Please open the file via here: https://drive.google.com/file/d/0B18H6Y9CiR-wYnhY

Question

Please open the file via here:

https://drive.google.com/file/d/0B18H6Y9CiR-wYnhYcExVS2kzTjA

And Please help to answer all 6 questions in the part 1 in words by detaily.(by typed, not in photo) thank you very much!

Part 1:

1. Before running any multiple regression analyses, compute and report the means and standard deviations for all variables EXCEPT id. Run a multiple regression analysis in which you predict salary from the other four primary variables.(detailly)

2. Report the results of the analysis. Be sure to identify the regression weights for each predictor and indicate which, if any, are significant predictors.(detailly)

3. What is the R2 for the model? What does this value mean? What percentage of variance in salary is explained by the four predictors?(detailly)

4. What is the predicted salary for a male who has been out of school for 7 years, has 18 publications, and 75 citations? What is the predicted salary for a female who is otherwise the same as her male counterpart? Rerun the regression analysis, including only the significant predictors from the first analysis.(detailly)

5. What is the R2 for the model? What does this value mean? What percentage of variance in salary is explained by the included predictors?(detailly)

6. What is the predicted salary for a male who has been out of school for 7 years, has 18 publications, and 75 citations? What is the predicted salary for a female who is otherwise the same as her male counterpart?(detailly)

Explanation / Answer

1)

Regression Analysis: Salary versus TimePhD, Pubs, Cites, Sex

The regression equation is
Salary = 39587 + 857 TimePhD + 92.7 Pubs + 202 Cites - 918 Sex

2)

Predictor Coef SE Coef T P
Constant 39587 2717 14.57 0.000
TimePhD 857.0 287.9 2.98 0.004
Pubs 92.75 85.93 1.08 0.285
Cites 201.93 57.51 3.51 0.001
Sex -918 1860 -0.49 0.624

From the p value Pubs and Sex are not significant predictors. TimePhd and cities are the significant predictors.

3)here R-Sq = 50.3% hence of the total variation in the response variable only 50.3% variation is explained by y(dependent) variable.

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