Exam 15: Multiple Regression

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. Analysis of Variance Source DF SS MS Regression 2 16477.3 8238.7 Residual Error 27 3038.0 112.5 Total 29 19515.4

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 correct null and alternative Hypotheses for testing the regression coefficient of Price is

Use the following information for problems To determine what affects turnover rate, a sample of 33 companies was randomly selected and data collected on the average annual bonus and turnover rate (%). In addition, a questionnaire was administered to the employees of each company to arrive at a trust index (measured on a scale of 0 - 100). Below are the multiple regression results. Dependent Variable is Turnover Rate Predictor Coef SE Coef T P Constant 12.1005 0.7826 15.46 0.000 Trust Index -0.07149 0.01966 -3.64 0.001 Average Bonus -0.0007216 0.0001481 -4.87 0.000 $S = 1.49746 \quad \mathrm { R } - \mathrm { Sq } = 79.68 \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 78.3 \frac { 8 } { 8 }$ Analysis of Variance Source DF SS MS Regression 2 262.73 131.36 Residual Error 30 67.27 2.24 Total 32 330.00 -State the hypotheses for testing the regression coefficient of Trust Index. Based on the results, what do you conclude?

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). In a Multiple regression estimating turnover rate using average bonus and trust index, What is the correct null hypotheses for testing the regression coefficient of Trust Index?

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 Analysis of Variance Source DF SS MS Regression 2 16477.3 8238.7 Residual Error 27 3038.0 112.5 Total 29 19515.4

Use the following information for problems To determine what affects turnover rate, a sample of 33 companies was randomly selected and data collected on the average annual bonus and turnover rate (%). In addition, a questionnaire was administered to the employees of each company to arrive at a trust index (measured on a scale of 0 - 100). Below are the multiple regression results. Dependent Variable is Turnover Rate Predictor Coef SE Coef T P Constant 12.1005 0.7826 15.46 0.000 Trust Index -0.07149 0.01966 -3.64 0.001 Average Bonus -0.0007216 0.0001481 -4.87 0.000 $S = 1.49746 \quad \mathrm { R } - \mathrm { Sq } = 79.68 \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 78.3 \frac { 8 } { 8 }$ Analysis of Variance Source DF SS MS Regression 2 262.73 131.36 Residual Error 30 67.27 2.24 Total 32 330.00 -Write the null and alternative hypotheses for the F-test in this multiple regression model.

Use the following information for problems Sales figures (number of units), selling price and amount spent on advertising (as a percentage of total advertising expenditure in the previous quarter) for the popular Sony Bravia Television were obtained for last quarter from a sample of 30 different stores. The results of a multiple regression are presented below. Dependent Variable $1 \mathrm {~s}$ Sales Predictor Coef sE Coef T P Constant 90.19 25.08 3.60 0.001 Price =0.03055 0.01005 -3.04 0.005 Advertising 3.0926 0.3680 8.40 0.000 $S = 10.6075 \quad R - S q = 84.4 \leqslant \quad R - S q ( a d j ) = 83.3 \div \frac { 7 } { 6 }$ Analyaia of Variance Source DF SS MS Regresgion 2 16477.3 8238.7 Residual Error 27 3038.0 112.5 Total 29 19515.4 -Use the scatterplots provided below to check assumptions for multiple regression. For each plot, list the assumption being checked, whether or not it is satisfied, and why.

Use the following information for problems To determine what affects turnover rate, a sample of 33 companies was randomly selected and data collected on the average annual bonus and turnover rate (%). In addition, a questionnaire was administered to the employees of each company to arrive at a trust index (measured on a scale of 0 - 100). Below are the multiple regression results. Dependent Variable is Turnover Rate Predictor Coef SE Coef T P Constant 12.1005 0.7826 15.46 0.000 Trust Index -0.07149 0.01966 -3.64 0.001 Average Bonus -0.0007216 0.0001481 -4.87 0.000 $S = 1.49746 \quad \mathrm { R } - \mathrm { Sq } = 79.68 \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 78.3 \frac { 8 } { 8 }$ Analysis of Variance Source DF SS MS Regression 2 262.73 131.36 Residual Error 30 67.27 2.24 Total 32 330.00 -How much of the variability in Turnover Rate is explained by the regression equation?

