Using Regression to Forecast in Microsoft Excel

Name: Using Regression to Forecast in Microsoft Excel
Rating: 4.6 (3 reviews)

Learn how to apply regression techniques in Excel to predict trends and make data-driven forecasts

Created byCreateMyExcel with Ben Scholl

Last updated 7/2025

English

What you'll learn

Learn how to choose the right regression method for different data types.
Apply simple linear regression to analyze straight-line trends in data.
Understand and use the regression equation to describe relationships.
Use the LINEST function to calculate best-fit values and interpret results.
Forecast future values using Excel tools like TREND and the Fill Handle.
Extend data trends using the Series command for linear projections.
Analyze sales and advertising data with case-based forecasting models.
Apply exponential, logarithmic, and power regressions for curved trends.
Use GROWTH and LOGEST functions for nonlinear trend forecasting.
Perform multiple regression analysis to track several variables at once.

Course content

1 section • 30 lectures • 1h 36m total length

Applying Regression to Track Trends and Make Forecasts3:08
Choosing a Regression Method2:08
Using Simple Regression on Linear Data3:12
Explore regression on linear data by linking independent and dependent variables. Add a trend line in Excel and view the line of best fit equation y = mx + b.
Understanding The Regression Equation7:18
Calculating Best- Fit Values Using LINEST4:44
Analyzing The Sales Versus Advertising Trend4:46
Making Forecasts3:17
Extending a Linear Trend With The Fill Handle1:13
Extend a linear trend in Microsoft Excel by using the fill handle to drag down and forecast future values from past data, using the generated regression equation.
Extending a Linear Trend Using The Series Command1:35
Forecasting with TREND1:48
Forecasting With LINEST1:46
Case Study; Trend Analysis & Forecasting for Seasonal Sales - Part 15:17
Case Study; Trend Analysis & Forecasting for Seasonal Sales - Part 23:27
Case Study; Trend Analysis & Forecasting for Seasonal Sales - Part 32:31
Case Study; Trend Analysis & Forecasting for Seasonal Sales - Part 41:55
Case Study; Trend Analysis & Forecasting for Seasonal Sales - Part 59:51
Using Simple Regression on Nonlinear Data2:51
Plotting an Exponential Trendline1:17
Exponential Trending and Forecasting using the GROWTH function1:38
Exponential Trending and Forecasting LOGEST Function3:35
Working with a Logarithmic Trend2:29
Plotting a Logarithmic Trendline1:48
Explore how to plot a logarithmic trendline in Microsoft Excel to forecast employee growth, compare models using r-squared, and display the equation for clear data insight.
Calculating Logarithmic Trend and Forecast Values2:25
Plotting a Power Trendline2:15
Working with a Power Trend1:44
Examine two power trends for forecasting in Excel, including y to the x to the power and y equals x to the negative 0.25 power, with separate-axis charts.
Calculating Power Trend and Forecast Values2:14
Using Polynomial Regression Analysis3:34
Calculating Polynomial Trend and Forecast Values8:47
Learn to compute polynomial trends and forecast values in Excel using regression equations, increasing orders, and the line estimate function for future profits.
Using Multiple Regression Analysis2:06
Explore multiple regression analysis in Excel to forecast units sold using factors such as advertising spend and list price, interpreting R-squared and regression equations.
Using Multiple Regression Analysis (cont.)2:16

Requirements

Microsoft Excel (Office 2021 or Microsoft 365) installed.
Basic knowledge of Excel (e.g., entering data, simple calculations).
Access to a computer with a stable internet connection.
No prior advanced Excel skills required; suitable for beginners.

Description

This course is designed for learners who want to apply regression analysis in Excel to uncover trends, analyze relationships, and forecast future outcomes with confidence. Using the powerful tools available in Microsoft Excel (Office 2021 and Microsoft 365), students will develop hands-on skills in both linear and nonlinear regression techniques, enabling them to make data-driven decisions across a variety of professional and academic contexts.

The course begins by exploring how to choose the most appropriate regression method based on data type and trends. Students will learn to use simple linear regression to model relationships, interpret the regression equation, and calculate best-fit values using functions such as LINEST and TREND. Through practical examples—such as analyzing the link between sales and advertising—students will forecast future values using the Fill Handle, Series command, and Excel’s built-in forecasting tools.

As the course progresses, learners will explore more complex models, including exponential, logarithmic, power, and polynomial regression. They’ll gain experience using Excel’s GROWTH, LOGEST, and other forecasting functions to model nonlinear data. The course concludes with an introduction to multiple regression analysis, allowing students to analyze how several variables interact in predicting outcomes.

By the end of this course, students will be able to select, apply, and interpret regression models in Excel to identify patterns and build reliable forecasts for real-world applications.

Who this course is for:

This course is for students learning data analysis using Excel.
Ideal for business professionals who need to forecast trends.
Great for marketers analyzing sales and advertising results.
Useful for analysts tracking seasonal or long-term changes.
Made for Excel users ready to apply regression in real-world tasks.
Perfect for finance or economics students studying predictive tools.
Designed for those comparing multiple regression types in Excel.
A fit for anyone modeling growth using exponential or power trends.
Tailored for learners working with both linear and nonlinear data.
Helpful for professionals needing accurate Excel-based forecasting.