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

Circle the best answer, or write your answer on the line.
Ren plotted duckweed spreading on the class pond, and the calculator fit y = 40(1.5)^x. What does the model predict for x = 3?
Tala plotted fruit flies in the science-class jar, and the calculator fit y = 25(1.4)^x. What does the model predict for x = 2?
Scatter plot on a dot grid. Across (x): days, from 0 to 7, a tick every 1. Up (y): fruit flies in the jar, from 0 to 190, a tick every 20. Each dot is one count. The dots, left to right: (0, 25), (1, 34), (2, 50), (3, 67), (4, 99), (5, 144), (6, 178). The calculator's fit, rounded: y = 25(1.4)^x. Fill in: 25 × 1.4 × 1.4 = ? Scatter plot on a dot grid. Across (x): days, from 0 to 7, a tick every 1. Up (y): fruit flies in the jar, from 0 to 190, a tick every 20. Each dot is one count. The dots, left to right: (0, 25), (1, 34), (2, 50), (3, 67), (4, 99), (5, 144), (6, 178). The calculator's fit, rounded: y = 25(1.4)^x. Fill in: 25 × 1.4 × 1.4 = ? 0 1 2 3 4 5 6 7 0 20 40 60 80 100 120 140 160 180 days fruit flies in the jar y = 25(1.4)^x 25 × 1.4 × 1.4 = ? Ari plotted a mug of cocoa cooling, and the calculator fit y = 50(0.8)^x. What does the model predict for x = 3?
Scatter plot on a dot grid. Across (x): minutes, from 0 to 7, a tick every 1. Up (y): degrees above room temperature, from 0 to 60, a tick every 10. Each dot is one reading. The dots, left to right: (0, 49.8), (1, 40.7), (2, 32.7), (3, 25.7), (4, 20.6), (5, 16.7), (6, 13.2). The calculator's fit, rounded: y = 50(0.8)^x. Fill in: 50 × 0.8 × 0.8 × 0.8 = ? Scatter plot on a dot grid. Across (x): minutes, from 0 to 7, a tick every 1. Up (y): degrees above room temperature, from 0 to 60, a tick every 10. Each dot is one reading. The dots, left to right: (0, 49.8), (1, 40.7), (2, 32.7), (3, 25.7), (4, 20.6), (5, 16.7), (6, 13.2). The calculator's fit, rounded: y = 50(0.8)^x. Fill in: 50 × 0.8 × 0.8 × 0.8 = ? Scatter plot 0 1 2 3 4 5 6 7 0 10 20 30 40 50 60 minutes degrees above room temperature calculator's fit, rounded: y = 50(0.8)^x Fill in: 50 × 0.8 × 0.8 × 0.8 = ? Bo plotted a water rocket's height, and the calculator fit y = -4.9x² + 28.4x + 1.2. What does the model predict for x = 3?
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, 1.2), (1, 24.7), (2, 38.3), (3, 42.4), (4, 36.2), (5, 20.7). The calculator's fit, rounded: y = -4.9x² + 28.4x + 1.2. 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, 1.2), (1, 24.7), (2, 38.3), (3, 42.4), (4, 36.2), (5, 20.7). The calculator's fit, rounded: y = -4.9x² + 28.4x + 1.2. 0 1 2 3 4 5 6 0 10 20 30 40 50 60 seconds after launch height in meters y = -4.9x² + 28.4x + 1.2 Each dot is one reading. Esme plotted a bike's stopping distance against its speed, and the calculator fit y = 0.4x² + 1.5x + 2. What does the model predict for x = 8?
Scatter plot on a dot grid. Across (x): speed in meters per second, from 2 to 14, a tick every 2. Up (y): stopping distance in meters, from 0 to 90, a tick every 10. Each dot is one test. The dots, left to right: (2, 6.6), (4, 14.4), (6, 25.6), (8, 39.2), (10, 57), (12, 77.3). The calculator's fit, rounded: y = 0.4x² + 1.5x + 2. Scatter plot on a dot grid. Across (x): speed in meters per second, from 2 to 14, a tick every 2. Up (y): stopping distance in meters, from 0 to 90, a tick every 10. Each dot is one test. The dots, left to right: (2, 6.6), (4, 14.4), (6, 25.6), (8, 39.2), (10, 57), (12, 77.3). The calculator's fit, rounded: y = 0.4x² + 1.5x + 2. Scatter plot 2 4 6 8 10 12 14 0 10 20 30 40 50 60 70 80 90 speed in meters per second stopping distance in meters y = 0.4x² + 1.5x + 2 Each dot is one test. The calculator's fit, rounded: y = 0.4x² + 1.5x + 2. Kira plotted visitors inside the science museum, and the calculator fit y = -3x² + 30x + 10. What does the model predict for x = 3?
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 100, a tick every 10. Each dot is one count. The dots, left to right: (0, 11), (1, 37), (2, 57), (3, 71), (4, 83), (5, 85), (6, 83), (7, 74), (8, 58), (9, 36). The calculator's fit, rounded: y = -3x² + 30x + 10. 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 100, a tick every 10. Each dot is one count. The dots, left to right: (0, 11), (1, 37), (2, 57), (3, 71), (4, 83), (5, 85), (6, 83), (7, 74), (8, 58), (9, 36). The calculator's fit, rounded: y = -3x² + 30x + 10. 0 1 2 3 4 5 6 7 8 9 10 0 10 20 30 40 50 60 70 80 90 100 hours after opening visitors inside y = -3x² + 30x + 10 Read this first
Use the table
x y 0 12 1 20 2 31 3 50 4 78 5 125 Differences: 8, 11, 19, 28, 47. Second differences: 3, 8, 9, 19. Ratios (each y over the one before): 1.67, 1.55, 1.61, 1.56, 1.6.
Luz recorded lily pads spreading on a park pond and worked out the differences and ratios. Which kind of model fits the data best?
- Exponential: the ratios stay about the same.
- Quadratic: the differences keep changing from row to row.
- Linear: the values keep going up.
Use the table
x y 0 15 1 52 2 79 3 98 4 109 5 115 Differences: 37, 27, 19, 11, 6. Second differences: -10, -8, -8, -5. Ratios (each y over the one before): 3.47, 1.52, 1.24, 1.11, 1.06.
Milo recorded visitors inside the science museum and worked out the differences and ratios. Which kind of model fits the data best?
- Quadratic: the second differences stay about the same.
- Exponential: the differences keep changing from row to row.
- Linear: the values keep changing.
Use the table
x y 0 30 1 44 2 66 3 99 4 151 5 226 Differences: 14, 22, 33, 52, 75. Second differences: 8, 11, 19, 23. Ratios (each y over the one before): 1.47, 1.5, 1.5, 1.53, 1.5.
Noah recorded algae growing in a fishbowl and worked out the differences and ratios. Which kind of model fits the data best?
- Quadratic: the differences keep changing as the table goes down.
- Exponential: the ratios stay about the same.
- Linear: the values keep going up.
Use the table
x y 2 5 4 13 6 25 8 42 10 63 12 89 Differences: 8, 12, 17, 21, 26. Second differences: 4, 5, 4, 5. Ratios (each y over the one before): 2.6, 1.92, 1.68, 1.5, 1.41.
Pia recorded a bike's stopping distance against its speed and worked out the differences and ratios. Which kind of model fits the data best?
- Exponential: the differences keep changing down the table.
- Quadratic: the second differences stay about the same.
- Linear: the values keep going up.