Working with the Concepts19. Calorles and protein: The following table presents the numberof grams of protein and the number of calories per 100 gramsfor each of 18 fast-food products.ProteinCaloriesProteinCalories5.6236717.2513.732893763153.5818812.9621.2422.5416.2310.646.163.6911.972752682213092265.8440812.224608.051895.1536634416315.9316.52334241Source: United Stutes Department of Agriculturea. Compute the least-squares regression line for predictingcalories (y) frem protein (x).b. Construct a 95% confidence interval for theslope.c. Test H: p, = 0 versus H: 6+ 0. Can you conclude thatthe amount of protein is useful in predicting the number ofcalories? Use the a = 0.05 level of significance.20. Like father lil

19. a. The regression analysis is conducted using EXCEL. The software procedure is given below: Enter the data. Select Data > Data Analysis >Regression> OK. Enter Input Y Range as y. Enter Input X Range as x. Mark Labels. Click OK. The output using EXCEL is as follows: From the output, the intercept is 302.5212, and slope is -0.5082. The least-squares regression line for predicting calories (y) from protein (x) is, y^=a+bx=302.5212-0.5082x Thus, the least-squares regression line for predicting calories (y) from protein (x) is y^=302.5212-0.5082x.b. From the output, the 95% confidence interval for the slope is -7.8845,6.8680. Thus, the 95% confidence interval for the slope is (-7.8845,6.8680). c. From the output, the test statistic is -0.1461, and P-value is 0.8857. Decision rule: If P-value≤α, then reject the null hypothesis. Otherwise, do not reject the null hypothesis. Conclusion: The P-value (0.8857) is greater than the level of significance (0.05). Based on decision rule, fail to reject the null hypothesis. Thus, it cannot be conclude that the amount of protein is useful in predicting the number of calories.

Math

Statistics

a. Compute the least-squares regression line for predicting calories (y) frem protein (x). b. Construct a 95% confidence interval for the slope. C. Test H A=0 versus H P+ 0. Can you conclude that the amount of protein is useful in predicting the number of calories? Use the a = 0.05 level of significance.

a. Compute the least-squares regression line for predicting calories (y) frem protein (x). b. Construct a 95% confidence interval for the slope. C. Test H A=0 versus H P+ 0. Can you conclude that the amount of protein is useful in predicting the number of calories? Use the a = 0.05 level of significance.

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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