A simple linear regression analysis is a statistical method that helps to predict the value of a dependent variable based on the value of an independent variable. It assesses the linear relationship between two continuous variables and provides insights into the relationship’s direction, magnitude, and statistical significance.
For instance, you can use simple linear regression to predict the sales of a product based on the advertising spend (i.e., your dependent variable would be “sales” and your independent variable would be “advertising spend”). You could also determine how much of the variation in sales can be explained by advertising spend. Similarly, you could use linear regression to predict the weight of a person based on their height (i.e., your dependent variable would be “weight” and your independent variable would be “height”). You could also determine how much of the variation in weight can be attributed to the person’s height.
Note that simple linear regression is also known as bivariate linear regression, and the dependent variable is also referred to as the outcome, target, or criterion variable. At the same time, the independent variable is also called the predictor, explanatory, or regressor […]