Fit an exponential or quadratic model to data
Grade 9 Math Worksheet

Circle the best answer, or write your answer on the line.
Ren plotted algae growing in a fishbowl, and the calculator fit y = 30(1.35)^x. What does the model predict for x = 4, to the nearest whole number?
Tala plotted lily pads spreading on a park pond, and the calculator fit y = 8(1.45)^x. What does the model predict for x = 5, to the nearest whole number?
Scatter plot on a dot grid. Across (x): weeks, from 0 to 7, a tick every 1. Up (y): lily pads on the pond, from 0 to 90, a tick every 10. Each dot is one count. The dots, left to right: (0, 8), (1, 12), (2, 17), (3, 25), (4, 36), (5, 48), (6, 78). The calculator's fit, rounded: y = 8(1.45)^x. Scatter plot on a dot grid. Across (x): weeks, from 0 to 7, a tick every 1. Up (y): lily pads on the pond, from 0 to 90, a tick every 10. Each dot is one count. The dots, left to right: (0, 8), (1, 12), (2, 17), (3, 25), (4, 36), (5, 48), (6, 78). The calculator's fit, rounded: y = 8(1.45)^x. Scatter plot on a dot grid. Each dot is one count. The calculator's fit, rounded: y = 8(1.45)^x. 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 70 80 90 weeks lily pads on the pond y = 8(1.45)^x Zee plotted a bowl of soup cooling, and the calculator fit y = 62(0.94)^x. What does the model predict for x = 6, to the nearest whole number?
Scatter plot on a dot grid. Across (x): minutes, from 0 to 21, a tick every 3. Up (y): degrees above room temperature, from 0 to 80, a tick every 10. Each dot is one reading. The dots, left to right: (0, 61.2), (3, 52.4), (6, 42.7), (9, 36), (12, 29.2), (15, 24.3), (18, 20.9). The calculator's fit, rounded: y = 62(0.94)^x. Scatter plot on a dot grid. Across (x): minutes, from 0 to 21, a tick every 3. Up (y): degrees above room temperature, from 0 to 80, a tick every 10. Each dot is one reading. The dots, left to right: (0, 61.2), (3, 52.4), (6, 42.7), (9, 36), (12, 29.2), (15, 24.3), (18, 20.9). The calculator's fit, rounded: y = 62(0.94)^x. 0 3 6 9 12 15 18 21 0 10 20 30 40 50 60 70 80 minutes degrees above room temperature y = 62(0.94)^x Ari plotted a wet beach towel drying in the sun, and the calculator fit y = 240(0.72)^x. What does the model predict for x = 3, to the nearest whole number?
Scatter plot on a dot grid. Across (x): hours, from 0 to 7, a tick every 1. Up (y): grams of water left in the towel, from 0 to 250, a tick every 30. Each dot is one weighing. The dots, left to right: (0, 236), (1, 172), (2, 126), (3, 89), (4, 68), (5, 45), (6, 33). The calculator's fit, rounded: y = 240(0.72)^x. Scatter plot on a dot grid. Across (x): hours, from 0 to 7, a tick every 1. Up (y): grams of water left in the towel, from 0 to 250, a tick every 30. Each dot is one weighing. The dots, left to right: (0, 236), (1, 172), (2, 126), (3, 89), (4, 68), (5, 45), (6, 33). The calculator's fit, rounded: y = 240(0.72)^x. 0 1 2 3 4 5 6 7 0 30 60 90 120 150 180 210 240 hours grams of water left in the towel y = 240(0.72)^x Esme plotted a water rocket's height, and the calculator fit y = -4.9x² + 27.5x + 1.5. What is the residual for the dot at x = 3, to the nearest tenth?
Scatter plot on a dot grid. Across (x): seconds after launch, from 0 to 6, a tick every 1. Up (y): height in meters, from 0 to 60, a tick every 10. Each dot is one reading. The dots, left to right: (0, 1.9), (1, 23), (2, 37.1), (3, 40.4), (4, 32.2), (5, 16.1). The calculator's fit, rounded: y = -4.9x² + 27.5x + 1.5. Scatter plot on a dot grid. Across (x): seconds after launch, from 0 to 6, a tick every 1. Up (y): height in meters, from 0 to 60, a tick every 10. Each dot is one reading. The dots, left to right: (0, 1.9), (1, 23), (2, 37.1), (3, 40.4), (4, 32.2), (5, 16.1). The calculator's fit, rounded: y = -4.9x² + 27.5x + 1.5. Scatter plot on a dot grid 0 1 2 3 4 5 6 0 10 20 30 40 50 60 seconds after launch height in meters y = -4.9x² + 27.5x + 1.5 Each dot is one reading. The calculator's fit, rounded: y = -4.9x² + 27.5x + 1.5. Jun plotted how high a play ball bounces back, and the calculator fit y = 120(0.68)^x. What is the residual for the dot at x = 2, to the nearest tenth?
