Variance homogeneity, also known as homoscedasticity, is the assumption that different samples in a dataset have the same variance. This assumption is crucial for the validity of many statistical tests, such as ANOVA and linear regression, which rely on consistent variability across groups to accurately detect effects or relationships.

Variance Homogeneity

Variance homogeneity

Homoscedasticity refers to the assumption that the variance of errors or disturbances in a regression model is constant across all levels of the independent variable(s). It is crucial for ensuring the validity of statistical tests and confidence intervals in linear regression analysis, as heteroscedasticity can lead to inefficient estimates and biased inference.

homoscedasticity

same variance

statistical tests

ANOVA, or Analysis of Variance, is a statistical method used to determine if there are significant differences between the means of three or more independent groups. It helps in understanding whether the observed variations between group means are due to actual differences or random chance.

ANOVA

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. It is widely used for prediction and forecasting, as well as understanding the strength and nature of relationships between variables.

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