Question

What distribution should be used to model variance? Why?

Answer 1

• Distribution should be used to model variance is one way analysis of variance ( one way ANOVA).
• one way ANOVA is a technique that can be used to compare means of two or more samples (using the F distribution).
• This technique can be used only for numerical response data, the "Y", usually one variable, and numerical or (usually) categorical input data, the "X", always one variable, hence "one-way".
• one-way ANOVA is used to test for differences among at least three groups

The results of a one-way ANOVA can be considered reliable as long as the following assumptions are met:

• Reaction variable residuals are ordinarily disseminated (or roughly typically appropriated).
• Fluctuations of populaces are equivalent.
• Reactions for a given gathering are autonomous and indistinguishably disseminated typical arbitrary factors (not a straightforward irregular example (SRS)).

Model variance:

The ordinary straight model depicts treatment bunches with likelihood circulations which are indistinguishably chime molded (typical) bends with various means. In this way fitting the models requires just the methods for every treatment gathering and a fluctuation computation (a normal change inside the treatment bunches is utilized). Estimations of the methods and the change are executed as a major aspect of the theory test.

The regularly utilized ordinary direct models for a totally randomized examination are

The core ANOVA analysis consists of a series of calculations. The data is collected in tabular form. Then

Each treatment group is summarized by the number of experimental units, two sums, a mean and a variance. The treatment group summaries are combined to provide totals for the number of units and the sums. The grand mean and grand variance are computed from the grand sums. The treatment and grand means are used in the model.
The three DFs and SSs are calculated from the summaries. Then the MSs are calculated and a ratio determines F.
A computer typically determines a p-value from F which determines whether treatments produce significantly different results. If the result is significant, then the model provisionally has validity.