Results Section For Multiple Regression. The analysis revealed 2 dummy variables that has a significant relationship with the DV. If additional models are fit with different predictors use the adjusted R 2 values and the predicted R 2 values to compare how well the models fit.
Multiple linear regression requires at least two independent variables which can be nominal ordinal or intervalratio level variables. These assumptions deal with outliers collinearity of data independent errors random normal distribution of errors homoscedasticity linearity of data and non-zero. The multiple regression model with all four predictors produced R² 575 F4 135 4567 p 001.
Evaluating the Regression Results.
The multiple regression model with all four predictors produced R² 575 F4 135 4567 p 001. Note that it should be made clear in the text what the variables are and how each is measured. A multiple linear regression was calculated to predict weight based on their height and sex. As can be seen in Table1 the Analytic and Quantitative GRE scales had significant positive regression weights indicating students with higher scores on these scales were expected to have higher 1st year GPA after controlling for the other.