[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”4.16″ global_colors_info=”{}” theme_builder_area=”post_content” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on” da_disable_devices=”off|off|off”][et_pb_row admin_label=”row” _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n
Google Sheets provides functions for many data analysis methods, including linear regression. This method is frequently used to quantify the relationship between a dependent and an independent variable. In other words, if you\u2019ve noticed a linear trend<\/a> in your data, you can forecast future values using the linear regression method. 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.<\/p>\n In this article, you will learn about the LINEST function and syntax, as well as how to use it for regression analysis in Google Sheets. You will see examples of how to use it when you have one explanatory variable \u2013 simple regression \u2013 and how to use it when you have multiple explanatory variables \u2013 multiple regression.<\/p>\n [\/et_pb_text][et_pb_text module_id=”linest-function-syntax” _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n The LINEST function has four parameters, and only the first is required.<\/p>\n =LINEST(known_data_y, [known_data_x], [calculate_b], [verbose])<\/p>\n This means you can use it to calculate the trend in values for your dependent variable even if you don\u2019t 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.<\/p>\n In the following sections, you will learn how to use the function for both simple and multiple linear regression.<\/p>\n [\/et_pb_text][et_pb_text module_id=”how-to-use-the-linest-function-in-google” _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Follow the instructions below to run a simple linear regression analysis in Google Sheets. For this example, I will use the sales amount as the response or dependent variable. The explanatory or independent variable is the amount spent on paid advertising.<\/p>\n 1.<\/strong> Open the Google Sheets file with the data for the explanatory and response variables.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-simple-data.webp” alt=”Linear regression in Google Sheets \u2014 sales_amount and paid_ads columns ready for the analysis” title_text=”Sales_amount and paid_ads columns ready for the analysis” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n 2.<\/strong> Type \u201c=LINEST(" in an empty cell and you will see the help pop-up. Select the array of cells with the known values for the response variable, "sales_amount".<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-simple-known-y.webp” alt=”Linear regression in Google Sheets \u2014 LINEST started with the sales_amount range as known_data_y” title_text=”LINEST started with the sales_amount range as known_data_y” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n 3.<\/strong> After the comma, select the range of known values for the independent variable, \u201cpaid_ads\u201d.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-simple-known-x.webp” alt=”Linear regression in Google Sheets \u2014 paid_ads range added as known_data_x” title_text=”Paid_ads range added as known_data_x” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n 4.<\/strong> I will set the last two parameters to \u201cTRUE\u201d, as I want \u201cb\u201d to be calculated, and I want to see more regression statistics than the slope and intercept. Remember to close the parenthesis and press Enter.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-simple-verbose.webp” alt=”Linear regression in Google Sheets \u2014 calculate_b and verbose set to TRUE in LINEST” title_text=”Calculate_b and verbose set to TRUE in LINEST” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n 5.<\/strong> That\u2019s it. All the regression statistics are available in your spreadsheet, but which is which? In the next step, I include labels for each of the statistics provided.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-simple-output.webp” alt=”Linear regression in Google Sheets \u2014 regression statistics returned by LINEST” title_text=”Regression statistics returned by LINEST” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n 6.<\/strong> Below, I have identified the statistics provided by the formula when the \u201cverbose\u201d parameter is \u201cTRUE\u201d. If the parameter is set to \u201cFALSE\u201d or left blank, only the \u201cslope\u201d and \u201cintercept\u201d statistics are provided.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-simple-labels.webp” alt=”Linear regression in Google Sheets \u2014 LINEST output labeled with the slope, intercept, standard errors, r\u00b2, F statistic, and sums of squares” title_text=”LINEST output labeled with the slope, intercept, standard errors, r\u00b2, F statistic, and sums of squares” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text module_id=”how-to-run-a-multiple-linear-regression” _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n Follow the instructions below to run multiple linear regression analysis in Google Sheets. I will build on the previous example by adding a second independent variable. The dependent variable is still the sales amount, but the explanatory variables are now the amount spent on paid advertising and the amount spent on sales salaries.<\/p>\n 1.<\/strong> Open the Google Sheets file with the data for the response variable and both explanatory variables.