What is the assumption of homoscedasticity in linear regression?

The residuals have constant variance across all levels of the independent variable
The residuals are normally distributed
The relationship between the independent and dependent variable is linear
The independent variables are uncorrelated

What is the assumption of homoscedasticity in linear regression?

Step 1: Understand what a regression model is. It is a way to predict a value based on other values.
Step 2: Know that in regression, we look at the differences between the predicted values and the actual values. These differences are called residuals or errors.
Step 3: Homoscedasticity is a term that means these residuals should have a consistent spread or variance.
Step 4: This means that no matter what value of the independent variable you are looking at, the size of the errors should be about the same.
Step 5: If the errors get bigger or smaller as the independent variable changes, that is called heteroscedasticity, which is not what we want in a good regression model.

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