Go to https://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. Generate a scatterplot of the data. We want to see if there is acorrelation between GRE scores and their chance of admission into a university's master's program.

1. Select "Proceed to Checking Assumptions". Is the linearity condition met? (Hint: Select the assumption plotthat checks "Linearity".)

A.Yes, the scatterplot shows a linear pattern in the data.

B.Yes, the residual plot is normal.

C.Yes, there is no megaphone pattern found in the scatterplot.

D.Yes, there is no megaphone pattern in the residual plot.

2. Select "Proceed to Regression Analysis". Refer to the generated table. If we perform a t test for the slope ofthe regression line, what is the test statistic and p-value for this procedure?

A.t = -20.68, p-value = 0.00

B.t = -20.68, p-value = -2.67

C.t = 26.84, p-value = 0.01

D.t = 26.84, p-value = 0.00

3. Calculate a 95% confidence interval for the slope on the line. Assuming that = 0.05, can we use this interval as evidence that there is a linear relationship between GRE score and chance of admission?

A.No. Confidence intervals cannot be used to make conclusions for hypothesis tests.

B.No. We don't know the alternative hypothesis, so we cannot make a conclusion on the relationship between GRE score and chance of admission.

C.Yes. Because our alternative hypothesis is one-sided and 0 is not found in the confidence interval, we reject the null hypothesis and conclude that there is a linear relationship between GRE score and chance of admission.

D.Yes. Because our alternative hypothesis is two-sided and 0 is not found in the confidence interval, we reject the null hypothesis and conclude that there is a linear relationship between GRE score and chance of admission.

Go to the link https://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. Generate a scatterplot of the data. Select "Proceed to Checking Assumptions", then "Proceed to Regression Analysis", then "Proceed to Predictions".

4. Suppose a student scores a 310 on the GRE. With 98% confidence, A student's chances of admission is between _____ and _____.

A.(0.457, 0.856)

B.(0.489, 0.824)

C.(0.645, 0.668)

D.(0.647, 0.666)

5. With 98% confidence, the mean chance of admission for all students who score a 310 on the GRE is between _____ and _____.

A.(0.457, 0.856)

B.(0.489, 0.824)

C.(0.645, 0.668)

D.(0.647, 0.666)