##
Analysis of Variance for Skin Cancer Data
Section* *

We've covered quite a bit of ground. Let's review the analysis of variance table for the example concerning skin cancer mortality and latitude (Skin Cancer data).

### Analysis of Variance

Source | DF | Adj SS | Adj MS | F-Value | P-Value |
---|---|---|---|---|---|

Constant | 1 | 36464 | 36464 | 99.80 | 0.000 |

Residual Error | 47 | 17173 | 365 | ||

Total | 48 | 53637 |

### Model Summary

S | R-sq | R-sq(adj) |
---|---|---|

19.12 | 68.0% | 67.3% |

### Coefficients

Predictor | Coef | SE Coef | T-Value | P-Value |
---|---|---|---|---|

Constant | 389.19 | 23.81 | 16.34 | 0.000 |

Lat | -5.9776 | 0.5984 | -9.99 | 0.000 |

### Regression Equation

Mort = 389 - 5.98 Lat

Recall that there were 49 states in the data set.

- The degrees of freedom associated with
*SSR*will always be 1 for the simple linear regression model. The degrees of freedom associated with*SSTO*is*n*-1 = 49-1 = 48. The degrees of freedom associated with*SSE*is*n*-2 = 49-2 = 47. And the degrees of freedom add up: 1 + 47 = 48. - The sums of squares add up:
*SSTO*=*SSR*+*SSE*. That is, here: 53637 = 36464 + 17173.

Let's tackle a few more columns of the analysis of variance table, namely the "**mean square**" column, labeled **MS**, and the *F*-statistic column labeled **F**.

### Definitions of mean squares

We already know the "**mean square error ( MSE)**" is defined as:

\(MSE=\dfrac{\sum(y_i-\hat{y}_i)^2}{n-2}=\dfrac{SSE}{n-2}\)

That is, we obtain the mean square error by dividing the error sum of squares by its associated degrees of freedom *n*-2. Similarly, we obtain the "**regression mean square ( MSR)**" by dividing the regression sum of squares by its degrees of freedom 1:

\(MSR=\dfrac{\sum(\hat{y}_i-\bar{y})^2}{1}=\dfrac{SSR}{1}\)

Of course, that means the regression sum of squares (*SSR*) and the regression mean square (*MSR*) are always identical for the simple linear regression model.

Now, why do we care about mean squares? Because their expected values suggest how to test the null hypothesis \(H_{0} \colon \beta_{1} = 0\) against the alternative hypothesis \(H_{A} \colon \beta_{1} ≠ 0\).

### Expected mean squares

Imagine taking many, many random samples of size *n* from some population, estimating the regression line, and determining *MSR* and *MSE* for each data set obtained. It has been shown that the average (that is, the expected value) of all of the *MSR*s you can obtain equals:

\(E(MSR)=\sigma^2+\beta_{1}^{2}\sum_{i=1}^{n}(X_i-\bar{X})^2\)

Similarly, it has been shown that the average (that is, the expected value) of all of the *MSE*s you can obtain equals:

\(E(MSE)=\sigma^2\)

These expected values suggest how to test \(H_{0} \colon \beta_{1} = 0\) versus \(H_{A} \colon \beta_{1} ≠ 0\):

- If \(\beta_{1} = 0\), then we'd expect the ratio
*MSR*/*MSE*to equal 1. - If \(\beta_{1} ≠ 0\), then we'd expect the ratio
*MSR*/*MSE*to be greater than 1.

These two facts suggest that we should use the ratio, *MSR*/*MSE*, to determine whether or not \(\beta_{1} = 0\).

**Note!**because \(\beta_{1}\) is squared in

*E(MSR)*, we cannot use the ratio

*MSR*/

*MSE*:

- to test \(H_{0} \colon \beta_{1} = 0\) versus \(H_{A} \colon \beta_{1} < 0\)
- or to test \(H_{0} \colon \beta_{1} = 0\) versus \(H_{A} \colon \beta_{1} > 0\).

We have now completed our investigation of all of the entries of a standard analysis of variance table. The formula for each entry is summarized for you in the following analysis of variance table:

Source of Variation | DF | SS | MS | F |
---|---|---|---|---|

Regression | 1 | \(SSR=\sum_{i=1}^{n}(\hat{y}_i-\bar{y})^2\) | \(MSR=\dfrac{SSR}{1}\) | \(F^*=\dfrac{MSR}{MSE}\) |

Residual error | n-2 |
\(SSE=\sum_{i=1}^{n}(y_i-\hat{y}_i)^2\) | \(MSE=\dfrac{SSE}{n-2}\) | |

Total | n-1 |
\(SSTO=\sum_{i=1}^{n}(y_i-\bar{y})^2\) |

However, we will always let Minitab do the dirty work of calculating the values for us. Why is the ratio *MSR*/*MSE* labeled *F* * in the analysis of variance table? That's because the ratio is known to follow an **F**** distribution** with 1 numerator degree of freedom and *n*-2 denominator degrees of freedom. For this reason, it is often referred to as the analysis of variance *F*-test. The following section summarizes the formal *F*-test.

### The formal *F*-test for the slope parameter \(\beta_{1}\)

The null hypothesis is \(H_{0} \colon \beta_{1} = 0\).

The alternative hypothesis is \(H_{A} \colon \beta_{1} ≠ 0\).

The test statistic is \(F^*=\dfrac{MSR}{MSE}\).

As always, the *P*-value is obtained by answering the question: "What is the probability that we’d get an *F** statistic as large as we did if the null hypothesis is true?"

The *P*-value is determined by comparing *F** to an *F* distribution with 1 numerator degree of freedom and *n*-2 denominator degrees of freedom.

In reality, we are going to let Minitab calculate the *F** statistic and the *P*-value for us. Let's try it out on a new example!