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One-Way ANOVA
The between-sample sum of squares (SSB) is a measure of the data variablility explained by the model. It is also a non-standardized measure of how well the model fits the data. Larger values of SSB indicate the model fits the data better, all things being equal. The SSB is sometime called the sum of square due to Treatment (SST) by some sources. Other names for it include sum of squares due to model (SSM) and sum of squares due to regression (SSR). As usual, compare the notation used in yoru book with the notation used here.
As usual, these calculations refer only to the one-way completely randomized design, the most basic of sample designs.
Example #118: Let us test the null hypothesis that the average yield does not depend upon the treatment used. Since there are 3 treatments in this experiment, the hypotheses are
H0 : μ1 = μ2 = μ3
HA : At least one mean differs from the others.
To test this hypothesis, we collect data. The data consist of two measurements on each unit: yield value and treatment level. (Note that yield is assumed numeric and treatment is assumed categorical.) It is typical to group the experimental units by treatment level. Thus, our data are
Treatment 1:
16, 12, 16, 16, 13, 10
Treatment 2:
12, 9, 13, 13, 15, 15, 8, 15
Treatment 3:
10, 9, 14, 8, 11, 18, 11, 12
With this information, calculate the sum of squares between (SSB) for the data.
To summarize the above, the values of import are:
\( \bar{x} \) | = | 12.5455 |
---|---|---|
\( \bar{x}_1 \) | = | 13.8333 |
\( \bar{x}_2 \) | = | 12.5 |
\( \bar{x}_3 \) | = | 11.625 |
\( n_1 \) | = | 6 |
\( n_2 \) | = | 8 |
\( n_3 \) | = | 8 |
In the box below, please enter the sum of squares between (SSB) for the data, then click on the “Check your answer!” button. Please round your answer to the ten-thousandths place.
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