Question

SUMMARY OUTPUT Regression Statistics Multiple R 0.440902923 R Square 0.194395388 Adjusted R Square 0.165100675 Standard Error 0.428710255 Observations 115 ANOVA df SS MS F Significance F Regression 4 4.878479035 1.219619759 6.635852231 8.02761E-05 Residual 110 20.21717314 0.183792483 Total 114 25.09565217 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.321875686 0.323939655 0.99362854 0.322584465 -0.320096675 0.963848047 -0.320096675 0.963848047 Gender -0.307211858 0.082630734 -3.717888514 0.000317832 -0.470966578 -0.143457137 -0.470966578 -0.143457137 Age 0.000724105 0.091134233 0.007945479 0.993674883 -0.179882553 0.181330763 -0.179882553 0.181330763 GPA 0.171984622 0.05279787 3.257415886 0.001495536 0.067351635 0.276617608 0.067351635 0.276617608 Total Q 0.000260423 0.003407326 0.076430415 0.939215512 -0.006492097 0.007012944 -0.006492097 0.007012944

The data set is a study of student persistent enrolling in the next semester based on Gender, Age, GPA, a 22 questionnaire on self-efficacy, and student enrollment status.The educational researcher wants to study the relationship between student enrollment status as it relates to gender, age, GPA, and the total response to a 22 questionnaire survey. 2. The estimated multiple regression analysis equation. 3. Does the model work? 4. How well does the model work? 5. Which variables contribute to the model? 6. General interpretation of the data and the data analysis

Answer 1

2) the estimated multiple regression model is, y=0.322-0.307gender+0.000724age+0.172GPA+0.000260423total q

3) there is a linear relation between the regressors to explain the response variable, the model may work.

4) the model won't work well, as the r sq and adjusted r sq is very small, i.e. the total variability of the respornse variable is only 44.09% explained by the explanatory variables.

5) from p values of the independent variable we can say that, gender and GPA are the variables that contributes to the model

6) there exists regressors which are insignificant, so the model is not good. It won't be a good predictive model as r sq is very small