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6.92 334.5 6.17 1049.9 4.88 485.1 4.49 289.6 3.98 551.8 3.74 288.9 3.25 295.2 3.15 500.1 2.76 611.0 2.64 1311.5 2.59 413.8 2.25 263.2 2.23 490.7 0.99 204.2 0.84 491.7 0.64 74.2 0.52 240.6 0.30 56.6 Compute the least-squares line for predicting y from x. Make a plot of residuals versus fitted values. a. b. Compute the least-squares line for predicting y from In x. Make a plot of residuals versus fitted values. Compute the least-squares line for predicting In y from In x. Make a plot of residuals versus fitted values. C. d. Which of the three models (a) through (c) fits best? Explain. Using the best model, predict the peak flow when the discharge is 50.0 km?lyr. e. f. Using the best model, find a 95% prediction interval for the peak flow when the discharge is 50.0 kmlyr. The article "Characteristics and Trends of River Discharge into Hudson, James, and Ungava Bays, 1964-2000" (S. Dery, M. Stieglitz, et al., Journal of Climate, 2005:2540-2557) presents measurements of discharge rate x (in kmlyr) andpeakflow y (in m/s) for 42 rivers that drain into the Hudson, James, and Ungava Bays. The data are shown in the following table: Discharge Peak Flow 94.24 4110.3 66.57 4961.7 59.79 10275.5 48.52 6616.9 40.00 7459.5 32.30 2784.4 31.20 3266.7 30.69 4368.7 26.65 1328.5 22.75 4437.6 21.20 1983.0 20.57 1320.1 19.77 1735.7 18.62 1944.1 17.96 3420.2 17.84 2655.3 16.06 3470.3 1561.6 14.69 11.63 869.8 11.19 936.8 11.08 1315.7 10.92 1727.1 9.94 768.1 7.86 483.3

6.92 334.5 6.17 1049.9 4.88 485.1 4.49 289.6 3.98 551.8 3.74 288.9 3.25 295.2 3.15 500.1 2.76 611.0 2.64 1311.5 2.59 413.8 2.25 263.2 2.23 490.7 0.99 204.2 0.84 491.7 0.64 74.2 0.52 240.6 0.30 56.6 Compute the least-squares line for predicting y from x. Make a plot of residuals versus fitted values. a. b. Compute the least-squares line for predicting y from In x. Make a plot of residuals versus fitted values. Compute the least-squares line for predicting In y from In x. Make a plot of residuals versus fitted values. C. d. Which of the three models (a) through (c) fits best? Explain. Using the best model, predict the peak flow when the discharge is 50.0 km?lyr. e. f. Using the best model, find a 95% prediction interval for the peak flow when the discharge is 50.0 kmlyr. The article "Characteristics and Trends of River Discharge into Hudson, James, and Ungava Bays, 1964-2000" (S. Dery, M. Stieglitz, et al., Journal of Climate, 2005:2540-2557) presents measurements of discharge rate x (in kmlyr) andpeakflow y (in m/s) for 42 rivers that drain into the Hudson, James, and Ungava Bays. The data are shown in the following table: Discharge Peak Flow 94.24 4110.3 66.57 4961.7 59.79 10275.5 48.52 6616.9 40.00 7459.5 32.30 2784.4 31.20 3266.7 30.69 4368.7 26.65 1328.5 22.75 4437.6 21.20 1983.0 20.57 1320.1 19.77 1735.7 18.62 1944.1 17.96 3420.2 17.84 2655.3 16.06 3470.3 1561.6 14.69 11.63 869.8 11.19 936.8 11.08 1315.7 10.92 1727.1 9.94 768.1 7.86 483.3

