Download
special topics in multiple regression analysis n.
Skip this Video
Loading SlideShow in 5 Seconds..
Special Topics in Multiple Regression Analysis PowerPoint Presentation
Download Presentation
Special Topics in Multiple Regression Analysis

Special Topics in Multiple Regression Analysis

171 Vues Download Presentation
Télécharger la présentation

Special Topics in Multiple Regression Analysis

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Special Topics in Multiple Regression Analysis Chapter 11 – Appendix 11-A Learning Objectives: • Explain the use of dummy variables in regression analysis. • Examine residuals and outliers as they relate to multiple regression analysis.

  2. Dummy Variable . . . . an independent variable that has two (or more) distinct levels, which are coded 0 and 1.

  3. Dummy Variable Coding Category X1 X2 Physician 0 0 Attorney 1 0 Professor 0 1

  4. Exhibit A-1 Selected Variables from Employee Survey Independent Variables (Job Satisfaction & Gender) 2. I am doing the kind of work I want. Strongly Strongly Disagree Agree 1 2 3 4 5 6 7 5. My job allows me to learn new skills. Strongly Strongly Disagree Agree 1 2 3 4 5 6 7 7. My work give me a sense of accomplishment. Strongly Strongly Disagree Agree 1 2 3 4 5 6 7 19. Gender 0 = Male 1 = Female Dependent Variable 15. I am proud to tell others that I work for Samouel’s restaurant. Strongly Strongly Disagree Agree 1 2 3 4 5 6 7

  5. Exhibit A-2 Regression Model of Job Satisfaction and Commitment for Samouel’s Employees Model Summary *Predictors: (Constant), X19 – Gender, X7 – Accomplishment, X5 – Learn New Skills, X2 – Work I Want Dependent Variable: X15 – Proud

  6. Exhibit A-3 Beta Coefficients for Job Satisfaction and Commitment Regression Coefficients *Dependent Variable: X15 – Proud

  7. Exhibit A-4 Comparison of Male and Female Employee Perceptions Residual Statistics

  8. Exhibit A-4 Comparison of Male and Female Employee Perceptions Continued ANOVA Table

  9. Regression Analysis Terms • Explained variance = R2. • Unexplained variance or error = residuals.

  10. Regression Assumptions • The error variance is constant over all values of the independent variables; • The errors are uncorrelated with each of the independent variables; and • The errors are normally distributed.

  11. Residuals Plots • Plot of standardized residuals – enables you to determine if the errors are normally distributed (see Exhibit A-5). • Normal probability plot – enables you to determine if the errors are normally distributed. It compares the observed standardized residuals against the expected standardized residuals from a normal distribution (see Exhibit A-6). • Plot of standardized residuals – can be used to identify outliers. It compares the standardized predicted values of the dependent variable against the standardized residuals from the regression equation (see Exhibit A-7).

  12. Exhibit A-5 Histogram of Employee Survey Dependent Variable X15 – Proud

  13. Exhibit A-6 Normal Probability Plot of Regression Standardized Residuals

  14. Exhibit A-7 Scatterplot of Employee Survey Dependent Variable X15 – Proud

  15. Exhibit A-8 Residual Statistics for Employee Survey Residual Statistics* *Dependent Variable: X15 – Proud

  16. Exhibit A-8 Casewise Diagnostics for Employee Survey Casewise Diagnostics* Dependent Variable: X15 – Proud