The following information applies to questions 1-3.

Go tohttps://rconnect.byu.edu/Stat121App/

On the left panel, select "Regression". Select the "Graduate Admission for Indian Students" dataset, then select "GRE.score" as your explanatory variable and "Chance.of.Admit" as your response variable. We want to see if there is a correlation between GRE scores and their chance of admission into a university's master's program. Generate a scatterplot of the data. Ignore the regression assumptions for now by selecting "Proceed to Checking Assumptions", then "Proceed to Regression Analysis".

1. What is the value ofr2?Interpret it in context.

a) r2= 0.64. 64% of admissions can be explained by GRE score.

b) r2= 0.64. 64% of the variation in chances of admission can be explained by GRE score.

c) r2= 0.80. 80% of admissions can be explained by GRE score.

d) r2= 0.80. 80% of the variation in chances of admission can be explained by GRE score.

2. Using the given table, find the slope and interpret it in context.

a) The slope is 0.01, meaning that for every 1 point increase in a GRE score, the average chance of admission increases by 0.01 (or 1%).

b) The slope is 0.01, meaning that for every 0.01 percent increase in a GRE score, the average chance of admission increases by 1.

c) The slope is -2.44, meaning that for every 1 point increase in a GRE score, the average chance of admission decreases by -2.44%.

d) The slope is -2.44, meaning that for every 2.44 point increase in a GRE score, the average chance of admission decreases by 1%.

3. Obtain the equation for the least-squares regression line for this data. What is the average chance of admission for a student who scores a 305 on the GRE?

a) 45% (or 0.45)

b) 51% (or 0.51)

c) 61% (or 0.61)

d) 70% (or 0.71)