This implementation of the Anova procedure as derived from 'Mathematical Statistics with Applications' 7th edition by Wackerly, et el. That resource provides a full explanation, however a summary is given below. The goal of Anova is to "identify the important independent variables and determine how they affect the response". Where each independent variable is a factor and the intensity of the variable the level. The procedure calculates the Total Sum of Squares which is the sum of the squared deviations of each variable from the mean and a remainder random error. We consider that under the null hypothesis the independent variables are assumed to be unrelated to the response variable, and each portion of the total sum of squares divided by a corresponding constant provides an independent and unbiased estimator of $\sigma^2$ of the experimental error. If a variable is highly related to the response its contribution to the total sum of squares will be large. The variable sum of squares total $SST$ is compared with the sum of squares for the error $SSE$ An F test is used to determine whether the null hypothesis should be rejected. This implementation addresses the case for $k$ variables, and is used to determine the F test for the null hypothesis $\mu_1 = \mu_2 = ... = \mu_k$ and common variance $\sigma^2$. In this case we are working with matrices provided by the breeze library, at this time. However, it would be useful to later change the implementation to use another structure which allows uneven lengths of samples, since the number of rows for each sample will be equal in the matrix form. It is possible however to use unqual sample sizes for each $ith$ sample. An [[AnovaTable]] is used to contain the variables for the anova process providing a "one way layout" that comprises of the following elements. The total sum of squares is computed form the $SSE$ and the $SST$ $$ TotalSS = \sum_{i=1 \in k} \sum_{j=1 \in n_i} (Y_{ij} - \bar{Y})^2 $$ It can be summarised as being the total of all observations squared subtracting the correction for the mean $CM$ $$ \sum_{i=1 \in k} \sum_{j=1 \in n_i} Y_{ij}^2 - CM $$ where the correction for the mean is calculated as the total for all observations squared divided by $n$ $$ CM = \frac{1}{n} ( \sum_{i=1 \in k} \sum_{j=1 \in n_i} Y_{ij} )^2 $$ The total of each sample set is defined as $Y_{i.}$: $$ Y_i. = \sum_{j=1 \in n_i} Y_{ij} $$ and the mean of each sample set is estimated as $\bar{Y_{i.}}$. $$ \bar{Y_{i.} } = \frac{1}{n_i} \sum_{j=1 \in n_i} Y_{ij} $$ $$ \frac{1}{n_i} Y_{i.} $$ This is used in calculating the Sum of squares for treatments which will be large if the differences between the treatments is also large. $$ SST = \sum_{i=1 \in k} n_i (\bar{Y_{i.}} - \bar{Y})^2 $$ Note also that ${Z\ squared} = \frac{SST}{{\sigma^2}}$ having a $\chi^2$ distribution with $k-1$ df for $k$ factors. $$ SST = \sum_{i=1 \in k} \frac{Y_{i.}^2}{n_i} - CM $$ The second part the sum of squared errors is computed as $$ SSE = Total SS - SST $$ However it can also represent a total of the sample variances multiplied by a degree of freedom as shown in Wackerly. $$ SSE = \sum_{i=1 \in k} (n_i - 1) S_i^2 $$ where the sample variance $S^2$ is $$ {S^2} = \frac{1}{n_i-1} \sum_{j=1 \in n_i} (Y_{ij} - \bar{Y_{i.}})^2 $$ which is an unbiased estimator of $ \sigma_i^2 = \sigma^2 $ The Mean squared error is an estimator for the pooled variance $S^2$ with $n-k$ degrees of freedom. $$ MSE = \frac{SSE}{n-k} $$ The Mean square total is accumulated from the estimates of the mean for each sample with degree of freedom $k-1$. $$ MST = \frac{SST}{k-1} $$ Once the anova table is calculated the F-Test is used to test the null hypothesis $\mu_1 = \mu_2 = ... = \mu_k$ with even variance, and is rejected at the critical level $\alpha$ $$ F = \frac{MST}{MSE} > F_\alpha $$ The statistic is an F distribution with $k-1$ and $n-k$ numerator and denominator degrees of freedom. The key assumptions are the normal assumption for the $k$ samples, with equal means and variance. __Exampel Usage__ The example is derived from the test case TestAnovaInference which is also derived from an example in Wackerly on page 671. The test data for the example is the following matrix with $k = 4$ sets of observations.
/
columns correspond to k samples
rows correspond to sample observation Y_ij
/
val table = DenseMatrix(
(65.0, 75.0, 59.0, 94.0),
(87.0, 69.0, 78.0, 89.0),
(73.0, 83.0, 67.0, 80.0),
(79.0, 81.0, 62.0, 88.0),
(81.0, 72.0, 83.0, 0.0),
(69.0, 79.0, 76.0, 0.0),
(0.0, 90.0, 0.0, 0.0))
val anova = Anova(table)
val testResult = anova.test(0.05)
NumeratorDF: 3
DenominatorDF: 24
SST: 2876.107142857145
SSE: 23604.857142857145
MSE: 983.5357142857143
MST: 958.7023809523816
TotalDF: 27
TotalSS: 26480.96428571429
F-stat (observed statistic): 0.9747509592456765
F-alpha (critical value): 3.0000000000000013
P-Value: 0.4219003172019309
Observed-Prob 0.44613162513336035
alpha (significance level):0.05