Use the following information for problems To determine what affects turnover rate, a sample of 33 companies was randomly selected and data collected on the average annual bonus and turnover rate (%). In addition, a questionnaire was administered to the employees of each company to arrive at a trust index (measured on a scale of 0 - 100). Below are the multiple regression results. Dependent Variable is Turnover Rate Predictor Coef SE Coef T P Constant 12.1005 0.7826 15.46 0.000 Trust Index -0.07149 0.01966 -3.64 0.001 Average Bonus -0.0007216 0.0001481 -4.87 0.000 $S = 1.49746 \quad \mathrm { R } - \mathrm { Sq } = 79.68 \quad \mathrm { R } - \mathrm { Sq } ( \mathrm { adj } ) = 78.3 \frac { 8 } { 8 }$ Analysis of Variance Source DF SS MS Regression 2 262.73 131.36 Residual Error 30 67.27 2.24 Total 32 330.00 -Use the plots provided to check whether conditions for multiple regression are satisfied. For each plot, list the condition being checked, whether or not it is satisfied, and why.

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. Dependent Variable is Turnover Rate Predictor Coef SE Coef T P Constant 12.1005 0.7826 15.46 0.000 Trust Index -0.07149 0.01966 -3.64 0.001 Average Bonus -0.0007216 0.0001481 -4.87 0.000

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

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

Use the following information for problems Sales figures (number of units), selling price and amount spent on advertising (as a percentage of total advertising expenditure in the previous quarter) for the popular Sony Bravia Television were obtained for last quarter from a sample of 30 different stores. The results of a multiple regression are presented below. Dependent Variable $1 \mathrm {~s}$ Sales Predictor Coef sE Coef T P Constant 90.19 25.08 3.60 0.001 Price =0.03055 0.01005 -3.04 0.005 Advertising 3.0926 0.3680 8.40 0.000 $S = 10.6075 \quad R - S q = 84.4 \leqslant \quad R - S q ( a d j ) = 83.3 \div \frac { 7 } { 6 }$ Analyaia of Variance Source DF SS MS Regresgion 2 16477.3 8238.7 Residual Error 27 3038.0 112.5 Total 29 19515.4 -How much of the variability in Sales is explained by the regression equation?

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. Based on the output below, which Of the following statements is/are true? Dependent Variable is Sales Predictor Coef SE Coef T P Constant 90.19 25.08 3.60 0.001 Price -0.03055 0.01005 -3.04 0.005 Advertising 3.0926 0.3680 8.40 0.000 $S = 10.6075 \quad R - S q = 84.4 \frac { \circ } { 8 } \quad \mathrm { Sq } ( \mathrm { adj } ) = 83.3 \text { 웋 }$ Analysis of Variance Source DF SS MS Regression 2 16477.3 8238.7 Residual Error 27 3038.0 112.5 Total 29 19515.4

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 a multiple regression model was performed using average annual bonus and trust index (scale of 0 - 100) to Explain turnover rate. According to the output below, what is the F statistic to Determine the overall significance of the estimated is Analysis of Variance Source DF SS MS Regression 2 262.73 131.36 Residual Error 30 67.27 2.24 Total 32 330.00

Use the following information for problems Sales figures (number of units), selling price and amount spent on advertising (as a percentage of total advertising expenditure in the previous quarter) for the popular Sony Bravia Television were obtained for last quarter from a sample of 30 different stores. The results of a multiple regression are presented below. Dependent Variable $1 \mathrm {~s}$ Sales Predictor Coef sE Coef T P Constant 90.19 25.08 3.60 0.001 Price =0.03055 0.01005 -3.04 0.005 Advertising 3.0926 0.3680 8.40 0.000 $S = 10.6075 \quad R - S q = 84.4 \leqslant \quad R - S q ( a d j ) = 83.3 \div \frac { 7 } { 6 }$ Analyaia of Variance Source DF SS MS Regresgion 2 16477.3 8238.7 Residual Error 27 3038.0 112.5 Total 29 19515.4 -Use the F-test to determine whether the slope coefficients are significantly different from 0. Write the null and alternative hypotheses and calculate the F-statistic.

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 Dependent Variable is sales Predictor Coef SE Coef T P Constant 90.19 25.08 3.60 0.001 Price -0.03055 0.01005 -3.04 0.005 Advertising 3.0926 0.3680 8.40 0.000