,
26.7,
54.7,
58,
45.7,
150.8,
74.5,
69.5,
138.2,
34.5,
77,
74.3,
41.7,
117,
86.4,
112,
37.8,
50,
79.2,
86,
73.6,
91.5,
26.1,
5.1,
33.6,
130.3,
53.9,
147.5,
99.2,
1... | 0 | 10.5 | null | histogram: Distribution of basket value (USD)
Horizontal axis: Basket value
Vertical axis: Frequency (observations)
Bin | Count
-----------
5.1 to 22.59 | 1
22.59 to 40.08 | 7
40.08 to 57.57 | 8
57.57 to 75.06 | 6
75.06 to 92.55 | 10
92.55 to 110.04 | 9
110.04 to 127.53 | 6
127.53 to 145.02 | 5
145.02 to 162.51 | 6
16... |
images/education_scores-0020-clean.png | dv-0020-clean-00 | education_scores-0020 | education | Which bar in this histogram is the tallest? | 45.4 to 50.48 | Answer: 45.4 to 50.48
Evidence: 20 to 25.08: 20; 25.08 to 30.16: 12; 30.16 to 35.24: 15; 35.24 to 40.32: 15; 40.32 to 45.4: 13; 45.4 to 50.48: 23; 50.48 to 55.56: 9; 55.56 to 60.64: 8; 60.64 to 65.72: 3; 65.72 to 70.8: 2
Calculation: count = 23 | structured | statistical_interpretation | modal_bin | histogram | Distribution of assessment score (points) | Assessment score | Frequency | observations | easy | [] | modal_bin | [
"20 to 25.08: 20",
"25.08 to 30.16: 12",
"30.16 to 35.24: 15",
"35.24 to 40.32: 15",
"40.32 to 45.4: 13",
"45.4 to 50.48: 23",
"50.48 to 55.56: 9",
"55.56 to 60.64: 8",
"60.64 to 65.72: 3",
"65.72 to 70.8: 2"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
22.8,
56.2,
30.8,
31.5,
47.8,
47.2,
45.7,
20,
28.6,
47.4,
52.1,
31.5,
38.8,
43.1,
27.8,
32.5,
30.7,
64.7,
25.1,
30.3,
58.3,
47.8,
67.2,
31.7,
33,
47.4,
56.8,
48.5,
26.9,
20,
64.8,
44.1,
20,
39.6,
49.5,
28.3,
51.5,
34.9,
53.2,
32.1,
40.3... | 0 | 24.15 | null | histogram: Distribution of assessment score (points)
Horizontal axis: Assessment score
Vertical axis: Frequency (observations)
Bin | Count
-----------
20 to 25.08 | 20
25.08 to 30.16 | 12
30.16 to 35.24 | 15
35.24 to 40.32 | 15
40.32 to 45.4 | 13
45.4 to 50.48 | 23
50.48 to 55.56 | 9
55.56 to 60.64 | 8
60.64 to 65.72 ... |
images/education_scores-0020-clean.png | dv-0020-clean-01 | education_scores-0020 | education | Is the distribution of assessment score roughly symmetric, left-skewed, or right-skewed? | roughly symmetric | Answer: roughly symmetric
Evidence: skewness index: -0.003
Calculation: (mean - median) / sd = -0.003 | structured | statistical_interpretation | skew | histogram | Distribution of assessment score (points) | Assessment score | Frequency | observations | easy | [] | distribution_shape | [
"skewness index: -0.003"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
22.8,
56.2,
30.8,
31.5,
47.8,
47.2,
45.7,
20,
28.6,
47.4,
52.1,
31.5,
38.8,
43.1,
27.8,
32.5,
30.7,
64.7,
25.1,
30.3,
58.3,
47.8,
67.2,
31.7,
33,
47.4,
56.8,
48.5,
26.9,
20,
64.8,
44.1,
20,
39.6,
49.5,
28.3,
51.5,
34.9,
53.2,
32.1,
40.3... | 0 | 24.15 | null | histogram: Distribution of assessment score (points)
Horizontal axis: Assessment score
Vertical axis: Frequency (observations)
Bin | Count
-----------
20 to 25.08 | 20
25.08 to 30.16 | 12
30.16 to 35.24 | 15
35.24 to 40.32 | 15
40.32 to 45.4 | 13
45.4 to 50.48 | 23
50.48 to 55.56 | 9
55.56 to 60.64 | 8
60.64 to 65.72 ... |
images/education_scores-0020-clean.png | dv-0020-clean-02 | education_scores-0020 | education | Which bin contains the median value? | 35.24 to 40.32 | Answer: 35.24 to 40.32
Evidence: median observation: 39.6
Calculation: median = 39.6 | structured | statistical_interpretation | central_tendency | histogram | Distribution of assessment score (points) | Assessment score | Frequency | observations | easy | [] | median_bin | [
"median observation: 39.6"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
22.8,
56.2,
30.8,
31.5,
47.8,
47.2,
45.7,
20,
28.6,
47.4,
52.1,
31.5,
38.8,
43.1,
27.8,
32.5,
30.7,
64.7,
25.1,
30.3,
58.3,
47.8,
67.2,
31.7,
33,
47.4,
56.8,
48.5,
26.9,
20,
64.8,
44.1,
20,
39.6,
49.5,
28.3,
51.5,
34.9,
53.2,
32.1,
40.3... | 0 | 24.15 | null | histogram: Distribution of assessment score (points)
Horizontal axis: Assessment score
Vertical axis: Frequency (observations)
Bin | Count
-----------
20 to 25.08 | 20
25.08 to 30.16 | 12
30.16 to 35.24 | 15
35.24 to 40.32 | 15
40.32 to 45.4 | 13
45.4 to 50.48 | 23
50.48 to 55.56 | 9
55.56 to 60.64 | 8
60.64 to 65.72 ... |
images/transport_journey-0021-clean.png | dv-0021-clean-00 | transport_journey-0021 | transport | Where is the peak of this distribution? | 57.12 to 61.56 | 57.12 to 61.56. Relevant values: 43.8 to 48.24: 8, 48.24 to 52.68: 8, 52.68 to 57.12: 16, 57.12 to 61.56: 23, 61.56 to 66: 9, 66 to 70.44: 6, 70.44 to 74.88: 6, 74.88 to 79.32: 3, 83.76 to 88.2: 1. | evidence | statistical_interpretation | modal_bin | histogram | Distribution of journey time (minutes) | Journey time | Frequency | observations | easy | [] | modal_bin | [
"43.8 to 48.24: 8",
"48.24 to 52.68: 8",
"52.68 to 57.12: 16",
"57.12 to 61.56: 23",
"61.56 to 66: 9",
"66 to 70.44: 6",
"70.44 to 74.88: 6",
"74.88 to 79.32: 3",
