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*Give the value of the correlation coefficient and interpret its meaning. Compute the least-squares regression line for predicting calories (y) from protein (x). Interpret the value of the slope. Predict the number of calories in a fast-food product containing 18 grams of protein. Round to the nearest whole number. What is the 95% confidence interval for the slope.

*Give the value of the correlation coefficient and interpret its meaning. Compute the least-squares regression line for predicting calories (y) from protein (x). Interpret the value of the slope. Predict the number of calories in a fast-food product containing 18 grams of protein. Round to the nearest whole number. What is the 95% confidence interval for the slope.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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*Give the value of the correlation coefficient and interpret its meaning.

  • Compute the least-squares regression line for predicting calories (y) from protein (x).
  • Interpret the value of the slope.
  • Predict the number of calories in a fast-food product containing 18 grams of protein. Round to the nearest whole number.
  • What is the 95% confidence interval for the slope.
  • Construct a 99% confidence interval for the slope.
  1. Test ? : ? = 0 versus ? : ? ≠ 0. Can you conclude that the amount of protein is useful in predicting the 01 11

number of calories? Use the ? = 0.05 level of significance.

  • Is this a left-tailed test, a right-tailed test, or a two-tailed test?
  • What is the value of the test statistic?
  • Identify the p-value for this procedure. Do not round.
  • Using the p-value method, should ?0 be rejected at the ? = 0.05 level of significance? Why or why not?
  • Can we conclude that the amount of protein in fast-food products is useful in predicting the number of calories?

 

Bb https://learn-us-east-1-prod-fleet02-xythos.content.blackboardcdn.com/5fca... Regression Statistics learn-us-east-1-prod-fleet02-xythos.content.blackboardcdn.com Regression Analysis with Excel (Section 11.3) Calories and protein: The following table presents the number of grams of protein and the number of calories per 100 grams for each of 18 fast-food products. Protein Calories Protein Calories 17.25 289 5.62 367 13.73 376 3.58 188 12.96 315 11.97 275 21.24 268 5.84 408 22.54 221 12.22 460 16.23 309 8.05 189 10.64 226 5.15 366 344 15.93 334 6.16 3.69 163 16.52 241 Source: United States Department of Agriculture The following output was obtained after performing regression analysis in Excel: Multiple R R Square Adjusted R Square Standard Error Observations Regression output variables Intercept Protein Untitled document - Google Docs 0.0365 0.0013 -0.0611 84.9721 18 coefficients std. error 302.5212 45.1484 -0.5082 3.4795 b Home | bartleby confidence interval t (df=16) p-value 95% lower 95% upper 6.701 5.10E-06 206.8108 398.2315 .8857 -7.8845 6.8680 -0.146 ← +
Transcribed Image Text:Bb https://learn-us-east-1-prod-fleet02-xythos.content.blackboardcdn.com/5fca... Regression Statistics learn-us-east-1-prod-fleet02-xythos.content.blackboardcdn.com Regression Analysis with Excel (Section 11.3) Calories and protein: The following table presents the number of grams of protein and the number of calories per 100 grams for each of 18 fast-food products. Protein Calories Protein Calories 17.25 289 5.62 367 13.73 376 3.58 188 12.96 315 11.97 275 21.24 268 5.84 408 22.54 221 12.22 460 16.23 309 8.05 189 10.64 226 5.15 366 344 15.93 334 6.16 3.69 163 16.52 241 Source: United States Department of Agriculture The following output was obtained after performing regression analysis in Excel: Multiple R R Square Adjusted R Square Standard Error Observations Regression output variables Intercept Protein Untitled document - Google Docs 0.0365 0.0013 -0.0611 84.9721 18 coefficients std. error 302.5212 45.1484 -0.5082 3.4795 b Home | bartleby confidence interval t (df=16) p-value 95% lower 95% upper 6.701 5.10E-06 206.8108 398.2315 .8857 -7.8845 6.8680 -0.146 ← +
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