lecture 3- linear regression

Question 1

what is r- correlation

Answer 1

r is the change in SD units of Y that occurs for every 1 SD change in X

Question 2

how do you calculate pearsons r?

Answer 2

1. Calculate covariance between the X and Y variables, and then standardize
2. Convert the X and Y scores to z-scores (standard scores), then divide by n

Question 3

correlation vs regression

Answer 3

Correlation: is there a relationship between 2 variables?
Regression: how well does one variable predict the other variable?

Question 4

what does prediction require?

Answer 4

Prediction requires calculating a line of best fit (an equation)

• You can then use this equation to obtain a best-fit estimate for any new data point (X)
  within the range of the original data set.

Question 5

dependant variable

Answer 5

DV or criterion variable or response variable
- the variable that you are trying to predict

Question 6

independent variable

Answer 6

IV or predictor variable or regressor
= the variable that you are trying to predict from

Question 7

simple linear regression = 1 predictor variable
multiple regression = 1+ predictor variables

Question 8

what is the equation for straight line?

Answer 8

y= a + bX

Question 9

what is the best regression line?

Answer 9

The best regression line is the line that minimizes the sum of squared deviations
(i.e., a line that satisfies the “least squares” criterion)

Question 10

deviations = predicted value minus observed value