"83.76 to 88.2: 1"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
58.9,
59.7,
70.2,
58.9,
59.3,
48.2,
68.9,
58.5,
52.9,
57.9,
68.4,
58.7,
70.5,
54.1,
58.5,
57.1,
46.5,
53.1,
59.1,
72.3,
64.5,
64.1,
54.8,
49.1,
88.2,
58.7,
58.2,
51.1,
71.6,
60.4,
55.5,
50.2,
54.9,
64.8,
54.5,
47.1,
63,
52.9,
55.1,
53.5,
... | 0 | 24.15 | null | histogram: Distribution of journey time (minutes)
Horizontal axis: Journey time
Vertical axis: Frequency (observations)
Bin | Count
-----------
43.8 to 48.24 | 8
48.24 to 52.68 | 8
52.68 to 57.12 | 16
57.12 to 61.56 | 23
61.56 to 66 | 9
66 to 70.44 | 6
70.44 to 74.88 | 6
74.88 to 79.32 | 3
79.32 to 83.76 | 0
83.76 to ... |
images/transport_journey-0021-clean.png | dv-0021-clean-01 | transport_journey-0021 | transport | Is the distribution of journey time roughly symmetric, left-skewed, or right-skewed? | roughly symmetric | Answer: roughly symmetric
Evidence: skewness index: 0.088
Calculation: (mean - median) / sd = 0.088 | structured | statistical_interpretation | skew | histogram | Distribution of journey time (minutes) | Journey time | Frequency | observations | easy | [] | distribution_shape | [
"skewness index: 0.088"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
58.9,
59.7,
70.2,
58.9,
59.3,
48.2,
68.9,
58.5,
52.9,
57.9,
68.4,
58.7,
70.5,
54.1,
58.5,
57.1,
46.5,
53.1,
59.1,
72.3,
64.5,
64.1,
54.8,
49.1,
88.2,
58.7,
58.2,
51.1,
71.6,
60.4,
55.5,
50.2,
54.9,
64.8,
54.5,
47.1,
63,
52.9,
55.1,
53.5,
... | 0 | 24.15 | null | histogram: Distribution of journey time (minutes)
Horizontal axis: Journey time
Vertical axis: Frequency (observations)
Bin | Count
-----------
43.8 to 48.24 | 8
48.24 to 52.68 | 8
52.68 to 57.12 | 16
57.12 to 61.56 | 23
61.56 to 66 | 9
66 to 70.44 | 6
70.44 to 74.88 | 6
74.88 to 79.32 | 3
79.32 to 83.76 | 0
83.76 to ... |
images/transport_journey-0021-clean.png | dv-0021-clean-02 | transport_journey-0021 | transport | Roughly where does the median journey time fall? | 57.12 to 61.56 | Answer: 57.12 to 61.56
Evidence: median observation: 58.5
Calculation: median = 58.5 | structured | statistical_interpretation | central_tendency | histogram | Distribution of journey time (minutes) | Journey time | Frequency | observations | easy | [] | median_bin | [
"median observation: 58.5"
] | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"21",
"22",
"23",
"24",
"25",
"26",
"27",
"28",
"29",
"30",
"31",
"32",
"33",
"34",
"35",
"36",
"37",
"38",
"39",
"40",
"41",... | [
58.9,
59.7,
70.2,
58.9,
59.3,
48.2,
68.9,
58.5,
52.9,
57.9,
68.4,
58.7,
70.5,
54.1,
58.5,
57.1,
46.5,
53.1,
59.1,
72.3,
64.5,
64.1,
54.8,
49.1,
88.2,
58.7,
58.2,
51.1,
71.6,
60.4,
55.5,
50.2,
54.9,
64.8,
54.5,
47.1,
63,
52.9,
55.1,
53.5,
... | 0 | 24.15 | null | histogram: Distribution of journey time (minutes)
Horizontal axis: Journey time
Vertical axis: Frequency (observations)
Bin | Count
-----------
43.8 to 48.24 | 8
48.24 to 52.68 | 8
52.68 to 57.12 | 16
57.12 to 61.56 | 23
61.56 to 66 | 9
66 to 70.44 | 6
70.44 to 74.88 | 6
74.88 to 79.32 | 3
79.32 to 83.76 | 0
83.76 to ... |
images/finance-0022-dense_labels.png | dv-0022-dense_labels-00 | finance-0022 | finance | According to the chart, which month has the highest operating expenses? | January | January. The relevant evidence is January: 236, February: 172, March: 222, April: 159, May: 68, June: 69. Compare every category and take the largest. | detailed | ranking | highest | grouped_bar | Operating expenses by month | Month | Operating expenses | million USD | medium | [
"dense_labels"
] | argmax | [
"January: 236",
"February: 172",
"March: 222",
"April: 159",
"May: 68",
"June: 69"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
236,
172,
222,
159,
68,
69
] | 0 | 247.8 | null | grouped bar chart: Operating expenses by month
Horizontal axis: Month
Vertical axis: Operating expenses (million USD)
Month | Operating expenses | Budget
-----------------------------------
January | 236 | 32
February | 172 | 44
March | 222 | 89
April | 159 | 109
May | 68 | 157
June | 69 | 218
According to the chart,... |
images/finance-0022-dense_labels.png | dv-0022-dense_labels-01 | finance-0022 | finance | Which month recorded the smallest operating expenses? | May | Answer: May
Evidence: January: 236; February: 172; March: 222; April: 159; May: 68; June: 69
Reasoning: Compare every category and take the smallest. | structured | ranking | lowest | grouped_bar | Operating expenses by month | Month | Operating expenses | million USD | medium | [
"dense_labels"
] | argmin | [
"January: 236",
"February: 172",
"March: 222",
"April: 159",
"May: 68",
"June: 69"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
236,
172,
222,
159,
68,
69
] | 0 | 247.8 | null | grouped bar chart: Operating expenses by month
Horizontal axis: Month
Vertical axis: Operating expenses (million USD)
Month | Operating expenses | Budget
-----------------------------------
January | 236 | 32
February | 172 | 44
