1. Based on the correlation value, your team decides to perform analysis to determine if a student's high school percentile depends on the student's SAT Score. What should you use to evaluate this?

Group of answer choices

Chi-Square Test for Independence

Simple Linear Regression

Multiple Linear Regression

Correlation

2.

Sydney suggests that you test for a relationship between Gender and SecondFallRegistered, to see if student retention is the same across genders. Which model should you use?

Group of answer choices

Simple Linear Regression

Correlation

Chi-Square Test for Independence

Multiple Linear Regression

3.

You create a simple linear regression model, using GPA (HSGPA) to predict CombinedScore. The fitted regression model is: CombinedScore = 35.6 + 50 * GPA. GPA ranges from 0.0 - 4.0, but your sample GPA values range from 2.1 to 3.8. Sydney wants to use your model to predict the CombinedScore for a student that has a 4.0 GPA. What should you tell her?

Group of answer choices

The predicted CombinedScore is 235.6.

You cannot extrapolate based on the model.

Not enough information.

The predicted CombinedScore is 35.6

4.

Using the student retention dataset, your team develops a regression model to predict the Combined Score (i.e., score used to rank students) using HSPercentile and the students SAT score. The regression equation is given by: CombinedScore = 118.95 + 1.91 * HSPercentile + 0.06 * SAT. A new student has applied and you want to predict their CombinedScore. They have a HSPercentile = 0.82 and SAT = 1320. Their predicted Combined Score (rounded to 2 decimal places) would be _______.

Group of answer choices

176.22

201.37

135.91

199.72

5.

You create a model using HSPercentile, Gender, and SAT Score to predict CombinedScore. You want to evaluate the model using the Adjusted R2 value. You obtain a value of 0.76. How should you explain this to Sydney?

The regression model with HSPercentile, Gender and SAT Score explains 76% of the variability in CombinedScore, adjusting for the number of predictors.

Not enough information.

Holding all other variables constant, a 1 unit change in the predictor variables results in a 0.76 increase in CombinedScore

The regression model with CombinedScoreexplains 76% of the variability in HSPercentile, Gender and SAT Score