October 2020

When we are studying bivariate quantitative data (variables \(x\) and \(y,\)) we are interested in how one variable changes as the other changes. We may ask how much of the change in one variable can be attributed to the change in the other variable. Inherently, this question requires the development of some method or model that can measure the amount of change in the dependent variable that can be attributed to the model. When making such a measurement, the interest lies in the

At its core, linear regression seeks to find the best-fitting straight line that describes the relationship between a predictor variable (often denoted as X) and a response variable (often denoted as Y). In simpler terms, it shows how well the data fit a regression line or curve. The coefficient of determination, often symbolized as R2, is a statistic that measures the degree of variance for a dependent variable that’s predicted by an independent variable or variables in a regression model.