March | 222 | 89
April | 159 | 109
May | 68 | 157
June | 69 | 218
Which month recorded th... |
images/finance-0022-dense_labels.png | dv-0022-dense_labels-02 | finance-0022 | finance | By how much does the operating expenses in January exceed May? | 168 | Answer: 168
Evidence: January: 236; May: 68
Calculation: 236 - 68
Reasoning: Read both values and subtract the second from the first. | structured | quantitative_reasoning | difference | grouped_bar | Operating expenses by month | Month | Operating expenses | million USD | medium | [
"dense_labels"
] | subtraction | [
"January: 236",
"May: 68"
] | [
"January",
"May"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
236,
172,
222,
159,
68,
69
] | 0 | 247.8 | null | grouped bar chart: Operating expenses by month
Horizontal axis: Month
Vertical axis: Operating expenses (million USD)
Month | Operating expenses | Budget
-----------------------------------
January | 236 | 32
February | 172 | 44
March | 222 | 89
April | 159 | 109
May | 68 | 157
June | 69 | 218
By how much does the op... |
images/finance-0022-dense_labels.png | dv-0022-dense_labels-03 | finance-0022 | finance | How does the operating expenses in May compare with January? | May is lower than January | Answer: May is lower than January
Evidence: May: 68; January: 236
Reasoning: Read both values and compare them directly. | structured | comparison | greater_less_equal | grouped_bar | Operating expenses by month | Month | Operating expenses | million USD | medium | [
"dense_labels"
] | comparison | [
"May: 68",
"January: 236"
] | [
"May",
"January"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
236,
172,
222,
159,
68,
69
] | 0 | 247.8 | null | grouped bar chart: Operating expenses by month
Horizontal axis: Month
Vertical axis: Operating expenses (million USD)
Month | Operating expenses | Budget
-----------------------------------
January | 236 | 32
February | 172 | 44
March | 222 | 89
April | 159 | 109
May | 68 | 157
June | 69 | 218
How does the operating ... |
images/finance-0022-dense_labels.png | dv-0022-dense_labels-04 | finance-0022 | finance | What is the overall direction of operating expenses from January to June? | decreased | decreased. The relevant evidence is January: 236, June: 69. Compare the last value with the first. | detailed | trend | overall_direction | grouped_bar | Operating expenses by month | Month | Operating expenses | million USD | medium | [
"dense_labels"
] | decreasing | [
"January: 236",
"June: 69"
] | [
"January",
"June"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
236,
172,
222,
159,
68,
69
] | 0 | 247.8 | null | grouped bar chart: Operating expenses by month
Horizontal axis: Month
Vertical axis: Operating expenses (million USD)
Month | Operating expenses | Budget
-----------------------------------
January | 236 | 32
February | 172 | 44
March | 222 | 89
April | 159 | 109
May | 68 | 157
June | 69 | 218
What is the overall dir... |
images/agriculture-0023-dense_labels.png | dv-0023-dense_labels-00 | agriculture-0023 | agriculture | According to the chart, which year has the highest rainfall? | 2023 | 2023. The relevant evidence is 2022: 728, 2023: 1313, 2024: 738, 2025: 221. Compare every category and take the largest. | detailed | ranking | highest | grouped_bar | Rainfall by year | Year | Rainfall | millimetres | medium | [
"dense_labels"
] | argmax | [
"2022: 728",
"2023: 1313",
"2024: 738",
"2025: 221"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
728,
1313,
738,
221
] | 0 | 1,378.65 | null | grouped bar chart: Rainfall by year
Horizontal axis: Year
Vertical axis: Rainfall (millimetres)
Year | Rainfall | Budget
------------------------
2022 | 728 | 1273
2023 | 1313 | 804
2024 | 738 | 490
2025 | 221 | 588
According to the chart, which year has the highest rainfall? |
images/agriculture-0023-dense_labels.png | dv-0023-dense_labels-01 | agriculture-0023 | agriculture | Which year recorded the smallest rainfall? | 2025 | Answer: 2025
Evidence: 2022: 728; 2023: 1313; 2024: 738; 2025: 221
Reasoning: Compare every category and take the smallest. | structured | ranking | lowest | grouped_bar | Rainfall by year | Year | Rainfall | millimetres | medium | [
"dense_labels"
] | argmin | [
"2022: 728",
"2023: 1313",
"2024: 738",
"2025: 221"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
728,
1313,
738,
221
] | 0 | 1,378.65 | null | grouped bar chart: Rainfall by year
Horizontal axis: Year
Vertical axis: Rainfall (millimetres)
Year | Rainfall | Budget
------------------------
2022 | 728 | 1273
2023 | 1313 | 804
2024 | 738 | 490
2025 | 221 | 588
Which year recorded the smallest rainfall? |
images/agriculture-0023-dense_labels.png | dv-0023-dense_labels-02 | agriculture-0023 | agriculture | What is the difference in rainfall between 2024 and 2022? | 10 | Answer: 10
Evidence: 2024: 738; 2022: 728
Calculation: 738 - 728
