# Quick Review Of Some Inference For Linear Regression Concepts

7 Questions | Total Attempts: 164  Settings  Inference and modeling concepts for AP Statistics students

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• 1.
How do you know that you should attempt to use a linear regression method for predicting y values from x values?
• A.

When the moon is full.

• B.

When Mrs. Linner gives you the evil eye.

• C.

When the data look linear when graphed in a scatterplot.

• D.

When the relationship should (theoretically) be linear.

• E.

Either C or D will suffice.

• 2.
How do you know that your linear model was a wise choice (the linear model is the correct model)?
• A.

When I flip a coin and it lands heads up.

• B.

When I have run out of other models to try.

• C.

When the r^2 value is close to 1.

• D.

When the graph of the residuals(x, y - y-hat)forms a straight line.

• E.

When the residuals(x, y - y-hat)are small and scattered(no pattern).

• 3.
When do you know that the model is a close fit to the data?
• A.

When I flip a coin and it lands heads up.

• B.

When I have run out of other models to try.

• C.

When the r^2 value is close to 1.

• D.

When the graph of the residuals(x, y - y-hat)forms a straight line.

• E.

When the residuals(x, y - y-hat)are small and scattered(no pattern).

• 4.
When is the relationship between x and y too strong to assume that the slope of the best fit line (Beta) is possibly zero?
• A.

When the confidence interval for the slope does not contain the value 0.

• B.

When the p value of the linear regression t-test is very small.

• C.

When the residuals are small and scattered.

• D.

When the r^2 value is close to 1

• E.

Answers A or B will suffice.

• 5.
Given r, sx, and sy you can calculate
• A.

Beta

• B.

Alpha

• C.

B

• D.

S

• E.

SE of b

• 6.
Given s, sx, and n you can calculate
• A.

Beta

• B.

Alpha

• C.

B

• D.

R

• E.

SE of b

• 7.
Given b and SE of b you still need to know _______________ to calculate the t-statistic and the p-value.
• A.

R

• B.

N

• C.

Df

• D.

A

• E.

B or C