Section 26 LM: ANOVA
26.1 Analysis of Variance Table
\[ \large fm \leftarrow lm(SBP \sim BMI, \space data=BP) \]
\[ \large anova(fm) \]
| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| BMI | 1 | 15103.04 | 15103.0386 | 1988.629 | 0 |
| Residuals | 498 | 3782.16 | 7.5947 | NA | NA |
| Total | 499 | 18885.20 | NA | NA | NA |
26.2 Explanation
Degrees of freedom (df)
\(\large n\) = Total number of observations
Regression df = BMI df = \(\large 1\)
Residual df = \(\large n - 1 - 1\)
Total df = Regression df + Residual df = \(\large n - 1\)
Total Sum of Squares (TSS)
\[ \large TSS = \sum\limits_{i=1}^{n} (y_i-\bar y)^2 = S_{yy}\]
Sum of Squares due to Regression (SSb)
\[ \large SSb = \hat\beta_1\sum\limits_{i=1}^{n} (x_i-\bar x)(y_i-\bar y) = \hat\beta_1S_{xy}\]
Residual Sum of Squares (RSS)
\[ \large RSS = TSS - SSb = S_{yy} - \hat\beta_1S_{xy} \]
Mean Squares
Mean square = Sum of squares / degrees of freedom
\(\large MS = SS / df\)
F-value (Variance Ratio)
F value = Regression MS / Residual MS
Pr(>F)
P-value: the probability of obtaining a variance ratio this large under the null hypothesis that the coefficient equals to zero.
Under the null hypothesis the variance ratio has an F distribution.
Error Variance = Residual Mean Square
\(\large \hat\sigma^2 = Residual \space MS \space = MSE\)