Reasoning: Read both values and subtract the second from the first. | structured | quantitative_reasoning | difference | grouped_bar | Rainfall by year | Year | Rainfall | millimetres | medium | [
"dense_labels"
] | subtraction | [
"2024: 738",
"2022: 728"
] | [
"2024",
"2022"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
728,
1313,
738,
221
] | 0 | 1,378.65 | null | grouped bar chart: Rainfall by year
Horizontal axis: Year
Vertical axis: Rainfall (millimetres)
Year | Rainfall | Budget
------------------------
2022 | 728 | 1273
2023 | 1313 | 804
2024 | 738 | 490
2025 | 221 | 588
What is the difference in rainfall between 2024 and 2022? |
images/agriculture-0023-dense_labels.png | dv-0023-dense_labels-03 | agriculture-0023 | agriculture | Is the rainfall in 2022 higher or lower than in 2024? | 2022 is lower than 2024 | 2022 is lower than 2024. The relevant evidence is 2022: 728, 2024: 738. Read both values and compare them directly. | detailed | comparison | greater_less_equal | grouped_bar | Rainfall by year | Year | Rainfall | millimetres | medium | [
"dense_labels"
] | comparison | [
"2022: 728",
"2024: 738"
] | [
"2022",
"2024"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
728,
1313,
738,
221
] | 0 | 1,378.65 | null | grouped bar chart: Rainfall by year
Horizontal axis: Year
Vertical axis: Rainfall (millimetres)
Year | Rainfall | Budget
------------------------
2022 | 728 | 1273
2023 | 1313 | 804
2024 | 738 | 490
2025 | 221 | 588
Is the rainfall in 2022 higher or lower than in 2024? |
images/agriculture-0023-dense_labels.png | dv-0023-dense_labels-04 | agriculture-0023 | agriculture | Did the rainfall increase, decrease, or remain unchanged from 2022 to 2025? | decreased | decreased. Relevant values: 2022: 728, 2025: 221. | evidence | trend | overall_direction | grouped_bar | Rainfall by year | Year | Rainfall | millimetres | medium | [
"dense_labels"
] | decreasing | [
"2022: 728",
"2025: 221"
] | [
"2022",
"2025"
] | [
"2022",
"2023",
"2024",
"2025"
] | [
728,
1313,
738,
221
] | 0 | 1,378.65 | null | grouped bar chart: Rainfall by year
Horizontal axis: Year
Vertical axis: Rainfall (millimetres)
Year | Rainfall | Budget
------------------------
2022 | 728 | 1273
2023 | 1313 | 804
2024 | 738 | 490
2025 | 221 | 588
Did the rainfall increase, decrease, or remain unchanged from 2022 to 2025? |
images/marketing-0024-dense_labels.png | dv-0024-dense_labels-00 | marketing-0024 | marketing | According to the chart, which week has the highest email open rate? | Week 4 | Week 4. The relevant evidence is Week 1: 47, Week 2: 41, Week 3: 48, Week 4: 62, Week 5: 14, Week 6: 35. Compare every category and take the largest. | detailed | ranking | highest | bar | Email open rate by week | Week | Email open rate | percent | medium | [
"dense_labels"
] | argmax | [
"Week 1: 47",
"Week 2: 41",
"Week 3: 48",
"Week 4: 62",
"Week 5: 14",
"Week 6: 35"
] | [
"Week 1",
"Week 2",
"Week 3",
"Week 4",
"Week 5",
"Week 6"
] | [
"Week 1",
"Week 2",
"Week 3",
"Week 4",
"Week 5",
"Week 6"
] | [
47,
41,
48,
62,
14,
35
] | 0 | 65.1 | null | bar chart: Email open rate by week
Horizontal axis: Week
Vertical axis: Email open rate (percent)
Week | Email open rate
----------------------
Week 1 | 47
Week 2 | 41
Week 3 | 48
Week 4 | 62
Week 5 | 14
Week 6 | 35
According to the chart, which week has the highest email open rate? |
images/marketing-0024-dense_labels.png | dv-0024-dense_labels-01 | marketing-0024 | marketing | According to the chart, which week has the lowest email open rate? | Week 5 | Week 5. The relevant evidence is Week 1: 47, Week 2: 41, Week 3: 48, Week 4: 62, Week 5: 14, Week 6: 35. Compare every category and take the smallest. | detailed | ranking | lowest | bar | Email open rate by week | Week | Email open rate | percent | medium | [
"dense_labels"
] | argmin | [
"Week 1: 47",
"Week 2: 41",
"Week 3: 48",
"Week 4: 62",
"Week 5: 14",
"Week 6: 35"
] | [
"Week 1",
"Week 2",
"Week 3",
"Week 4",
"Week 5",
"Week 6"
] | [
"Week 1",
"Week 2",
"Week 3",
"Week 4",
"Week 5",
"Week 6"
] | [
47,
41,
48,
62,
14,
35
] | 0 | 65.1 | null | bar chart: Email open rate by week
Horizontal axis: Week
Vertical axis: Email open rate (percent)
Week | Email open rate
----------------------
Week 1 | 47
Week 2 | 41
Week 3 | 48
Week 4 | 62
Week 5 | 14
Week 6 | 35
According to the chart, which week has the lowest email open rate? |
images/marketing-0024-dense_labels.png | dv-0024-dense_labels-02 | marketing-0024 | marketing | By how much does the email open rate in Week 4 exceed Week 1? | 15 | Answer: 15
Evidence: Week 4: 62; Week 1: 47
Calculation: 62 - 47
Reasoning: Read both values and subtract the second from the first. | structured | quantitative_reasoning | difference | bar | Email open rate by week | Week | Email open rate | percent | medium | [
"dense_labels"
] | subtraction | [
"Week 4: 62",
"Week 1: 47"
] | [
"Week 4",
"Week 1"
] | [
"Week 1",
"Week 2",
"Week 3",
"Week 4",
"Week 5",
"Week 6"
] | [
47,
