💪Linear regression uses least squares to fit a line to data and calculate R-Squared.
📝R-Squared measures how much of the variation in one variable can be explained by another.
💡F-Value compares the variance explained by the model to the variance not explained.
🔮Degrees of freedom determine the number of parameters in the fit equation.
💼P-values help determine the statistical significance of R-Squared.
What is R-Squared?
—R-Squared measures the proportion of the variation in one variable that can be explained by another variable.
What is the F-Value?
—The F-Value compares the variance explained by the linear regression model to the variance not explained by the model.
How is R-Squared calculated?
—R-Squared is calculated by dividing the variation explained by the model by the total variation in the dependent variable.
What are degrees of freedom?
—Degrees of freedom determine the number of parameters that can vary in a statistical model.
How do p-values determine statistical significance?
—P-values indicate the probability of obtaining a result as extreme as observed, assuming the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis.
00:00Linear regression uses least squares to fit a line to data and calculate R-Squared.
15:00R-Squared measures how much of the variation in one variable can be explained by another.
19:58The F-Value compares the variance explained by the model to the variance not explained.
20:19Degrees of freedom determine the number of parameters in the fit equation.
23:30P-values help determine the statistical significance of R-Squared.
