Linear regression (also called simple regression) is one of the most common techniques of regression analysis. Multiple regression is a broader class of regression analysis, which encompasses both ...

Simple linear regression is commonly used in forecasting and financial analysis—for a company to tell how a change in the GDP could affect sales, for example.

Here's how to run both simple and multiple linear regression in Google Sheets using the built-in LINEST function. No add-ons or coding required.

Multiple regression: This approach incorporates additional variables into your model, potentially improving accuracy by accounting for more factors affecting sales. To perform multiple regression ...

Multiple and Non-Linear Regression The variable you are trying to estimate is referred to as dependent, while the variable you use in the model to predict the dependent variable is called independent.

- Multiple linear regression formula. The equation for multiple linear regression extended to two explanatory variables (x 1 and x 2) is as follows: This can be extended to more than two explanatory ...

Figure 1: The results of multiple linear regression depend on the correlation of the predictors, as measured here by the Pearson correlation coefficient r (ref. 2). (a) ...

Linear Regression vs. Multiple Regression Example Consider an analyst who wishes to establish a relationship between the daily change in a company's stock prices and daily changes in trading volume .