41,
48,
62,
14,
35
] | 0 | 65.1 | null | bar chart: Email open rate by week
Horizontal axis: Week
Vertical axis: Email open rate (percent)
Week | Email open rate
----------------------
Week 1 | 47
Week 2 | 41
Week 3 | 48
Week 4 | 62
Week 5 | 14
Week 6 | 35
By how much does the email open rate in Week 4 exceed Week 1? |
images/marketing-0024-dense_labels.png | dv-0024-dense_labels-03 | marketing-0024 | marketing | How does the email open rate in Week 4 compare with Week 1? | Week 4 is higher than Week 1 | Answer: Week 4 is higher than Week 1
Evidence: Week 4: 62; Week 1: 47
Reasoning: Read both values and compare them directly. | structured | comparison | greater_less_equal | bar | Email open rate by week | Week | Email open rate | percent | medium | [
"dense_labels"
] | comparison | [
"Week 4: 62",
"Week 1: 47"
] | [
"Week 4",
"Week 1"
] | [
"Week 1",
"Week 2",
"Week 3",
"Week 4",
"Week 5",
"Week 6"
] | [
47,
41,
48,
62,
14,
35
] | 0 | 65.1 | null | bar chart: Email open rate by week
Horizontal axis: Week
Vertical axis: Email open rate (percent)
Week | Email open rate
----------------------
Week 1 | 47
Week 2 | 41
Week 3 | 48
Week 4 | 62
Week 5 | 14
Week 6 | 35
How does the email open rate in Week 4 compare with Week 1? |
images/marketing-0024-dense_labels.png | dv-0024-dense_labels-04 | marketing-0024 | marketing | What is the overall direction of email open rate from Week 1 to Week 6? | decreased | Answer: decreased
Evidence: Week 1: 47; Week 6: 35
Reasoning: Compare the last value with the first. | structured | trend | overall_direction | bar | Email open rate by week | Week | Email open rate | percent | medium | [
"dense_labels"
] | decreasing | [
"Week 1: 47",
"Week 6: 35"
] | [
"Week 1",
"Week 6"
] | [
"Week 1",
"Week 2",
"Week 3",
"Week 4",
"Week 5",
"Week 6"
] | [
47,
41,
48,
62,
14,
35
] | 0 | 65.1 | null | bar chart: Email open rate by week
Horizontal axis: Week
Vertical axis: Email open rate (percent)
Week | Email open rate
----------------------
Week 1 | 47
Week 2 | 41
Week 3 | 48
Week 4 | 62
Week 5 | 14
Week 6 | 35
What is the overall direction of email open rate from Week 1 to Week 6? |
images/operations-0025-dense_labels.png | dv-0025-dense_labels-00 | operations-0025 | operations | According to the chart, which month has the highest downtime? | February | Answer: February
Evidence: January: 49; February: 85; March: 78; April: 48; May: 22; June: 12
Reasoning: Compare every category and take the largest. | structured | ranking | highest | line | Downtime by month | Month | Downtime | hours | medium | [
"dense_labels"
] | argmax | [
"January: 49",
"February: 85",
"March: 78",
"April: 48",
"May: 22",
"June: 12"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
49,
85,
78,
48,
22,
12
] | 8.35 | 88.65 | null | line chart: Downtime by month
Horizontal axis: Month
Vertical axis: Downtime (hours)
Month | Downtime
----------------
January | 49
February | 85
March | 78
April | 48
May | 22
June | 12
According to the chart, which month has the highest downtime? |
images/operations-0025-dense_labels.png | dv-0025-dense_labels-01 | operations-0025 | operations | According to the chart, which month has the lowest downtime? | June | Answer: June
Evidence: January: 49; February: 85; March: 78; April: 48; May: 22; June: 12
Reasoning: Compare every category and take the smallest. | structured | ranking | lowest | line | Downtime by month | Month | Downtime | hours | medium | [
"dense_labels"
] | argmin | [
"January: 49",
"February: 85",
"March: 78",
"April: 48",
"May: 22",
"June: 12"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
49,
85,
78,
48,
22,
12
] | 8.35 | 88.65 | null | line chart: Downtime by month
Horizontal axis: Month
Vertical axis: Downtime (hours)
Month | Downtime
----------------
January | 49
February | 85
March | 78
April | 48
May | 22
June | 12
According to the chart, which month has the lowest downtime? |
images/operations-0025-dense_labels.png | dv-0025-dense_labels-02 | operations-0025 | operations | What is the difference in downtime between April and June? | 36 | Answer: 36
Evidence: April: 48; June: 12
Calculation: 48 - 12
Reasoning: Read both values and subtract the second from the first. | structured | quantitative_reasoning | difference | line | Downtime by month | Month | Downtime | hours | medium | [
"dense_labels"
] | subtraction | [
"April: 48",
"June: 12"
] | [
"April",
"June"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
49,
85,
78,
48,
22,
12
] | 8.35 | 88.65 | null | line chart: Downtime by month
Horizontal axis: Month
Vertical axis: Downtime (hours)
Month | Downtime
----------------
January | 49
February | 85
March | 78
April | 48
May | 22
June | 12
What is the difference in downtime between April and June? |
