In this comprehensive guide, we will explore everything you need to know about the FORECAST.LINEAR function in Excel. The FORECAST.LINEAR function is a powerful tool that allows you to predict future values based on historical data. By using linear regression, this function can help you make informed decisions and forecasts in various fields, such as finance, sales, and inventory management. In this article, we will cover the syntax, examples, tips and tricks, common mistakes, troubleshooting, and related formulae for the FORECAST.LINEAR function.


The syntax for the FORECAST.LINEAR function is as follows:

=FORECAST.LINEAR(x, known_y’s, known_x’s)


  • x is the data point for which you want to predict a corresponding y-value.
  • known_y’s is the range of dependent y-values (historical data).
  • known_x’s is the range of independent x-values (historical data).

It is important to note that the known_y’s and known_x’s ranges must have the same number of data points, and they should be formatted as either horizontal or vertical arrays.


Let’s look at some examples of how to use the FORECAST.LINEAR function in Excel.

Example 1: Suppose you have historical sales data for the past six months and want to predict sales for the next month. Your known_x’s range represents the month numbers (1 to 6), and your known_y’s range represents the sales figures for each month. To predict sales for the 7th month, you would use the following formula:


Example 2: Imagine you have data on the number of visitors to your website and the corresponding revenue generated for the past 12 months. You want to predict the revenue for a month with 10,000 visitors. In this case, your known_x’s range represents the number of visitors, and your known_y’s range represents the revenue. The formula would be:

=FORECAST.LINEAR(10000, C2:C13, B2:B13)


Here are some tips and tricks to help you get the most out of the FORECAST.LINEAR function:

  1. Ensure that your historical data is accurate and reliable. The accuracy of the FORECAST.LINEAR function depends on the quality of the data you provide.
  2. Consider using a scatter plot to visualize the relationship between your known_x’s and known_y’s data points. This can help you identify any outliers or trends that may affect your forecast.
  3. Remember that the FORECAST.LINEAR function assumes a linear relationship between the x and y values. If your data exhibits a non-linear relationship, consider using other forecasting methods or functions, such as FORECAST.ETS or TREND.
  4. Keep in mind that the FORECAST.LINEAR function is sensitive to outliers. If your data contains extreme values, consider using a different forecasting method or removing the outliers before using the function.

Common Mistakes When Using FORECAST.LINEAR

Here are some common mistakes to avoid when using the FORECAST.LINEAR function:

  1. Using different-sized ranges for known_y’s and known_x’s. Ensure that both ranges have the same number of data points.
  2. Using non-numeric data in the known_y’s and known_x’s ranges. The FORECAST.LINEAR function requires numeric data to perform calculations.
  3. Attempting to forecast too far into the future. The accuracy of the FORECAST.LINEAR function decreases as you try to predict values further away from your historical data.

Why Isn’t My FORECAST.LINEAR Working?

If your FORECAST.LINEAR function isn’t working as expected, consider the following troubleshooting steps:

  1. Double-check your formula syntax, ensuring that you have correctly entered the x value, known_y’s range, and known_x’s range.
  2. Verify that your known_y’s and known_x’s ranges have the same number of data points and contain only numeric values.
  3. Inspect your data for outliers or extreme values that may be affecting the accuracy of the forecast.
  4. Consider whether a linear relationship is appropriate for your data. If not, explore alternative forecasting methods or functions.

FORECAST.LINEAR: Related Formulae

Here are some related formulae that you may find useful when working with the FORECAST.LINEAR function:

  1. FORECAST.ETS: This function uses exponential smoothing to forecast future values based on historical data. It is particularly useful for data with seasonal patterns or trends.
  2. TREND: This function calculates the linear trend by fitting a straight line through the known_y’s and known_x’s data points. It can be used to predict future values or to fill in missing data points.
  3. GROWTH: This function calculates the exponential growth curve through the known_y’s and known_x’s data points. It is useful for predicting future values when the relationship between the variables is exponential.
  4. LINEST: This function returns the parameters of the linear regression line, such as the slope and intercept, for the known_y’s and known_x’s data points. It can be used for more advanced statistical analysis.
  5. LOGEST: This function returns the parameters of the exponential regression curve for the known_y’s and known_x’s data points. It is useful for more advanced statistical analysis when the relationship between the variables is exponential.

By understanding the FORECAST.LINEAR function and its related formulae, you can harness the power of Excel to make accurate predictions and informed decisions based on your historical data. With practice and attention to detail, you’ll become an expert at using this versatile function in no time.


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