Deck 9: Multiple Regression

Question 1

The plot below shows the Residuals vs Fitted Values for the model of predictors Height, Weight, and Chest measurements (all in inches) and response percent body fat (Pct.BF)
Before observation 251 was removed.It was assumed that a typo was made and an extra zero
Was added to the Pct.BF value.Which of the following statements are true?

Answer 1

A,B,C,E

Question 2

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales.The plot of residuals versus predicted
Values is shown below.What does the residual plot suggest?

A)The Linearity condition is not satisfied.
B)There is an extreme departure from normality.
C)The variance is not constant.
D)The presence of a couple of outliers.
E)The plot thickens from left to right.

Answer 2

D

Question 3

Selling price and percent of advertising budget spent were entered into a multiple regression to determine what affects flat panel LCD TV sales.Using the output below, estimate the
Number of units sold on average at a store that sells the Sony Bravia for $2199 and spends
10% of its advertising budget on the product.

Answer 3

A

Question 4

Selling price and percent of advertising budget spent were entered into a multiple regression to determine what affects flat panel LCD TV sales.Use the output shown below, determine
The amount of variability in Sales is explained by the estimated multiple regression model.

Question 5

Which of the following statements about partial regression plots is NOT true?

A)They have a slope equal to the coefficient value.
B)They can help you judge whether a nearly normal assumption of errors is met.
C)They can help you identify outliers and influential points.
D)They have a direction that corresponds to the sign of its multiple regression coefficient.
E)They can help you judge whether the straight enough assumption has been met for each predictor.

Question 6

A multiple regression model was used to predict the percent body fat (Pct.BF) from Predictors (all in inches): Height, Waist, and Chest.Which of the following statements is
NOT true.

A)By the rule of thumb, the model could make errors as much as 8.8% of body fat.
B)The model explains 72.2% of the variation in the percent of body fat.
C)There are 250 observations in this data set.
D)The coefficient of Waist indicates that body fat increases about 2.2 % for every additional inch of Waist measured.
E)All the above are true.

Question 7

In regression an observation has high leverage when

A)the observation has a combination of x-values that is far from the center of the data.
B)the observation is perfectly predicted by the regression.
C)the observation is poorly predicted by the regression.
D)removing the observation causes a large change in one of more coefficients of the model.
E)none of these

Question 8

A sample of 33 companies was randomly selected and data collected on the average annual bonus, turnover rate (%), and trust index (measured on a scale of 0 - 100).According to the
Output is shown below, what is the estimated multiple regression model?

Question 9

Selling price and percent of advertising budget spent were entered into a multiple regression to determine what affects flat panel LCD TV sales.The regression coefficient for Price was
Found to be -0.03055, which of the following is the correct interpretation for this value?

A)Increasing the price of the Sony Bravia by $100 will result in at least 3 fewer TV's sold.
B)For a given percent of advertising budget spent, a $100 increase in price of the Sony Bravia is associated with a decrease in sales of 3.055 units, on average.
C)After allowing for the percent of advertising budget spent on advertising, an increase of $100 in the price of the Sony Bravia will decrease sales by 3.055 units.
D)Holding the percent of advertising budget spent constant, an increase of $100 in the price of the Sony Bravia will decrease sales by .03%.
E)None of the above.

Question 10

Using the output below, calculate the predicted turnover rate for a company having a trust index score of 70 and an average annual bonus of $6500.

Question 11

Partial regression plots are useful for which of the following reasons?

A)They have a slope equal to the coefficient value.
B)They can help you judge whether a re-expression of a predictor is needed.
C)They can help you identify outliers and influential points.
D)They have a direction that corresponds to the sign of its multiple regression coefficient.
E)All of the above.

Question 12

Question 13

Which of the following are NOT assumptions of a multiple regression model?

A)R-squared should be above 90%
B)The relationship between the response and the predictors is linear.
C)The residuals are approximately normally distributed
D)The variability of the errors is about the same for all values of each predictor.
E)All of these are characteristics of a good regression model.

Question 14

A sample of 33 companies was randomly selected and data collected on the average annual bonus, turnover rate (%), and trust index (measured on a scale of 0 - 100).Based on the
Output, how much of the variability in Turnover Rate is explained by the estimated multiple
Regression model?

Question 15

Using the output below, which is the correct interpretation of the coefficient of Average Bonus?

A)For every additional $10,000 dollars in Average Bonus, the predicted Turnover Rate will go down about 7.2%.
B)For every additional $10,000 dollars in Average Bonus, the predicted Turnover Rate will go up about 7.2%.
C)For every additional $10,000 dollars in Average Bonus, and taking account of the other predictor (Trust Index), the predicted Turnover Rate will go down about 7.2%.
D)For every additional $10,000 dollars in Average Bonus, and taking account of the other predictor (Trust Index), the predicted Turnover Rate will go up about 7.2%.
E)None of the above.

Question 16

Selling price and percent of advertising budget spent were entered into a multiple regression to determine what affects flat panel LCD TV sales.Using the output shown below, which of
The following statements is NOT true?