Linear regression with a single predictor variable is known as simple regression. That is, if advertising expenditure is increased by one million Euro, then sales will be expected to increase by 23 million Euros, and if there was no advertising we would expect sales of 168 million Euros. If we use advertising as the predictor variable, linear regression estimates that Sales = 168 + 23 Advertising. In this case, our outcome of interest is sales-it is what we want to predict.
Each row in the table shows Benetton’s sales for a year and the amount spent on advertising that year.
The table below shows some data from the early days of the Italian clothing company Benetton. This post will show you examples of linear regression, including an example of simple linear regression and an example of multiple linear regression. Linear regression is also known as multiple regression, multivariate regression, ordinary least squares (OLS), and regression. For example, it can be used to quantify the relative impacts of age, gender, and diet (the predictor variables) on height (the outcome variable). Linear regression is commonly used for predictive analysis and modeling. Linear regression quantifies the relationship between one or more predictor variable(s) and one outcome variable.
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