Scatter plot on a dot grid. Across (x): bounce number, from 0 to 6, a tick every 1. Up (y): rebound height in cm, from 0 to 140, a tick every 20. Each dot is one bounce. The dots, left to right: (0, 122.1), (1, 83), (2, 52.3), (3, 37.9), (4, 26), (5, 17.8). The calculator's fit, rounded: y = 120(0.68)^x. Scatter plot on a dot grid. Across (x): bounce number, from 0 to 6, a tick every 1. Up (y): rebound height in cm, from 0 to 140, a tick every 20. Each dot is one bounce. The dots, left to right: (0, 122.1), (1, 83), (2, 52.3), (3, 37.9), (4, 26), (5, 17.8). The calculator's fit, rounded: y = 120(0.68)^x. 0 1 2 3 4 5 6 0 20 40 60 80 100 120 140 bounce number rebound height in cm y = 120(0.68)^x Kira plotted visitors inside the science museum, and the calculator fit y = -2.5x² + 26x + 12. What is the residual for the dot at x = 5, to the nearest tenth?
Scatter plot on a dot grid. Across (x): hours after opening, from 0 to 10, a tick every 1. Up (y): visitors inside, from 0 to 90, a tick every 10. Each dot is one count. The dots, left to right: (0, 11), (1, 37), (2, 55), (3, 67), (4, 75), (5, 78), (6, 79), (7, 72), (8, 64), (9, 42). The calculator's fit, rounded: y = -2.5x² + 26x + 12. Scatter plot on a dot grid. Across (x): hours after opening, from 0 to 10, a tick every 1. Up (y): visitors inside, from 0 to 90, a tick every 10. Each dot is one count. The dots, left to right: (0, 11), (1, 37), (2, 55), (3, 67), (4, 75), (5, 78), (6, 79), (7, 72), (8, 64), (9, 42). The calculator's fit, rounded: y = -2.5x² + 26x + 12. 0 1 2 3 4 5 6 7 8 9 10 0 10 20 30 40 50 60 70 80 90 hours after opening visitors inside y = -2.5x² + 26x + 12 Read this first
Table, x | data | line | exponential
0 | 17 | -20 | 18; 1 | 32 | 23 | 29; 2 | 42 | 66 | 46; 3 | 71 | 109 | 74; 4 | 119 | 152 | 118; 5 | 194 | 195 | 189; 6 | 285 | 238 | 302. The line is y = 43x - 20.5; the exponential model is y = 18(1.6)^x. Predictions are rounded to whole numbers.
Luz fit a line and an exponential model to fruit flies in the science-class jar. The table lists both models' predictions. Which model fits the data better?
- The exponential model: its predictions stay close to every dot.
- The line, because it is closer at x = 5.
- The line, because a line is simpler and easier to use than a curve.
- Both fit equally well.
Table, x | data | line | quadratic
0 | 2.9 | 13 | 3; 2 | 12.8 | 13 | 13; 4 | 19.9 | 14 | 20; 6 | 22.7 | 14 | 23; 8 | 22 | 15 | 22; 10 | 15.8 | 16 | 17; 12 | 6.5 | 16 | 7. The line is y = 0.3x + 12.6; the quadratic model is y = -0.5x² + 6.4x + 2.5. Predictions are rounded to whole numbers.
Milo fit a line and a quadratic model to bean plant growth against plant food. The table lists both models' predictions. Which model fits the data better?
- The quadratic model: its predictions stay close to every dot.
- The line, because it is closer at x = 10.
- The line, because a line is simpler and easier to read than a curve.
- Both fit equally well.
Table, x | data | line | quadratic
0 | 11 | 39 | 10; 1 | 32 | 41 | 32; 2 | 45 | 44 | 48; 3 | 63 | 46 | 60; 4 | 67 | 49 | 68; 5 | 68 | 51 | 70; 6 | 69 | 53 | 68; 7 | 63 | 56 | 60; 8 | 47 | 58 | 48; 9 | 31 | 61 | 32. The line is y = 2.4x + 38.9; the quadratic model is y = -2.4x² + 24x + 10. Predictions are rounded to whole numbers.
Pia fit a line and a quadratic model to visitors inside the science museum. The table lists both models' predictions. Which model fits the data better?
- Both fit equally well.
- The line, because a line is simpler.
- The line, because it is closer at x = 2.
- The quadratic model: its predictions stay close to every dot.