To be confident in our conclusions, we must meet three assumptions with linear regression: linearity, normalcy, and homoscedasticity.
For starters, linear regression requires a linear relationship between the independent and dependent variables. Because linear regression is susceptible to outlier effects, it's also important to look for them.
No, just because your observed variables don't match a normal distribution doesn't mean you have to alter them. The t-test and ANOVA in linear regression analysis do not assume normality for predictors (IV) or outcomes (ANOVA) (DV).
When we use linear regression to model the relationship between a response and a predictor, we make a few assumptions. These assumptions are basically requirements that must be met before we can draw conclusions from model estimates or use a model to make a forecast.
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