Question 1

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. The regression coefficient for Advertising was found to be +3.0926, which of the following is the correct Interpretation for this value?

Accepted Answer

The coefficient for Advertising indicates the effect of a change in advertising spending on sales, while controlling for the effect of price. Specifically, option D correctly interprets the coefficient as indicating that a one percent increase in advertising spending is associated with an average increase in sales of 3.0926 units, controlling for price. Option A incorrectly states that price has a negative effect on sales, which is not indicated by the information given. Option B incorrectly suggests that the interpretation applies only to changes within a given quarter, which is not supported by the information given. Option C includes the incorrect phrase "over the previous quarter," which is not relevant to the interpretation of the coefficient.

Question 2

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).
Using the output below, and a significance level of α = .01, we can conclude that Dependent Variable is Turnover Rate
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \ \text { Constant } & 12.1005 & 0.7826 & 15.46 & 0.000 \ \text { Trust Index } & - 0.07149 & 0.01966 & - 3.64 & 0.001 \ \text { Average Bonus } & - 0.0007216 & 0.0001481 & - 4.87 & 0.000 \end{array}$
$$S = 1.49746 \quad \mathrm { R } - \mathrm { Sq } = 79.6 \% \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 78.3 \%$$
Analysis of Variance
$\begin{array} { l r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } \ \text { Regression } & 2 & 262.73 & 131.36 \ \text { Residual Error } & 30 & 67.27 & 2.24 \ \text { Total } & 32 & 330.00 & \end{array}$

Accepted Answer

The answer of A sample of 33 companies was randomly...

Question 3

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. The adjusted R2 value was reported
As 83.3%. This means that

Accepted Answer

The adjusted R2 value being reported as 83.3% means that this percentage of the variance in TV sales can be accounted for by the model including both selling price and amount spent advertising. It does not mean that one predictor is more important than the other or that they are both significant predictors, but rather that the model as a whole is a good fit for the data. Therefore, option D is the best choice.

Question 4

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. $$\begin{array}{l}
\text { Dependent Variable is Turnover Rate }\
\begin{array} { l r r r r } 
\text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \
\text { Constant } & 12.1005 & 0.7826 & 15.46 & 0.000 \
\text { Trust Index } & - 0.07149 & 0.01966 & - 3.64 & 0.001 \
\text { Average Bonus } & - 0.0007216 & 0.0001481 & - 4.87 & 0.000
\end{array}
\end{array}$$

Accepted Answer

The predicted turnover rate is calculated using the regression equation: Turnover Rate = 12.1005 - 0.07149*(Trust Index) - 0.0007216*(Average Bonus). Substituting the given values: Turnover Rate = 12.1005 - 0.07149*(70) - 0.0007216*(6500) = 2.4%.

Question 5

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?

Accepted Answer

The answer of Selling price and amount spent advertising were...

Question 6

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. According to the output below, the
Calculated t-statistic to determine if amount spent on advertising is a significant
Independent variable in explaining Sony Bravia sales is $$\begin{array}{l}
\text { Dependent Variable is sales }\
\begin{array} { l r r r r } 
\text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \
\text { Constant } & 90.19 & 25.08 & 3.60 & 0.001 \
\text { Price } & - 0.03055 & 0.01005 & - 3.04 & 0.005 \
\text { Advertising } & 3.0926 & 0.3680 & 8.40 & 0.000
\end{array}
\end{array}$$

Accepted Answer

The calculated t-statistic for the amount spent on advertising is 8.40, which has a significant level of 0.000. This means that the amount spent on advertising is a significant independent variable in explaining Sony Bravia sales. Therefore, choice C is the correct answer.

Question 7

Selling price and amount spent advertising 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. $$\begin{array}{l}
\text { Dependent Variable is Sales }\
\begin{array} { l r r r r } 
\text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \
\text { Constant } & 90.19 & 25.08 & 3.60 & 0.001 \
\text { Price } & - 0.03055 & 0.01005 & - 3.04 & 0.005 \
\text { Advertising } & 3.0926 & 0.3680 & 8.40 & 0.000
\end{array}
\end{array}$$

Accepted Answer

The equation for the regression is:Sales = 90.19 - 0.03055 * Price + 3.0926 * AdvertisingPlugging in the values for Price and Advertising, we get:Sales = 90.19 - 0.03055 * 2199 + 3.0926 * (0.10 * 2199)Sales = 90.19 - 67.47 + 68.91Sales = 91.63Therefore, 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 is 91.63 units.

Question 8

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. The correct null and alternative Hypotheses for testing the regression coefficient of Price is

Accepted Answer

The null hypothesis should state that the regression coefficient of the price variable is equal to zero, meaning that price does not have any significant effect on flat panel LCD TV sales. The alternative hypothesis should state that the regression coefficient is not equal to zero, meaning that price has a significant effect on sales. Choice D correctly represents these hypotheses.

Question 9

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. Using the output below, calculated
F statistic to determine the overall significance of the estimated multiple regression
Model is $$\begin{array}{l}
\text { Analysis of Variance }\
\begin{array} { l r r r } 
\text { Source } & \text { DF } & \text { SS } & \text { MS } \
\text { Regression } & 2 & 16477.3 & 8238.7 \
\text { Residual Error } & 27 & 3038.0 & 112.5 \
\text { Total } & 29 & 19515.4 &
\end{array}
\end{array}$$

Accepted Answer

The answer of Selling price and amount spent advertising were...

