This guide will discuss how to perform multiple linear regression in Excel.
Excel is an excellent tool to use when we want to easily perform statistical calculations and data analysis. Since it has built-in functions and data analysis tools, we can perform different types of data analysis. However, we will only be focusing on multiple linear regression.
So multiple linear regression is a mathematical technique that uses several independent variables to make statistical predictions about the outcome or result of a dependent variable.
Moreover, it is a statistical technique we utilize to understand the relationship between two or more explanatory variables and one response variable. So the main goal of multiple linear regression is to identify the linear association or relationship between the independent and dependent variables.
When we only have one explanatory variable or independent variable, it would be better for us to perform a simple linear regression instead of multiple linear regression.
Furthermore, there are many benefits to performing multiple regression in Excel. Firstly, we can obtain better predictive insights about our data since Excel returns a comprehensive output. Secondly, we can improve our decision-making and problem-solve using multiple regression information.
Let’s take a sample scenario wherein we must perform multiple linear regression in Excel.
Suppose you surveyed the impact of the number of hours spent studying and the number of prep tests taken on the final exam score of a student. To determine the relationship among the variables, you performed a multiple linear regression in Excel.
In this case, the number of hours studied, and the number of prep tests are the explanatory or independent variables, while the final exam score is the response or dependent variable.
Great! Now we can move on and dive into a real example of performing multiple linear regression in Excel.
A Real Example of Performing Multiple Linear Regression in Excel
Let’s say we have a data set regarding the sales performance of 10 employees. Firstly, we have the number of hours worked and the number of flyers given to customers, which serve as our independent variables. Secondly, we have the number of sales made, which is our dependent variable.
So our initial data set would look like this:
Essentially, multiple linear regression handles two types of variables: the independent variable and the dependent variable. So the independent variables are the factors or elements we can change or control in the data set.
On the other hand, dependent variables are the factors or elements that get affected by the changes made to the independent variables. Additionally, there is a formula for calculating multiple linear regression.
So the formula is Y = ß0 + ß1x1 + ß2x2 + ... + ßpxp wherein y refers to the dependent variable, and ß0 refers to the y value when every independent variable equals zero. Then, x1, x2, and xp refer to the independent variables, while ß1, ß2, and ßp refer to the estimated regression coefficients.
Luckily, we can easily perform multiple linear regression in Excel using the data analysis tool found in the Data tab. So we would simply input our independent variables, the number of hours worked, and the number of flyers given as our X range.
Then, our dependent variable, the number of sales made, is our Y range. Afterward, Excel will automatically return a summary output based on the regression performed.
In the summary output, we can find the R square value, the standard error value, the F statistic, the significance of F, the P values, the coefficients, the estimated regression equation, and many more.
Firstly, the r square value is the proportion of the variance in the dependent variable explained by the independent variables. Next, the standard error refers to the average distance the observed values fall from the regression line.
Additionally, the significance of F will determine whether the regression model as a whole is significant or not. In simpler terms, it explains whether the independent variables combined have a statistically significant relationship with the dependent variable.
Then, the p values will individually tell us whether each independent variable is statistically significant or not. And the coefficients determine the average expected change in the dependent variable when we assume the other independent variable stays constant.
Lastly, we can utilize the coefficients found in the returned summary output to create an estimated regression equation. And this will help us calculate the expected number of sales for an employee given the number of hours worked and the number of flyers given.
So our final data set would look like this:
You can make your own copy of the spreadsheet above using the link attached below.
Amazing! Now we can discuss the process of how to perform multiple linear regression in Excel.
How to Perform Multiple Linear Regression in Excel
In this section, we will explain the step-by-step process of how to perform multiple linear regression in Excel. Furthermore, each step has detailed instructions and pictures to help you.
1. Firstly, we need to add the Data Analysis tool in Excel. To do this, we will go to File and select More at the bottom. Then, we will click Options in the dropdown menu.
2. Secondly, we will go to Add-ins and select Analysis ToolPak. Next, we will click Go beside Manage.
3. Thirdly, we will check the box beside Analysis ToolPak and click OK to apply the changes.
4. Once we have successfully added the Data Analysis tool, we can now perform multiple linear regression. So we will select our entire data set and go to the Data tab. Next, we will click Data Analysis found in the Analysis section.
5. In the Data Analysis window, we will choose Regression. Then, we will click OK.
6. Afterward, we need to input the necessary details. First, we will input the dependent variable in the Input Y Range section. Second, we will place the two independent variables in the Input X Range section. Third, we will check the box beside Labels.
Next, we will select a new cell where we want to display the regression results. Lastly, we will press OK to apply the changes.
7. And tada! We have successfully performed multiple linear regression in Excel.
And that’s pretty much it! We have discussed how to perform multiple linear regression in Excel. Now you can apply this method in your work whenever you need to determine the relationship between two or more independent variables and one dependent variable.
Are you interested in learning more about what Excel can do? You can now use the various other Microsoft Excel formulas available to create great worksheets that work for you. Make sure to subscribe to our newsletter to be the first to know about the latest guides and tutorials from us.
