Exam 9: Regression Analysis

While checking for linearity by examining the residual plot, the residuals must:

Interaction is:

Use the data given below to answer the following question(s). Following is an extract from a firm's database detailing the number of hours spent on the job by employees and their corresponding pay.(Note: Assume a level of significance of 0.05 wherever necessary.) $\begin{array} { | r | r | } \hline \begin{array} { l } \text { Hours spent } \\\text { on the job }\end{array} & \text { Salary (\$) } \\\hline 4 & 340 \\\hline 12 & 850 \\\hline 7 & 570 \\\hline 5 & 470 \\\hline 11 & 820 \\\hline 8 & 610 \\\hline 9 & 630 \\\hline 13 & 900 \\\hline 10 & 800 \\\hline 6 & 480 \\\hline\end{array}$ -Why is regression analysis necessary in business? What categories of regression models are used?

When two or more independent variables in the same regression model can predict each other better than the dependent variable, the condition is referred to as ________.

Use the data given below to answer the following question(s). Following is an extract from a firm's database detailing the number of hours spent on the job by employees and their corresponding pay.(Note: Assume a level of significance of 0.05 wherever necessary.) $\begin{array} { | r | r | } \hline \begin{array} { l } \text { Hours spent } \\\text { on the job }\end{array} & \text { Salary (\$) } \\\hline 4 & 340 \\\hline 12 & 850 \\\hline 7 & 570 \\\hline 5 & 470 \\\hline 11 & 820 \\\hline 8 & 610 \\\hline 9 & 630 \\\hline 13 & 900 \\\hline 10 & 800 \\\hline 6 & 480 \\\hline\end{array}$ -The best-fitting line maximizes the residuals.

Standard residuals:

Use the data given below to answer the following question(s). Following is an extract from the database of a construction company.The table shows the height of walls in feet and the cost of raising them.The estimated simple linear regression equation is given as Ŷ = b0 + b1X.(Hint: Use Excel functions). $\begin{array} { | r | r | } \hline \begin{array} { l } \text { Height } \\\text { (ft) }\end{array} & \text { Cost (\$) } \\\hline 4 & 670 \\\hline 3 & 430 \\\hline 7 & 810 \\\hline 9 & 1100 \\\hline 6 & 790 \\\hline 8 & 880 \\\hline 5 & 760 \\\hline 11 & 1200 \\\hline\end{array}$ -For a simple linear regression model, significance of regression is:

Use the data given below to answer the following question(s). Following is an extract from a firm's database detailing the number of hours spent on the job by employees and their corresponding pay.(Note: Assume a level of significance of 0.05 wherever necessary.) $\begin{array} { | r | r | } \hline \begin{array} { l } \text { Hours spent } \\\text { on the job }\end{array} & \text { Salary (\$) } \\\hline 4 & 340 \\\hline 12 & 850 \\\hline 7 & 570 \\\hline 5 & 470 \\\hline 11 & 820 \\\hline 8 & 610 \\\hline 9 & 630 \\\hline 13 & 900 \\\hline 10 & 800 \\\hline 6 & 480 \\\hline\end{array}$ -List the systematic approach to building good multiple regression models.

Use the data given below to answer the following question(s). Following is an extract from the database of a construction company.The table shows the height of walls in feet and the cost of raising them.The estimated simple linear regression equation is given as Ŷ = b0 + b1X.(Hint: Use Excel functions). $\begin{array} { | r | r | } \hline \begin{array} { l } \text { Height } \\\text { (ft) }\end{array} & \text { Cost (\$) } \\\hline 4 & 670 \\\hline 3 & 430 \\\hline 7 & 810 \\\hline 9 & 1100 \\\hline 6 & 790 \\\hline 8 & 880 \\\hline 5 & 760 \\\hline 11 & 1200 \\\hline\end{array}$ -The R² value:

Use the data given below to answer the following question(s). Following is an extract from the database of a construction company.The table shows the height of walls in feet and the cost of raising them.The estimated simple linear regression equation is given as Ŷ = b0 + b1X.(Hint: Use Excel functions). $\begin{array} { | r | r | } \hline \begin{array} { l } \text { Height } \\\text { (ft) }\end{array} & \text { Cost (\$) } \\\hline 4 & 670 \\\hline 3 & 430 \\\hline 7 & 810 \\\hline 9 & 1100 \\\hline 6 & 790 \\\hline 8 & 880 \\\hline 5 & 760 \\\hline 11 & 1200 \\\hline\end{array}$ -What is the value of the coefficient b₁?