What is linear regression and how do I interpret its results?

What is linear regression and how do I interpret its results?

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. To interpret its results, you look at the coefficients, which indicate the change in the dependent variable for a one-unit change in the independent variable.

What do the coefficients mean in a linear regression model?

The coefficients represent the expected change in the dependent variable for each one-unit increase in the independent variable, holding all other variables constant. A positive coefficient indicates a direct relationship, while a negative coefficient indicates an inverse relationship.

How do I know if my linear regression model is good?

You can assess the model's goodness-of-fit using metrics like R-squared, which indicates the proportion of variance explained by the model, and by checking residual plots for patterns.

What are some common pitfalls in interpreting linear regression results?

Common pitfalls include assuming causation from correlation, ignoring multicollinearity, and failing to check for outliers that can skew results.