2 2 1. Analyze R, R , Adjusted R , Significance F for each of the four regressio

Question

2 2 1. Analyze R, R , Adjusted R , Significance F for each of the four regressions and talk about inconsistencies between each regression. Be sure to interpret the multiple regression as a whole. 2. If relevant, discuss possible multicollinearity and its correction. Suggest alternative models you might use to test the robustness of the results. See the Supplemental Topics in Regression for this topic 3. Predict the salary for a female whose GMAT score is 624 and who graduates from a school with an acceptance rate of 27. What about for a male? Look at the sample data to see if these salaries seem reasonable with what you have observed. 4. Can you predict the salary for a female whose GMAT score is 550? Why or why not? See the Supplemental Topics in Regression for the answer.

Explanation / Answer

We look at the values of r , r2 and adj r2 in the summary outputs

for the first model
the value of r is 0.4923 , which means that the 2 variables GMAt and salary have a weak positive correlation

r2 is 0.2424 , which means that the model is able to explain only 24% variation in salary due to variation in GMAt score

adj r2 is a conservative figure and accounts for the number of independent variables used in the model

adj r2 is 0.147 , which means that the model is able to explain only 14.7% variation in salary due to variation in GMAt score

For the second model

the value of r is 0.4174, which means that the 2 variables acc.rate and salary have a weak positive correlation

r2 is 0.17427 , which means that the model is able to explain only 17.4% variation in salary due to variation in acc.rate

adj r2 is a conservative figure and accounts for the number of independent variables used in the model

adj r2 is 0.07 , which means that the model is able to explain only 7% variation in salary due to variation in acc.rate

for the third model

the value of r is 0.4476, which means that the 2 variables gender and salary have a weak positive correlation

r2 is 0.200 , which means that the model is able to explain only 20% variation in salary due to variation in gender

adj r2 is a conservative figure and accounts for the number of independent variables used in the model

adj r2 is 0.100 , which means that the model is able to explain only 10% variation in salary due to variation in gender

for the fourth model

r2 is 0.3019 , which means that the model is able to explain only 309% variation in salary due to variation in acc.rate, gmat score and gender

adj r2 is a conservative figure and accounts for the number of independent variables used in the model

adj r2 is -0.04 , Negative Adjusted R2 appears when Residual sum of squares approaches to the total sum of squares, which essentialy means that the explanation towards the response variable salary is extremely low due to the predictor variables

Please note that we can answer only 1 question at a time , asp er the answering guidelines

Related Questions

Navigate

• Browse (All)
• Browse 2
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.