Reference no: EM132514736

Q1) Suppose that we have the least squares regression line Y = 3 + 2X and that the sum of the squares of the errors (SSE) for this line is 13,712. What can be said about the sum of the squares of the errors (SSE) for this data set and the line Y = 3 + 2.5X ?

Group of answer choices

A) The sum of the squares of the errors (SSE) for the line Y = 3 + 2.5X will be less than 13,712.

B) The sum of the squares of the errors (SSE) for the line Y = 3 + 2.5X will be greater than 13,712.

C) The sum of the squares of the errors (SSE) for the line Y = 3 + 2.5X will be 13,712.

Q2) We analyzed the relationship between the budgets (in millions of dollars) and the U.S. Box Office Sales (in millions of dollars) for 75 popular movies. The scatterplot showed a weak positive association with a linear form. Here are the StatCrunch linear regression results:

Simple linear regression results:

Dependent Variable: US_Box_Office

Independent Variable: Budget

US_Box_Office = 245.86629 + 0.60784721 Budget

Sample size: 74

R (correlation coefficient) = 0.40

R-sq = 0.16

Estimate of error standard deviation: 92.47

What does the number 92.47 tell us?

Group of answer choices

A) On average the U.S. Box Office Sales are within $92.47 million of the movie's budget.

B) 92.47% of the variation in U.S. Box Office Sales is explained by the movie's budget.

C) The average amount of error in the regression line predictions of U.S. Box Office Sales is $92.47 million.