Not many people know that Google Sheets provides functions for many data analysis methods, and they aren’t that complex, even for beginners. But one of these widely known Google Sheets tools is known as the Linear Regression method.

This article will detail everything you need to know about the Google Sheets linear regression method. We will dive into the syntax of the LINEST Google Sheets function, some use cases of linear regression in Google Sheets alongside a step-by-step explanation to help you gain a thorough understanding of the useful linear regression Google Sheets functionality.

## What is Linear Regression in Google Sheets?

Before we learn how linear regression on Google Sheets works, we must figure out what it is. Linear regression is a method for quantifying the relationship between one or more explanatory variables and one or more response variables.

In a nutshell, Google Sheets linear regression answers the question, “How can I use X to predict Y?” where X is information you already have, and Y is information you want.

Assume you want to sell a PC and want to know how much you can get for it. You have information about the PC, which is your X, and you want to know the selling price, which is your Y.

Linear regression generates an equation in which you enter your given numbers (X), and it outputs the desired variable.

Types of Linear Regression

In Google Sheets, there are two types of linear regression. The first is Simple Linear Regression in Google Sheets, and the second is Multiple Regression in Google Sheets. Both types of regressions can be performed in Google Sheets using the LINEST function in Google Sheets. When there is only one explanatory variable, we use simple linear regression; when there are two or more explanatory variables, we use multiple linear regression.

## Use Cases of Linear Regression in Google Sheets

Now that we know what linear regression is, you must also understand where and when you will need regression. You can use regression to answer whether and how some factors influence others or how the variables are related. For example, you can use it to determine to what extent experience or gender impacts salaries.

Regression is also useful when you want to forecast the future values for a data set. For example, you could try to predict a household’s electricity consumption for the next hour, given the outdoor temperature, time of day, and the number of residents in that household.

It is also worth mentioning that regression can be used in many different fields, such as economics, computer science, social sciences, and many more.

As mentioned before, there are two types of linear regression. There is simple and multiple linear regression. To understand both of these, we will look at two examples.

Let’s assume we want to understand the relationship between hours studied and exam score. By studying for an exam, you receive the exam score. We can investigate this relationship using simple linear regression with hours studied as an explanatory variable and exam score as a response variable.

Now to understand Multiple Linear Regression, let’s assume we want to know if the number of hours spent studying and the number of practice exams taken affects a student’s score on a specific college entrance exam. This is where Multiple linear regression can be used to investigate this matter, with hours studied and prep exams taken as explanatory variables and exam score as a response variable.

To put it into simpler words, Simple linear regression has only one x and one y variable. Multiple linear regression has one y and two or more x variables.

## Linear Regression Google Sheets Syntax

Let’s look at the syntax of the LINEST function.

The LINEST function has four parameters, and only the first is required.

The syntax is as follows:

=LINEST(known_data_y [known_data_x], [calculate_b], [verbose])

• known_data_y* (Required): The known values for the response or dependent variable (y).

• known_data_x (Optional): The known values for the explanatory or independent variable (x).

• calculate_b (Optional): Indicates whether the y-intercept (b) should be calculated. The default value is “TRUE”, which is what we want for linear regression.

• verbose (Optional): Indicates whether you want additional regression statistics or just the slope and intercept. The default value is “FALSE”.

This means you can use it to calculate the trend in values for your dependent variable even if you don’t have an independent variable. However, you can also use it when you have multiple independent variables that need to be considered. This makes it a very flexible and useful function.

## Linear Regression Google Sheets Step-by-Step Guide

For this Step-by-Step guide, I will use the following table that contains data with a linear

trend to explain how to do linear regression in Google Sheets using the LINEST function.

This table shows how the age of a car affects its price, and these two variables will be used in the simple linear regression example. In addition, this table also shows how the age of this car affects its maintenance cost; we will use this variable and the price in the multiple regression example.

So, let’s see how they work!

## Simple Linear Regression

**Step 1: **Add a table for the different values that will be generated using the **=LINEST** function.

**Step 2: **Use the function **LINEST** in Google Sheets to generate regression statistics.

In the screenshot above, I have used the function:

=LINEST(A2:A12,B2:B12,TRUE,TRUE)

to find the regression statistics for the variables **Car Age** and **Car Price.**

## Multiple Regression

**Step 1: **Add another column to the right of column G.

**Step 2: **Change the cell range for * Known_data_x* from

*to*

**B2:B12***in order to include the*

**B2:C12****Maintenance Cost**variable.

And done! You’ve successfully generated Google Sheets regression statistics using Simple Linear regression and Multiple Regression methods.

## Frequently Asked Questions:

### Can You Run Regression Analysis in Google Sheets?

If a user wants to run regression analysis in Google Sheets, there is fortunately a simple method for that,

The LINEST function in Google Sheets allows you to perform both simple and multiple linear regression on the known values for your variables. You can quickly find the slope and the intercept, as well as other regression statistics.

### Is Google Sheets Good for Data Analysis?

Although Google Sheets is less effective than Excel for data analysis, it is still a very powerful tool that is closing the gap with the top spreadsheet program quickly.

## Conclusion

After going through everything in detail about the linear regression method, we must note that Linear regression is only one of several regression techniques available. In predictive modeling, there are several types of these techniques, of which we have just discussed simple and multiple linear regression in Google Sheets. Linear regression is a simple but effective algorithm.

Just like every other Google Sheets tool, this feature may seem complicated just by looking at it but once you get practical with it, linear regression analysis in Google Sheets is actually pretty simple.

Hope this article answered all your questions about Linear Regression.

**Additional Resources**

I have listed some additional Google Sheets functionalities that are related to the linear regression Google Sheets function below. These amazing functionalities help bolster your data analysis and help you become more efficient.