Proportions

Chi Square test

One way Anova

Kruskal-Wallis Test

Variation Levene test

Multiple Correlation

Multiple linear Regression

Generate Distribution

Calculations

Filter

Box-Cox transformation

Gauge R&R

Distribution fitting

Graphs

For commersial use

75 EURO

The One way Anova calculates if the mean out of two or more date-set at least one data-set is significant different. The calculation calculates the Anova only from the checked data-sets in the Anova dialogue.

- The smaller the p value is the more likely there is a data-set with a significant difference.
- Develve uses the commonly accepted value of p < 0.05 for significance.
- No significant difference does not mean that the mean of the data-sets are the same!

- The data-sets must be normally distributed see Anderson Darling normality test.
- If the data set is not normally distributed see "What to do with not normally distributed data". The Kruskal-Wallis Test is the not normally distributed counter part of One way anova.

- The samples are independent
- The variation of the data-sets are equal

- GreenNo significant difference
- YellowSignificant difference
- OrangeSample size to small

Total amount of data points

Amount of columns (data sets)

Amount of rows (data points of one data set)

data point of row i of data-set of column j

Degrees of freedom

Sum of squares

Mean squares

=Max Mean difference in the group

From the selected data-sets there is at least one data-set that is significant different in mean compared to the other data-sets.

But beware of the difference in variation and one data-set that is not normally distributed.

- Normality 0.00 Not OK.
- Levene Variation test 0.00 Not OK.

Data file

- http://en.wikipedia.org/wiki/One-way_analysis_of_variance
- http://www.itl.nist.gov/div898/handbook/ppc/section2/ppc231.htm