Question 10

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. If the scatterplots of sales versus
Both selling price and amount spent on advertising are created, they are being used to
Determine whether

Accepted Answer

The scatterplots of sales versus both selling price and amount spent on advertising are used to check the linearity condition in multiple regression analysis. This condition assesses whether the relationship between the predictors (selling price and advertising spend) and the response variable (sales) can be appropriately modeled using a linear equation.

Question 11

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. Use the output shown below,
Calculate the amount of variability in Sales is explained by the estimated multiple
Regression model. $$\begin{array}{l}
\text { Analysis of Variance }\
\begin{array} { l r r r } 
\text { Source } & \text { DF } & \text { SS } & \text { MS } \
\text { Regression } & 2 & 16477.3 & 8238.7 \
\text { Residual Error } & 27 & 3038.0 & 112.5 \
\text { Total } & 29 & 19515.4 &
\end{array}
\end{array}$$

Accepted Answer

The amount of variability in sales explained by the model can be found by calculating the R-squared value, which measures the proportion of variance in the dependent variable (sales) that is explained by the independent variables (selling price and amount spent advertising) in the model. This value can be calculated by dividing the SS regression by the total SS, as follows:
R-squared = SS regression / SS Total = 16477.3 / 19515.4 = 0.8443 or 84.43%
Therefore, the answer is C.

Question 12

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).
The regression coefficient for the variable Trust Index is -0.07149. The correct Interpretation of this value is

Accepted Answer

The regression coefficient of -0.07149 for the Trust Index means that for each unit increase in the Trust Index, the turnover rate decreases by 0.07149%. Therefore, an increase of 10 points in the Trust Index would be associated with a decrease of 0.7149% (or approximately 0.71%) in the turnover rate, holding other factors constant.

Question 13

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. Based on the output below, which
Of the following statements is/are true? Dependent Variable is Sales
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & \text { T } & \text { P } \ \text { Constant } & 90.19 & 25.08 & 3.60 & 0.001 \ \text { Price } & - 0.03055 & 0.01005 & - 3.04 & 0.005 \ \text { Advertising } & 3.0926 & 0.3680 & 8.40 & 0.000 \end{array}$
$$S = 10.6075 \quad R - S q = 84.4 \frac { \circ } { 8 } \quad \mathrm { Sq } ( \mathrm { adj } ) = 83.3 \text { 웋 }$$
Analysis of Variance
$\begin{array} { l r r r } \text { Source } & \text { DF } & \text { SS } & \text { MS } \ \text { Regression } & 2 & 16477.3 & 8238.7 \ \text { Residual Error } & 27 & 3038.0 & 112.5 \ \text { Total } & 29 & 19515.4 & \end{array}$

Accepted Answer

A) The p-value for the F-test is less than 0.05, indicating that the multiple regression model is significant overall.
B) The p-value for the coefficient of Selling Price is less than 0.05, indicating that Selling Price is a significant independent variable in explaining Bravia sales.
C) The p-value for the coefficient of Amount Spent on Advertising is less than 0.05, indicating that Amount Spent on Advertising is a significant independent variable in explaining Bravia sales.

Question 14

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. A multiple regression model was fit
To the data and the graph of residuals shows a unimodal and symmetric pattern. What
Does this graph suggest?

Accepted Answer

The answer of Selling price and amount spent advertising were...

Question 15

Selling price and amount spent advertising 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?

Accepted Answer

The negative coefficient for Price (-0.03055) indicates that there is a negative relationship between the price of the TV and the sales, holding the amount spent on advertising constant. Specifically, for every $100 increase in the price of the Sony Bravia, sales will decrease by 3.055 units. Therefore, the correct interpretation is option C.

Question 16

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).
The regression coefficient associated with Average Bonus was found to be -0.0007216. The correct interpretation of this value is

Accepted Answer

The regression coefficient associated with Average Bonus (-0.0007216) indicates that there is a negative relationship between average annual bonus and turnover rate, while holding the trust index constant. Therefore, increasing the average annual bonus by $100 for companies with the same trust index score is associated with an average decrease in the turnover rate by 7.2%.

Quiz 15: Multiple Regression

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. The regression coefficient for Advertising was found to be +3.0926, which of the following is the correct Interpretation for this value?

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. The adjusted R2 value was reported As 83.3%. This means that

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?

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. The correct null and alternative Hypotheses for testing the regression coefficient of Price is

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. If the scatterplots of sales versus Both selling price and amount spent on advertising are created, they are being used to Determine whether

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) . The regression coefficient for the variable Trust Index is -0.07149. The correct Interpretation of this value is

Selling price and amount spent advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. A multiple regression model was fit To the data and the graph of residuals shows a unimodal and symmetric pattern. What Does this graph suggest?

Selling price and amount spent advertising 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 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) . The regression coefficient associated with Average Bonus was found to be -0.0007216. The correct interpretation of this value is