# Simple Linear Regression Google Sheets Guide (Video + 2 Examples)

﻿﻿Not many people know that Google Sheets has functions for various data analysis methods and is not too complicated even for beginners. However, one of these popular Google Sheets tools is known as Linear Regression.

This article details everything you need to know about linear regression techniques in Google Sheets. We detail the syntax of the LINEST Google Sheets function, some examples of using linear regression in Google Sheets, and step-by-step instructions to fully understand Google Sheets’ useful linear regression features.

## What is Linear Regression in Google Sheets?

Before learning how linear regression in Google Sheets works, you need to understand what it is. Linear regression is a method of quantifying the relationship between one or more explanatory variables and one or more response variables.

In a nutshell, Linear Regression in Google Sheets answers the question, “How can I predict Y using X?” where X is the information you already have and Y is the information you need.

Suppose you want to sell your PC and want to know how much it will cost. You have information (X) about a PC and would like to know its selling price (Y.

Linear regression generates an equation that inputs a given number (X) and outputs the variable of interest.

Types of linear regression

There are two types of linear regression in Google Sheets. The first is Simple Linear Regression in Google Sheets and the second is Multiple Regression in Google Sheets. Both types of regression can be run in Google Sheets using the Google Sheets LINEST function. If you have only one explanatory variable, use simple linear regression. If you have more than one explanatory variable, use multiple regression.

## Use Cases of Linear Regression in Google Sheets

Now that we understand what linear regression is, we also need to understand when and where regression is needed. Regression can be used to answer whether and how one factor influences another or how the variables are related. For example, you can use this to determine how much experience and gender affect your salary.

Regression is also useful for predicting future values ​​in a dataset. For example, you can try to predict your household’s electricity consumption for the next hour, given the outdoor temperature, the time of day, and the number of people in that household.

It’s also worth mentioning that regression can be used in many fields, including economics, computer science, and social science.

As mentioned earlier, there are two types of linear regression. There is simple linear regression and multiple linear regression. To understand both of these, let’s look at two examples.

Let’s assume we want to understand the relationship between study time and exam scores. By studying for exams, you can earn exam scores. This relationship can be investigated using simple linear regression with learning time as the explanatory variable and test scores as the response variable.

To understand multiple linear regression, let’s assume that we want to know whether the number of hours spent studying and the number of mock exams taken affect a student’s score on a particular college entrance exam. To investigate this question, we can use multiple linear regression, taking study time and preliminary exams as explanatory variables and exam scores as response variables.

simply put, simple linear regression has only one x and y variable. Multiple regression has one y variable 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 parameter is required.

The syntax is:

=LINEST(known data y [known data x], [computation b], [details])

• known_data_y* (required): the known value of the response or dependent variable (y).

• known_data_x (optional): known values ​​of explanatory or independent variables (x).

• Calculate_b (optional): Indicates if the y-intercept (b) should be calculated. The default value is ‘TRUE’, which is the desired value for linear regression.

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

This means that even if you don’t have an independent variable, you can use it to calculate the trend of the dependent variable’s value. However, it can also be used when there are multiple independent variables to consider. This makes it a very flexible and useful feature.

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

This step-by-step guide uses the following table with linear data a trend explaining how to use the lINEST function to perform linear regression in Google Sheets.

This table shows how the year of the car affects the price. These two variables are used in a simple linear regression example. Additionally, this table shows how the age of this car affects maintenance costs. We will use this variable and price in our multiple regression example.

Let’s see how they work!

## Simple Linear Regression

Step 1: Add a table of different values ​​generated using the =LINEST function. Step 2: Generate regression statistics using the Google Sheets function LINEST. The screenshot above uses the following functions:

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

Find 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 of Known_data_x from B2:B12 to B2:C12 to include the maintenance cost variable. And done! I have successfully generated regression statistics for Google Sheets using simple linear regression and multiple regression methods.

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

If a user wants to run a regression analysis in Google Sheets, fortunately there is an easy way to do so,

The LINEST function in Google Sheets allows you to perform both simple linear and multiple regression on known values ​​of variables. Quickly find slopes, intercepts, and other regression statistics.

### Is Google Sheets Good for Data Analysis?

Google Sheets isn’t as effective as Excel for data analysis, but it’s still a very powerful tool that’s quickly closing the gap with the top spreadsheet programs.

## Conclusion

After discussing the linear regression technique in detail, it should be noted that linear regression is just one of several regression techniques available. There are several of these techniques for predictive modeling, of which we discussed Simple Regression and Multiple Linear Regression in Google Sheets. Linear regression is a simple but effective algorithm.

As with all Google Sheets tools, this feature may seem complicated at first glance, but once you get started, linear regression analysis in Google Sheets is actually pretty straightforward.