Linear Regression

Question 1

When do we use linear regression?

Answer 1

Predicting the outcome of 1 numeric variable based on the values of another numeric variable

Question 2

What is the equation for linear regression?

Answer 2

y^ = a + bx

Question 3

Interpretation for linear regression

Answer 3

__% of the variability in ____ (y^) is accounted for by the variability in ____(x)

Question 4

Assumptions for linear regression

Answer 4

Relationship between x and y is linear (use residual plots to find no curvature), Errors are normally distributed (histogram and box plot for symmetry, outliers), Errors have constant variance for all x-values (look at residual plot of vertical scatter being the same)

Question 5

Hypotheses

Answer 5

H0: ____ is not a linear predictor of ____

Ha: ____ is a linear predictor of _____

Question 6

What values do we list?

Answer 6

Test statistic (t or F), p-value

Question 7

Conclusion

Answer 7

There is/isn’t sufficient evidence of a linear relationship between _____ and _____

Question 8

CI

Answer 8

We are 95% confident that as ____ increases by 1 ___(units) ______ increases/decreases between _____ and ____ on average

Question 9

How do you find residual values

Answer 9

y - y^