images/operations-0025-dense_labels.png | dv-0025-dense_labels-03 | operations-0025 | operations | How does the downtime in June compare with April? | June is lower than April | June is lower than April. Relevant values: June: 12, April: 48. | evidence | comparison | greater_less_equal | line | Downtime by month | Month | Downtime | hours | medium | [
"dense_labels"
] | comparison | [
"June: 12",
"April: 48"
] | [
"June",
"April"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
49,
85,
78,
48,
22,
12
] | 8.35 | 88.65 | null | line chart: Downtime by month
Horizontal axis: Month
Vertical axis: Downtime (hours)
Month | Downtime
----------------
January | 49
February | 85
March | 78
April | 48
May | 22
June | 12
How does the downtime in June compare with April? |
images/operations-0025-dense_labels.png | dv-0025-dense_labels-04 | operations-0025 | operations | Between January and June, did the downtime rise, fall, or stay the same? | decreased | decreased. Relevant values: January: 49, June: 12. | evidence | trend | overall_direction | line | Downtime by month | Month | Downtime | hours | medium | [
"dense_labels"
] | decreasing | [
"January: 49",
"June: 12"
] | [
"January",
"June"
] | [
"January",
"February",
"March",
"April",
"May",
"June"
] | [
49,
85,
78,
48,
22,
12
] | 8.35 | 88.65 | null | line chart: Downtime by month
Horizontal axis: Month
Vertical axis: Downtime (hours)
Month | Downtime
----------------
January | 49
February | 85
March | 78
April | 48
May | 22
June | 12
Between January and June, did the downtime rise, fall, or stay the same? |
images/energy-0026-dense_labels.png | dv-0026-dense_labels-00 | energy-0026 | energy | According to the chart, which month has the highest outage duration? | Jun | Answer: Jun
Evidence: May: 443; Jun: 857; Jul: 475; Aug: 150
Reasoning: Compare every category and take the largest. | structured | ranking | highest | line | Outage duration by month | Month | Outage duration | minutes per customer | medium | [
"dense_labels"
] | argmax | [
"May: 443",
"Jun: 857",
"Jul: 475",
"Aug: 150"
] | [
"May",
"Jun",
"Jul",
"Aug"
] | [
"May",
"Jun",
"Jul",
"Aug"
] | [
443,
857,
475,
150
] | 114.65 | 892.35 | null | line chart: Outage duration by month
Horizontal axis: Month
Vertical axis: Outage duration (minutes per customer)
Month | Outage duration
-----------------------
May | 443
Jun | 857
Jul | 475
Aug | 150
According to the chart, which month has the highest outage duration? |
images/energy-0026-dense_labels.png | dv-0026-dense_labels-01 | energy-0026 | energy | According to the chart, which month has the lowest outage duration? | Aug | Aug. The relevant evidence is May: 443, Jun: 857, Jul: 475, Aug: 150. Compare every category and take the smallest. | detailed | ranking | lowest | line | Outage duration by month | Month | Outage duration | minutes per customer | medium | [
"dense_labels"
] | argmin | [
"May: 443",
"Jun: 857",
"Jul: 475",
"Aug: 150"
] | [
"May",
"Jun",
"Jul",
"Aug"
] | [
"May",
"Jun",
"Jul",
"Aug"
] | [
443,
857,
475,
150
] | 114.65 | 892.35 | null | line chart: Outage duration by month
Horizontal axis: Month
Vertical axis: Outage duration (minutes per customer)
Month | Outage duration
-----------------------
May | 443
Jun | 857
Jul | 475
Aug | 150
According to the chart, which month has the lowest outage duration? |
images/energy-0026-dense_labels.png | dv-0026-dense_labels-02 | energy-0026 | energy | By how much does the outage duration in May exceed Aug? | 293 | 293. 443 - 150 gives the difference. | moderate | quantitative_reasoning | difference | line | Outage duration by month | Month | Outage duration | minutes per customer | medium | [
"dense_labels"
] | subtraction | [
"May: 443",
"Aug: 150"
] | [
"May",
"Aug"
] | [
"May",
"Jun",
"Jul",
"Aug"
] | [
443,
857,
475,
150
] | 114.65 | 892.35 | null | line chart: Outage duration by month
Horizontal axis: Month
Vertical axis: Outage duration (minutes per customer)
Month | Outage duration
-----------------------
May | 443
Jun | 857
Jul | 475
Aug | 150
By how much does the outage duration in May exceed Aug? |
images/energy-0026-dense_labels.png | dv-0026-dense_labels-03 | energy-0026 | energy | How does the outage duration in Aug compare with May? | Aug is lower than May | Aug is lower than May. The comparison follows from the values shown. | moderate | comparison | greater_less_equal | line | Outage duration by month | Month | Outage duration | minutes per customer | medium | [
"dense_labels"
] | comparison | [
"Aug: 150",
"May: 443"
] | [
"Aug",
"May"
] | [
"May",
"Jun",
"Jul",
"Aug"
] | [
443,
857,
475,
150
] | 114.65 | 892.35 | null | line chart: Outage duration by month
Horizontal axis: Month
Vertical axis: Outage duration (minutes per customer)
Month | Outage duration
-----------------------
May | 443
Jun | 857
Jul | 475
Aug | 150
How does the outage duration in Aug compare with May? |
images/energy-0026-dense_labels.png | dv-0026-dense_labels-04 | energy-0026 | energy | Did the outage duration increase, decrease, or remain unchanged from May to Aug? | decreased | Answer: decreased
