In this tutorial,` how to perform multiple linear regression in Excel.

Multiple regression, often known as multiple linear regression, is a mathematical technique that makes statistical predictions about the outcome of a dependent variable using a number of independent variables. This method can be used to resolve crucial business issues, reach sound financial conclusions, and carry out other data-driven tasks.

Finding the linear relationship between the independent factors and the dependent variable is one of the main aims of employing multiple linear regression. The components of the study that you can alter and control, known as independent variables, have an impact on how the dependent variable changes.

Once ready, we’ll get started by utilizing real-world examples to show you how to perform multiple linear regression in Excel.

Table of Contents

## Perform Multiple Linear Regression in Excel

Before we begin we will need a group of data to perform multiple linear regression in Excel.

### Step 1

First, you need to have a clean and tidy group of data.

### Step 2

To perform multiple linear regression, we can simply click â€˜Dataâ€™, then click â€˜Data Analysisâ€™.

### Step 3

A pop-up box will then appear. We then select â€˜Regressionâ€™ and click â€˜OKâ€™.

### Step 4

We then need to input some information to perform the multiple linear regression.

### Step 5

Once you pressed â€˜OKâ€™, the regression will generate a summary output.

## Interpret Multiple Linear Regression in Excel

The most important figures in the output should be interpreted as follows:

R Square: 0.776. This is called the coefficient of determination. The explanatory variables explain the proportion of the variance of the response variable. In this example, his 77.6% of the variation in exam performance can be explained by the number of hours he practiced and the number of piano lessons he took.

Standard error: 5.113. This is the average distance that observations deviate from the regression line. In this example, the observations are on average 5.113 units away from the regression line.

F: 41.67. This is the overall F statistic for the regression model, calculated as regression MS / residual MS.

Significance F: 3.13463. This is the p-value associated with the overall F statistic. It tells us whether or not the regression model as a whole is statistically significant. In other words, it tells us if the two explanatory variables combined have a statistically significant association with the response variable.