Chartered Financial Analyst (CFA) Practice Exam Level 2

How is the mean square regression (MSR) calculated?

1. By dividing total sum of squares by the number of observations

2. By dividing residual sum of squares (RSS) by K

3. By subtracting error sum of squares (SSE) from total variance

4. By calculating the average of squared deviations

The correct answer is: By dividing residual sum of squares (RSS) by K

The mean square regression (MSR) is calculated by dividing the regression sum of squares (SSR) by the degrees of freedom associated with the regression. In the context of multiple regression, where K represents the number of independent variables in the model, the MSR is thus determined by dividing the SSR by K. This calculation provides a measure of how well the independent variables explain the variability of the dependent variable. To provide further context, the regression sum of squares illustrates how much of the total variance in the dependent variable can be attributed to the independent variables in the model. By dividing this value by K (the number of predictors), the MSR reflects the average contribution of each predictor to the explained variance, allowing for a better understanding of the model's efficacy. The relationship between MSR, the total sum of squares, and the residual sum of squares gives insight into the overall fit of the regression model, emphasizing the importance of understanding these foundational concepts in regression analysis for the CFA Level 2 exam.