Evidence: May: 443; Aug: 150
Reasoning: Compare the last value with the first. | structured | trend | overall_direction | line | Outage duration by month | Month | Outage duration | minutes per customer | medium | [
"dense_labels"
] | decreasing | [
"May: 443",
"Aug: 150"
] | [
"May",
"Aug"
] | [
"May",
"Jun",
"Jul",
"Aug"
] | [
443,
857,
475,
150
] | 114.65 | 892.35 | null | line chart: Outage duration by month
Horizontal axis: Month
Vertical axis: Outage duration (minutes per customer)
Month | Outage duration
-----------------------
May | 443
Jun | 857
Jul | 475
Aug | 150
Did the outage duration increase, decrease, or remain unchanged from May to Aug? |
images/health-0027-dense_labels.png | dv-0027-dense_labels-00 | health-0027 | health | According to the chart, which facility has the highest referrals? | Urban Clinic | Answer: Urban Clinic
Evidence: Health Centre A: 309; Urban Clinic: 331; Health Centre B: 273; Rural Clinic: 306; District Hospital: 321
Reasoning: Compare every category and take the largest. | structured | ranking | highest | line | Referrals by facility | Facility | Referrals | referrals | medium | [
"dense_labels"
] | argmax | [
"Health Centre A: 309",
"Urban Clinic: 331",
"Health Centre B: 273",
"Rural Clinic: 306",
"District Hospital: 321"
] | [
"Health Centre A",
"Urban Clinic",
"Health Centre B",
"Rural Clinic",
"District Hospital"
] | [
"Health Centre A",
"Urban Clinic",
"Health Centre B",
"Rural Clinic",
"District Hospital"
] | [
309,
331,
273,
306,
321
] | 270.1 | 333.9 | null | line chart: Referrals by facility
Horizontal axis: Facility
Vertical axis: Referrals (referrals)
Facility | Referrals
--------------------
Health Centre A | 309
Urban Clinic | 331
Health Centre B | 273
Rural Clinic | 306
District Hospital | 321
According to the chart, which facility has the highest referrals? |
images/health-0027-dense_labels.png | dv-0027-dense_labels-01 | health-0027 | health | Which facility had the lowest referrals? | Health Centre B | Answer: Health Centre B
Evidence: Health Centre A: 309; Urban Clinic: 331; Health Centre B: 273; Rural Clinic: 306; District Hospital: 321
Reasoning: Compare every category and take the smallest. | structured | ranking | lowest | line | Referrals by facility | Facility | Referrals | referrals | medium | [
"dense_labels"
] | argmin | [
"Health Centre A: 309",
"Urban Clinic: 331",
"Health Centre B: 273",
"Rural Clinic: 306",
"District Hospital: 321"
] | [
"Health Centre A",
"Urban Clinic",
"Health Centre B",
"Rural Clinic",
"District Hospital"
] | [
"Health Centre A",
"Urban Clinic",
"Health Centre B",
"Rural Clinic",
"District Hospital"
] | [
309,
331,
273,
306,
321
] | 270.1 | 333.9 | null | line chart: Referrals by facility
Horizontal axis: Facility
Vertical axis: Referrals (referrals)
Facility | Referrals
--------------------
Health Centre A | 309
Urban Clinic | 331
Health Centre B | 273
Rural Clinic | 306
District Hospital | 321
Which facility had the lowest referrals? |
images/health-0027-dense_labels.png | dv-0027-dense_labels-02 | health-0027 | health | By how much does the referrals in Urban Clinic exceed District Hospital? | 10 | Answer: 10
Evidence: Urban Clinic: 331; District Hospital: 321
Calculation: 331 - 321
Reasoning: Read both values and subtract the second from the first. | structured | quantitative_reasoning | difference | line | Referrals by facility | Facility | Referrals | referrals | medium | [
"dense_labels"
] | subtraction | [
"Urban Clinic: 331",
"District Hospital: 321"
] | [
"Urban Clinic",
"District Hospital"
] | [
"Health Centre A",
"Urban Clinic",
"Health Centre B",
"Rural Clinic",
"District Hospital"
] | [
309,
331,
273,
306,
321
] | 270.1 | 333.9 | null | line chart: Referrals by facility
Horizontal axis: Facility
Vertical axis: Referrals (referrals)
Facility | Referrals
--------------------
Health Centre A | 309
Urban Clinic | 331
Health Centre B | 273
Rural Clinic | 306
District Hospital | 321
By how much does the referrals in Urban Clinic exceed District Hospital? |
images/health-0027-dense_labels.png | dv-0027-dense_labels-03 | health-0027 | health | Is the referrals in Urban Clinic higher or lower than in District Hospital? | Urban Clinic is higher than District Hospital | Answer: Urban Clinic is higher than District Hospital
Evidence: Urban Clinic: 331; District Hospital: 321
Reasoning: Read both values and compare them directly. | structured | comparison | greater_less_equal | line | Referrals by facility | Facility | Referrals | referrals | medium | [
"dense_labels"
] | comparison | [
"Urban Clinic: 331",
"District Hospital: 321"
] | [
"Urban Clinic",
"District Hospital"
] | [
"Health Centre A",
"Urban Clinic",
"Health Centre B",
"Rural Clinic",
"District Hospital"
] | [
309,
331,
273,
306,
321
] | 270.1 | 333.9 | null | line chart: Referrals by facility
Horizontal axis: Facility
Vertical axis: Referrals (referrals)