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-multiple-data.webp” alt=”Multiple linear regression in Google Sheets \u2014 sheet with sales_amount, paid_ads and sales_salaries” title_text=”Sheet with sales_amount, paid_ads and sales_salaries” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n 2.<\/strong> In an empty cell, type \u201c=LINEST(" and you will see the help pop-up. Select the array of cells with the known values for the response variable, "sales_amount".<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-multiple-known-y.webp” alt=”Multiple linear regression in Google Sheets \u2014 LINEST started with sales_amount as known_data_y” title_text=”LINEST started with sales_amount as known_data_y” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n 3.<\/strong> After the comma, select the range of known values for the independent variables, \u201cpaid_ads\u201d and \u201csales_salaries\u201d.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-multiple-known-x.webp” alt=”Multiple linear regression in Google Sheets \u2014 paid_ads and sales_salaries ranges added as known_data_x” title_text=”Paid_ads and sales_salaries ranges added as known_data_x” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n 4.<\/strong> The third parameter is set to \u201cTRUE\u201d, as I want \u201cb\u201d to be calculated. I also want to see all the regression statistics, so the last parameter is also \u201cTRUE\u201d. Remember to close the parenthesis and press \u201cEnter\u201d.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-multiple-verbose.webp” alt=”Multiple linear regression in Google Sheets \u2014 calculate_b and verbose set to TRUE” title_text=”Calculate_b and verbose set to TRUE” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n 5.<\/strong> That\u2019s it. The statistics for the multiple regression are available in your spreadsheet. In the next step, I will identify the statistics with labels.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-multiple-output.webp” alt=”Multiple linear regression in Google Sheets \u2014 LINEST output with #N\/A in the unused cells” title_text=”LINEST output with #N\/A in the unused cells” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n 6.<\/strong> Below, \u201cs.error\u201d is the standard error. The cells with \u201c#N\/A\u201d can be ignored, as there is no error: the cells are simply not needed.<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/static.sheetgo.com\/wp-content\/uploads\/2026\/09\/linear-regression-google-sheets-multiple-labels.webp” alt=”Multiple linear regression in Google Sheets \u2014 LINEST output labeled with coefficients, standard errors, and fit statistics” title_text=”LINEST output labeled with coefficients, standard errors, and fit statistics” align=”center” _builder_version=”4.22.2″ _module_preset=”default” width=”100%” global_colors_info=”{}” theme_builder_area=”post_content”][\/et_pb_image][et_pb_text module_id=”how-to-read-the-linest-output-array” _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n With “verbose” set to “TRUE”, LINEST returns five rows of statistics. Rows 1 and 2 have one column for each independent variable plus one for the intercept. Rows 3 to 5 only use the first two columns.<\/p>\n In the simple example, the slope is about 9.37, so each extra dollar spent on paid ads goes with about $9.37 more in sales. The r\u00b2 of 0.93 means the line explains 93% of the variation in sales.<\/p>\n In a multiple regression, LINEST lists the slopes in reverse order. The first value, 6.19, belongs to sales_salaries, the last x column, and 2.80 belongs to paid_ads. The #N\/A cells in rows 3 to 5 are just the columns those rows don’t use.<\/p>\n For a simple regression, r\u00b2 is also the square of the value the CORREL function<\/a> returns. To see the fitted line, plot the data in a scatter chart<\/a> and add a trendline. If you’d rather use a menu than a formula, the XLMiner Analysis ToolPak add-on<\/a> runs the same regression.<\/p>\n [\/et_pb_text][et_pb_text module_id=”conclusion” _builder_version=”4.22.2″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”]<\/p>\n As you have seen, linear regression analysis is easy using the LINEST function in Google Sheets. Whether you have one or multiple independent variables, you can quickly find the slope and the intercept.<\/p>\n However, you could also find those using the SLOPE and INTERCEPT functions. The difference is that LINEST can also provide a lot of additional regression statistics, which will allow you to perform a full analysis. These include standard errors for slope, intercept, and y-estimate, as well as the coefficient of determination, F statistic, degrees of freedom, and the regression and residual sums of squares.<\/p>\n You now know how to find simple and multiple linear regression in Google Sheets using the LINEST function. You also know how to identify the statistics represented by the function\u2019s output, so you can quickly interpret the results. To learn more about performing data analysis in Google Sheets<\/a>, check out our related article on how to perform what-if analysis in Google Sheets<\/a>.<\/p>\nLINEST function syntax<\/h2>\n
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How to use the LINEST function in Google Sheets (simple regression)<\/h2>\n
How to run a multiple linear regression in Google Sheets<\/h2>\n
How to read the LINEST output array<\/h2>\n
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Conclusion<\/h2>\n