statistics: 6.1 MULTIPLE LINEAR REGRESSION

what is simple linear regression? 1 pt

we study the effect of one predictor/IV on the outcome/DV

e.g. the effect of study hours on grade

what is multiple linear regression? 1 pt

we study the effect of more than one predictor on the outcome to create more complex models in which we can include several predictor variables

e.g. the effect of study hours, past grades, and class attendance on grades

multipple linear regression line formula? 5 pts

y = a + (b1 x X1) + (b2 x X2) + error

1. y→ DV/outcome

2. b1→ regression coefficient/slope for variable 1

3. b2→ regression coefficient/slope for variable 2

4. x→ IV/predictor

multiple linear regression: assumption checks? 7 pts

1. the DV is measured at the continous level (interval or ratio)

2. the IV is measured at the continous level (interval or ratio) but can also be nominal or ordinal

3. there is a linear relationship between the predictor and outcome that can be evaluated using a scatterplot

4. there should be no significant outliers

5. homoscedasticity- where the variances along the line of best fit remain similar as you move along the line

6. there should be no multicollinearity because it leads to problems with underestanding which IV contributes to the variance explained in the DV

7. residual errors of regression line are approximately normally distributed