Facility | Referrals
--------------------
Health Centre A | 309
Urban Clinic | 331
Health Centre B | 273
Rural Clinic | 306
District Hospital | 321
Is the referrals in Urban Clinic higher or lower than in District Hospita... |
images/education-0028-dense_labels.png | dv-0028-dense_labels-00 | education-0028 | education | Which school recorded the largest students per teacher? | School A | School A. Relevant values: School A: 61, School D: 26, School E: 27, School C: 52, School B: 17. | evidence | ranking | highest | bar | Students per teacher by school | School | Students per teacher | students | medium | [
"dense_labels"
] | argmax | [
"School A: 61",
"School D: 26",
"School E: 27",
"School C: 52",
"School B: 17"
] | [
"School A",
"School D",
"School E",
"School C",
"School B"
] | [
"School A",
"School D",
"School E",
"School C",
"School B"
] | [
61,
26,
27,
52,
17
] | 0 | 64.05 | null | bar chart: Students per teacher by school
Horizontal axis: School
Vertical axis: Students per teacher (students)
School | Students per teacher
-----------------------------
School A | 61
School D | 26
School E | 27
School C | 52
School B | 17
Which school recorded the largest students per teacher? |
images/education-0028-dense_labels.png | dv-0028-dense_labels-01 | education-0028 | education | Which school had the lowest students per teacher? | School B | School B. Relevant values: School A: 61, School D: 26, School E: 27, School C: 52, School B: 17. | evidence | ranking | lowest | bar | Students per teacher by school | School | Students per teacher | students | medium | [
"dense_labels"
] | argmin | [
"School A: 61",
"School D: 26",
"School E: 27",
"School C: 52",
"School B: 17"
] | [
"School A",
"School D",
"School E",
"School C",
"School B"
] | [
"School A",
"School D",
"School E",
"School C",
"School B"
] | [
61,
26,
27,
52,
17
] | 0 | 64.05 | null | bar chart: Students per teacher by school
Horizontal axis: School
Vertical axis: Students per teacher (students)
School | Students per teacher
-----------------------------
School A | 61
School D | 26
School E | 27
School C | 52
School B | 17
Which school had the lowest students per teacher? |
images/education-0028-dense_labels.png | dv-0028-dense_labels-02 | education-0028 | education | What is the difference in students per teacher between School A and School B? | 44 | 44. The relevant evidence is School A: 61, School B: 17. Read both values and subtract the second from the first. | detailed | quantitative_reasoning | difference | bar | Students per teacher by school | School | Students per teacher | students | medium | [
"dense_labels"
] | subtraction | [
"School A: 61",
"School B: 17"
] | [
"School A",
"School B"
] | [
"School A",
"School D",
"School E",
"School C",
"School B"
] | [
61,
26,
27,
52,
17
] | 0 | 64.05 | null | bar chart: Students per teacher by school
Horizontal axis: School
Vertical axis: Students per teacher (students)
School | Students per teacher
-----------------------------
School A | 61
School D | 26
School E | 27
School C | 52
School B | 17
What is the difference in students per teacher between School A and School ... |
images/education-0028-dense_labels.png | dv-0028-dense_labels-03 | education-0028 | education | How does the students per teacher in School B compare with School A? | School B is lower than School A | School B is lower than School A. The relevant evidence is School B: 17, School A: 61. Read both values and compare them directly. | detailed | comparison | greater_less_equal | bar | Students per teacher by school | School | Students per teacher | students | medium | [
"dense_labels"
] | comparison | [
"School B: 17",
"School A: 61"
] | [
"School B",
"School A"
] | [
"School A",
"School D",
"School E",
"School C",
"School B"
] | [
61,
26,
27,
52,
17
] | 0 | 64.05 | null | bar chart: Students per teacher by school
Horizontal axis: School
Vertical axis: Students per teacher (students)
School | Students per teacher
-----------------------------
School A | 61
School D | 26
School E | 27
School C | 52
School B | 17
How does the students per teacher in School B compare with School A? |
images/transport-0029-dense_labels.png | dv-0029-dense_labels-00 | transport-0029 | transport | According to the chart, which mode has the highest incidents? | Bus | Bus had the highest value among the categories shown. | moderate | ranking | highest | bar | Incidents by mode | Mode | Incidents | incidents | medium | [
"dense_labels"
] | argmax | [
"Cycle: 14",
"Ferry: 87",
"Bus: 88",
"Tram: 38"
] | [
"Cycle",
"Ferry",
"Bus",
"Tram"
] | [
"Cycle",
"Ferry",
"Bus",
"Tram"
] | [
14,
87,
88,
38
] | 0 | 92.4 | 1 | bar chart: Incidents by mode
Horizontal axis: Mode
Vertical axis: Incidents (incidents)
Mode | Incidents
----------------
Cycle | 14
Ferry | 87
Bus | 88
Tram | 38
According to the chart, which mode has the highest incidents? |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.