STATISTICS FOR DATA ANALYTICS - 21

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ANOVA TEST

ANALYSIS OF VARIANCE ( ANOVA )

Definition :- Anova is a statistical method used to compare the mean of 2 or more groups.

Factors (variable)
Levels.

Assumptions in Anova test :-

Normality of sampling distribution of means.
The distribution of sample mean is normally distributed.

Absence of OUtliers
Outlying core needs to be removed from the dataset..

Homogeneity of variance
Each one of the population has the same variance.

Samples are independent and random.

Type of Anova

One way anova.
One factor with at least 2 levels, these levels are independent.

Repeated measure anova
One factor with at least 2 levels, these levels are dependent.

Factorial anova :-
Two or More factors each of which with at least 2 levels , levels can be independent or dependent.

Critical value is less than calculated value then we reject null hypothesis.

Important

When we have only two samples, t-test and ANOVA give the same results.
But when the sample size increases, then the t-test becomes tedious as one has to perform the test for each combination of the groups.

While using multiple time t-tests it increases Type 1 error.

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