M6 Assignment (45 points)

INSTRUCTION:

1. Use this Word document to type out all the answers. Each answer should be supported by pasting the relevant SPSS output table(s) in this document, or type out calculation process if manual calculation was needed.

2. Download the Excel data file in Canvas for this assignment and import the data into SPSS.

3. Required documents to submit: (Include your name in all file names)

This Word document with answers and supporting tables/calculations

SPSS data file(s) (.sav format)

SPSS output file(s) (.spv format or exported into Word format)

Note: No grade is earned if any document is missing.

4. Review the module "Assignment Guides" for guidelines and tutorials.

Q1. Perform and Interpret a Multiple Linear Regression (22 points total)

Subit an SPSS data file using the Q1 worksheet in the Excel file provided this week.

A. The following steps guide you to plan a multiple regression model to examine the factors related to "acceleration."

1. The data set contains the variable "acceleration" and three other variables that may be related to "acceleration." Examine the relationships among these four variables using bivariate correlations. Be sure to paste the correlation matrix (table) here. (1 point)

2. Select two predictors for the outcome variable "acceleration", then explain your rationale for selecting those predictors.

(4 points total: 2 points for the two predictors, 2 points for rationale)

B. The following steps guide you to perform the multiple linear regression that you have just planned in the previous steps, with two predictor variables for the outcome variable of "acceleration".

1. Choose either ENTER or STEPWISE as the method of adding predictors to the model. Explain your rationale. (2 points)

2. Report the omnibus test result on the regression model, including F, p, and adjusted R². Be sure to paste the relevant tables to support your answer. (2 points)

3. Explain what the omnibus result mean in words. (2 points)

4. Report the unstandardized regression equation, based on the SPSS output. Proper symbols should be used in the equations. Be sure to paste the relevant output table(s) here to support your answer. (2 points)

5. Report the statistics on each predictor variable, including b, t, and p. (3 points)

6. Discuss the relative contributions of the two predictors in the model. Which is the stronger predictor? (2 points)

7. Should you be concerned about multicollinearity in this model? Why or why not? (2 points)

C. Now let's think about the relationship among the three variables (2 predictors and 1 outcome) as revealed first in section A (as simple correlations) and then in section B (as multiple regression).

Compare the result of the multiple regression analysis of the three variables (from section B) with the result of the simple correlations among the same three variables (from A1). Discuss the similarities and differences based on your observations. (2 points: 1 for similarities, 1 for differences)

Q2. Perform and Interpret a Multiple Linear Regression (23 points total)

Submit an SPSS data file from the Q2 worksheet in the Excel file provided this week. The data set contains information about the chain stores of a retail company. The company analyst collected data on a set of variables that are hypothesized to contribute to the annual net sales amount (this is the outcome variable). (1 point: deduct .5 for each error, up to 1 point total)

A. Preliminary analysis to explore the data.

1. Perform bivariate correlations among all variables in the data set and paste the table here. (1 point)

2. Describe the correlations you see. Are there any red flags (or concerns with the relationships among certain predictor variables)? Why or why not?

(2 points)

B. Perform a multiple linear regression analysis in SPSS, using all five predictors for the outcome of annual net sales amount.

1. Choose either ENTER or STEPWISE as the method of adding predictors to the model. Explain your rationale. (3 points)

2. Report the omnibus test result on the regression model, including F, p, and adjusted R² (2 points)

3. Explain what the omnibus result mean in words. (3 points)

4. Report the unstandardized regression equation, based on the SPSS output. Proper symbols should be used in the equations. (2 points)

5. Report the statistics on each predictor variable, including b, t, and p. (3 points)

6. Discuss the relative contributions of the five predictors in the model. Which is the strongest predictor? (3 points)

7. Should you be concerned about multicollinearity in this model? Why or why not? (3 points)

https://www.coursehero.com/u/file/102281373/M6-Data-Setxlsx/#question

https://drive.google.com/drive/folders/19ki5Lg3EUWRnHxZWfMXAgm47KAusKCwF?usp=sharing

Follow the link for the dataset