Exam 8: Trendlines and Regression Analysis

List the systematic approach to build good multiple regression models.

Identify the components of simple linear regression models and discuss their applications 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). $\begin{array} { | l | l | } \hline\begin{array} { l } \text { Height } \\\text { (ft) }\end{array} & \text { Cost (\$) } \\\hline 4 & 670 \\\hline 3 & 430 \\\hline 7 & 810 \\\hline 9 & 1100 \\\hline 6 & 790 \\\hline 8 & 880 \\\hline 5 & 760 \\\hline 11 & 1200 \\\hline\end{array}$ -Which of the following generates a scatter chart in Excel with the values predicted by the regression model included?

Which of the following is true about multicollinearity?

Which of the following Excel functions is applied to test for significance of regression?

Explain the concept of curvilinear regression model.

An increase in adjusted ^R2 indicates that the regression model has improved.

Which of the following mathematical functions, used in predictive analytical models, is represented by the formula y = ax3 + bx2 + cx + d?

Briefly explain the assumptions on which the statistical hypothesis tests associated with regression analysis are predicated.

In a curvilinear regression model, the represents the curvilinear effect.

Categorical variables that have been coded are called .

In Excel's Trendline tool, the value of the gives the measure of fit of the line to the data.

Why is regression analysis necessary in business? What categories of regression models are used?

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

When using the t-statistic in multiple regression to determine if a variable should be removed:

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.) $\begin{array} { | l | l | } \hline \begin{array} { l } \text { Hours spent } \\\text { on the job }\end{array} & \text { Salary (\$) } \\\hline 4 & 340 \\\hline 12 & 850 \\\hline 7 & 570 \\\hline 5 & 470 \\\hline 11 & 820 \\\hline 8 & 610 \\\hline 9 & 630 \\\hline 13 & 900 \\\hline 10 & 800 \\\hline 6 & 480 \\\hline\end{array}$ -Is the hours spent on the job a statistically significant variable in explaining the variation in pay of employees? (Hint: Use Regression tool).

Which of the following equations correctly expresses the relationship between the two variables?

means that the variation about the regression line is constant for all values of the independent variable.

Excel's Trendline feature cannot be used in modeling trends which include time variables.

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.) $\begin{array} { | l | l | } \hline \begin{array} { l } \text { Hours spent } \\\text { on the job }\end{array} & \text { Salary (\$) } \\\hline 4 & 340 \\\hline 12 & 850 \\\hline 7 & 570 \\\hline 5 & 470 \\\hline 11 & 820 \\\hline 8 & 610 \\\hline 9 & 630 \\\hline 13 & 900 \\\hline 10 & 800 \\\hline 6 & 480 \\\hline\end{array}$ -Draw conclusions for test of hypothesis for regression coefficients.

Identify the components of simple linear regression models and discuss their applications 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). $\begin{array} { | l | l | } \hline\begin{array} { l } \text { Height } \\\text { (ft) }\end{array} & \text { Cost (\$) } \\\hline 4 & 670 \\\hline 3 & 430 \\\hline 7 & 810 \\\hline 9 & 1100 \\\hline 6 & 790 \\\hline 8 & 880 \\\hline 5 & 760 \\\hline 11 & 1200 \\\hline\end{array}$ -What is the value of the coefficient b1?