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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6.92 334.5 6.17 1049.9 4.88 485.1 4.49 289.6 3.98 551.8 3.74 288.9 3.25 295.2 3.15 500.1 2.76 611.0 2.64 1311.5 2.59 413.8 2.25 263.2 2.23 490.7 0.99 204.2 0.84 491.7 0.64 74.2 0.52 240.6 0.30 56.6 Compute the least-squares line for predicting y from x. Make a plot of residuals versus fitted values. a. b. Compute the least-squares line for predicting y from In x. Make a plot of residuals versus fitted values. Compute the least-squares line for predicting In y from In x. Make a plot of residuals versus fitted values. C. d. Which of the three models (a) through (c) fits best? Explain. Using the best model, predict the peak flow when the discharge is 50.0 km?lyr. e. f. Using the best model, find a 95% prediction interval for the peak flow when the discharge is 50.0 kmlyr.
Transcribed Image Text:6.92 334.5 6.17 1049.9 4.88 485.1 4.49 289.6 3.98 551.8 3.74 288.9 3.25 295.2 3.15 500.1 2.76 611.0 2.64 1311.5 2.59 413.8 2.25 263.2 2.23 490.7 0.99 204.2 0.84 491.7 0.64 74.2 0.52 240.6 0.30 56.6 Compute the least-squares line for predicting y from x. Make a plot of residuals versus fitted values. a. b. Compute the least-squares line for predicting y from In x. Make a plot of residuals versus fitted values. Compute the least-squares line for predicting In y from In x. Make a plot of residuals versus fitted values. C. d. Which of the three models (a) through (c) fits best? Explain. Using the best model, predict the peak flow when the discharge is 50.0 km?lyr. e. f. Using the best model, find a 95% prediction interval for the peak flow when the discharge is 50.0 kmlyr.
The article "Characteristics and Trends of River Discharge into Hudson, James, and Ungava Bays, 1964-2000" (S. Dery, M. Stieglitz, et al., Journal of Climate, 2005:2540-2557) presents measurements of discharge rate x (in kmlyr) andpeakflow y (in m/s) for 42 rivers that drain into the Hudson, James, and Ungava Bays. The data are shown in the following table: Discharge Peak Flow 94.24 4110.3 66.57 4961.7 59.79 10275.5 48.52 6616.9 40.00 7459.5 32.30 2784.4 31.20 3266.7 30.69 4368.7 26.65 1328.5 22.75 4437.6 21.20 1983.0 20.57 1320.1 19.77 1735.7 18.62 1944.1 17.96 3420.2 17.84 2655.3 16.06 3470.3 1561.6 14.69 11.63 869.8 11.19 936.8 11.08 1315.7 10.92 1727.1 9.94 768.1 7.86 483.3
Transcribed Image Text:The article "Characteristics and Trends of River Discharge into Hudson, James, and Ungava Bays, 1964-2000" (S. Dery, M. Stieglitz, et al., Journal of Climate, 2005:2540-2557) presents measurements of discharge rate x (in kmlyr) andpeakflow y (in m/s) for 42 rivers that drain into the Hudson, James, and Ungava Bays. The data are shown in the following table: Discharge Peak Flow 94.24 4110.3 66.57 4961.7 59.79 10275.5 48.52 6616.9 40.00 7459.5 32.30 2784.4 31.20 3266.7 30.69 4368.7 26.65 1328.5 22.75 4437.6 21.20 1983.0 20.57 1320.1 19.77 1735.7 18.62 1944.1 17.96 3420.2 17.84 2655.3 16.06 3470.3 1561.6 14.69 11.63 869.8 11.19 936.8 11.08 1315.7 10.92 1727.1 9.94 768.1 7.86 483.3
Expert Solution
Step 1

For estimating the regression equation using least squares, we will use R to find for the regression equation:- 

The command which is used is lm(y~x). 

Statistics homework question answer, step 1, image 1

Y = 457.27  + 87.56 X 

Statistics homework question answer, step 1, image 2

 

Step 2

B) It is also solved in R using the same command in (A). 

Statistics homework question answer, step 2, image 1

The regression equation will be 

Y = 1165.3 log(X) -504

Statistics homework question answer, step 2, image 2

 

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