Data Science Course > Analysis of Variance

Assumption and Variation in One-Way ANOVA in Data Science

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  • Normality refers to the fact that each sample is drawn from a population that is regularly distributed.
  • Sample independence refers to the fact that each sample was drawn independently of the others.
  • Variation equality refers to the fact that the variance of data across groups should be the same.

(1) interval data of the dependent variable, (2) normality, (3) homoscedasticity, and (4) no multicollinearity are all assumptions that must be met in the factorial ANOVA.

The analysis of variance (ANOVA) is a statistical method that divides a data set's observed aggregate variability into two parts: systematic components and random factors. Random factors have no statistical impact on the supplied data set, whereas systematic influences do.

The samples must come from populations that are regularly distributed. The sampling is carried out accurately. Independent observations must be made within and between groups. Variations between populations must be equal (homoscedastic).

The one-way ANOVA is regarded as a reliable test for the assumption of normality. This means it tolerates deviations from its normalcy assumption fairly well.

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