M9-1

Dependent Variable
Independent Variable
Observations
Intercept coefficient
Independent variable coefficient
Regression Model
R-square
Adjusted R-square
Intercept P-value
Independent variable P-value
Your Interpretations and Analyses

M9-2

Dependent Variable
Independent Variable 1
Independent Variable 2
Independent Variable 3
Observations
Intercept coefficient
Coefficient of Independent variable 1
Coefficient of Independent variable 2
Coefficient of Independent variable 3
Regression Model
R-square
Adjusted R-square
Intercept P-value
P-value of Independent variable 1
P-value of Independent variable 2
P-value of Independent variable 3
Your Interpretations and Analyses

M9-3

Dependent Variable
Independent Variable 1
Independent Variable 2
Independent Variable 3
Observations
Intercept coefficient
Coefficient of Independent variable 1
Coefficient of Independent variable 2
Coefficient of Independent variable 3
Regression Model
R-square
Adjusted R-square
Intercept P-value
P-value of Independent variable 1
P-value of Independent variable 2
P-value of Independent variable 3
Your Interpretations and Analyses

Reynolds Inc. is a manufacturer of industrial scales and laboratory equipment. Managers At Reynolds want to investigate the relationship between length of employment of their salespeople and the number of electronic laboratory scales sold. There are 15 salespeople who were randomly selected for this investigation. \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} \hlineA & A & B & C & D & E & F & G & H & I \\ \hline 1 & SUMMARY OUTPUT & & & & & & & & \\ \hline \multicolumn{10}{|l|}{2} \\ \hline 3 & \multicolumn{2}{|c|}{ Regression Statistics } & & & & & & & \\ \hline 4 & Multiple R & 0.888897515 & & & & & & & \\ \hline 5 & R Square & 0.790138792 & & & & & & & \\ \hline 6 & Adjusted R Square & 0.773995622 & & & & & & & \\ \hline 7 & Standard Error & 48.49087146 & & & & & & & \\ \hline 8 & Observations & 15 & & & & & & & \\ \hline \multicolumn{10}{|l|}{9} \\ \hline 10 & ANOVA & & & & & & & & \\ \hline 11 & & df & SS & MS & F & Significance F & & & \\ \hline 12 & Regression & 1 & 115089.1933 & 115089.1933 & 48.94570268 & 9.39543E06 & & & \\ \hline 13 & Residual & 13 & 30567.74 & 2351.364615 & & & & & \\ \hline 14 & Total & 14 & 145656.9333 & & & & & & \\ \hline \multicolumn{10}{|l|}{15} \\ \hline 16 & & Coefficients & Standard Error & t Stat & P-value & Lower 95% & \begin{tabular}{|c|} Upper 95% \\ \end{tabular} & Lower 95.0% & Upper 95.0% \\ \hline 17 & Intercept & 113.7452874 & 20.81345608 & 5.464987985 & 0.000108415 & 68.78054927 & \begin{tabular}{|l|} 158.7100256 \\ \end{tabular} & \begin{tabular}{|l|} 68.78054927 \\ \end{tabular} & 158.7100256 \\ \hline 18 & Months Employed & 2.367463621 & 0.338396631 & 6.996120545 & 9.39543E-06 & 1.636402146 & 3.098525095 & 1.636402146 & 3.098525095 \\ \hline \end{tabular} - Tyler Personal Care conducted a regression study for one of its new shampoo products. The two factors believed to have the most influence on sales are unit selling price ad advertising expenditure. - To investigate the effects of these two variables on sales, prices of $2.00,$2.50, and $3.00 were paired with advertising expenditures of $50,000 and $100,000 in 24 test markets. 545 M9 CASES for Assignments-1.p Q file:///C:/Users/Downloads/545 M9 CASES for Assignments-1.pdf Automatic Zoom Fig D Excel Output for the Tyler Personal Care Linear Regression Model with Interaction \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} \hline & A & B & C & D & E & F & G & H & I \\ \hline 1 & SUMMARY OUTPUT & & & & & & & & \\ \hline \multicolumn{10}{|c|}{2} \\ \hline 3 & \multicolumn{2}{|c|}{ Regression Statistics } & & & & & & & \\ \hline 4 & Multiple R & 0.988993815 & & & & & & & \\ \hline 5 & R Square & 0.978108766 & & & & & & & \\ \hline 6 & Adjusted R Square & \begin{tabular}{|l|} 0.974825081 \\ \end{tabular} & & & & & & & \\ \hline 7 & Standard Error & \begin{tabular}{|l|} 28.17386496 \\ \end{tabular} & & & & & & & \\ \hline 8 & Observations & 24 & & & & & & & \\ \hline \multicolumn{10}{|c|}{9} \\ \hline 10 & ANOVA & & & & & & & & \\ \hline 11 & & df & SS & MS & F & Significance F & & & \\ \hline 12 & Regression & 3 & 709316 & 236438.6667 & 297.8692 & 9.25881E17 & & & \\ \hline 13 & Residual & 20 & 15875 & 793.7666667 & & & & & \\ \hline 14 & Total & 23 & 5191.3333 & & & & & & \\ \hline \multicolumn{10}{|l|}{15} \\ \hline 16 & & Coefficients & Standard Error & t Stat & P-value & Lower 95% & Upper 95% & Lower 99.0% & Upper 99.0% \\ \hline 17 & Intercept & -275.8333333 & 112.8421033 & -2.444418575 & \begin{tabular}{|l|} 0.023898351 \\ \end{tabular} & \begin{tabular}{|l|} -511.2178361 \\ \end{tabular} & -40.44883053 & -596.9074508 & 45.24078413 \\ \hline 18 & Price & 175 & 44.54679188 & 3.928453489 & 0.0008316 & 82.07702045 & 267.9229796 & 48.24924412 & 301.7507559 \\ \hline 19 & \begin{tabular}{l} Advertising Expenditure \\ ($1,000s) \end{tabular} & 19.68 & 1.42735225 & 13.78776683 & 1.1263E11 & 16.70259538 & 22.65740462 & 15.61869796 & 23.74130204 \\ \hline \end{tabular} AnexExpress, a major credit card company, has a very large database of information provided b its customers when they apply for credit cards. These customer records include information on the customer's annual household income, number of years of post-high school education, and number of the customer's household. The company also has records of the credit card charges accrued by each customer over the past year. AnexExpress wants to predict the credit card charges that will be accrued by these applicants by using annual household income ( $1000 as a unit), the number of years of post-high school education, and the number of the customer's household reported by new applications. The company randomly selected 3,000 customers as a sample