--- title: How do you calculate the MSE in a linear regression model? description: Mean squared error (MSE) measures the amount of error in statistical models. It assesses the average squared difference between the observed and predicted values. When a model has no error, the MSE eq --- [Skip to content](https://help.datasciencedojo.com/migration/how-do-you-calculate-the-mse-in-a-linear-regression-model#main-content) English Show submenu for translations [Customer portal](https://help.datasciencedojo.com/tickets-view?hsLang=en) ![DSD main logo.png\]](https://help.datasciencedojo.com/hs-fs/hubfs/DSD%20main%20logo.png?height=40&name=DSD%20main%20logo.png) - [Default HubSpot Blog](https://help.datasciencedojo.com/blog) Open main navigation Close main navigation - [Default HubSpot Blog](https://help.datasciencedojo.com/blog) - English Show submenu for translations - [Customer portal](https://help.datasciencedojo.com/tickets-view) - Go to datasciencedojo.com Go to datasciencedojo.com How can we help? - There are no suggestions because the search field is empty. 1. [Help Center](https://help.datasciencedojo.com/migration?hsLang=en) 2. [Machine Learning](https://help.datasciencedojo.com/migration/machine-learning?hsLang=en) 3. [Supervised](https://help.datasciencedojo.com/migration/machine-learning?hsLang=en#supervised) # How do you calculate the MSE in a linear regression model? Mean squared error (MSE) measures the amount of error in statistical models. It assesses the average squared difference between the observed and [predicted values](https://statisticsbyjim.com/glossary/fitted-values/). When a model has no error, the MSE equals zero. As model error increases, its value increases. The mean squared error is also known as the mean squared deviation (MSD). For example, in [regression](https://statisticsbyjim.com/regression/when-use-regression-analysis/), the mean squared error represents the average squared [residual](https://statisticsbyjim.com/glossary/residuals/)   ![Image depicting the relationship between the residuals and the mean squared error.](https://i0.wp.com/statisticsbyjim.com/wp-content/uploads/2017/04/residuals.png?resize=300%2C186&ssl=1) As the data points fall closer to the regression line, the model has less error, decreasing the MSE. A model with less error produces more precise predictions #### MSE Formula The formula for MSE is the following. ![formula for MSE.](https://i0.wp.com/statisticsbyjim.com/wp-content/uploads/2021/11/mse_formula.png?resize=246%2C78&ssl=1)   - [Data Science Bootcamp](https://help.datasciencedojo.com/migration/data-science-bootcamp?hsLang=en#main-content) - [Certificate](https://help.datasciencedojo.com/migration/data-science-bootcamp?hsLang=en#certificate) - [Logistics](https://help.datasciencedojo.com/migration/data-science-bootcamp?hsLang=en#logistics) - [Curriculum](https://help.datasciencedojo.com/migration/data-science-bootcamp?hsLang=en#curriculum) - [Target Audience](https://help.datasciencedojo.com/migration/data-science-bootcamp?hsLang=en#target-audience) - [Talk to an Advisor](https://help.datasciencedojo.com/migration/data-science-bootcamp?hsLang=en#talk-to-an-advisor) - [Prerequisites](https://help.datasciencedojo.com/migration/data-science-bootcamp?hsLang=en#prerequisites) - 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