Lessons

- CFA L2: Quantitative Methods - Introduction
- Quants: Correlation Analysis
- Quants: Single Variable Linear Regression Analysis
- Standard Error of the Estimate or SEE
- Confidence Intervals (CI) for Dependent Variable Prediction
- Coefficient of Determination (R-Squared)
- Analysis of Variance or ANOVA
- Multiple Regression Analysis
- Multiple Regression and Coefficient of Determination (R-Squared)
- Fcalc – the Global Test for Regression Significance
- Regression Analysis and Assumption Violations
- Qualitative and Dummy Variables in Regression Modeling
- Time Series Analysis: Simple and Log-linear Trend Models
- Auto-Regressive (AR) Time Series Models
- Auto-Regressive Models - Random Walks and Unit Roots
- ARMA Models and ARCH Testing
- How to Select the Most Appropriate Time Series Model?

# Fcalc – the Global Test for Regression Significance

- A statistically significant Fcalc (i.e. one that passes the Fcritical threshold, based on your degrees of freedom) can indicate that your model as a whole is meaningful.
- This test is really applicable for multiple regressions, where there is more than one slope coefficient (b1, b2, b3 … bi), as a t-test will not work for multiple regression models.
- The F-test is a one tailed test.
- The null hypothesis will be that the Fcalc is less than or equal to the Fcritical and you will be looking to reject the null with an Fcalc > Fcritical.
- A rejection of the null indicates that at least one of the slope coefficients is significant and there is some validity to the model.
- Fcalc has a math relationship with RSS (and MSSR), SSE (and MSSE), and TSS.