The sum of squares (SS) is the sum of squared distances, and is a measure of the variability that is from different sources.
Term | Description |
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b | number of operators |
n | number of replicates |
mean for each part | |
grand mean |
Term | Description |
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a | number of parts |
n | number of replicates |
mean for each operator | |
grand mean |
SS_{Part*Operator} = SS_{Total} – (SS_{Part} + SS_{Operator} + SS_{Repeatability})
Term | Description |
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each observation | |
mean of each factor level |
Term | Description |
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each observation | |
grand mean |
The degrees of freedom (DF) for each SS (sums of squares). In general, DF measures how much information is available to calculate each SS.
Term | Description |
---|---|
a | number of parts |
Term | Description |
---|---|
b | number of operators |
Term | Description |
---|---|
a | number of parts |
b | number of operators |
Term | Description |
---|---|
a | number of parts |
b | number of operators |
n | number of replicates |
Term | Description |
---|---|
a | number of parts |
b | number of operators |
n | number of replicates |
The mean squares (MS) is the variability in the data from different sources. MS accounts for the fact that different sources have different numbers of levels or possible values.
The F-statistic is used to determine whether the effects of Operator, Part, or Operator*Part are statistically significant.
The p-value is the probability of obtaining a test statistic (such as the F-statistic) that is at least as extreme as the value that is calculated from the sample, if the null hypothesis is true.