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

English [Auto],

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
Learn regression in Excel to track trends and forecast sales and profits. Explore simple and multiple regression, trend lines, best-fit equations, non-linear data, and limitations of qualitative methods.
Choosing a Regression Method2:08
Identify the regression method by understanding simple regression with one independent variable (linear or nonlinear), and multiple regression with two or more independent variables, to model relationships and forecast data.
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
Learn how to read and apply the regression equation in Excel, interpret y = mx + b and r-squared, and use trend to forecast sales by period.
Calculating Best- Fit Values Using LINEST4:44
Learn to compute best-fit line parameters in Excel using linest, extracting slope, intercept, and r-squared to forecast and pull statistics with index.
Analyzing The Sales Versus Advertising Trend4:46
Analyze the relationship between sales and advertising using linear regression in Excel, including trend lines, R squared, and the equation y equals mx plus b, to forecast future sales.
Making Forecasts3:17
Visualize and extend forecasts in Excel by adding a trend line to a regression model, display the equation and R squared, and forecast four future periods.
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
Extend a linear trend in Excel using the series command to forecast future periods, with autofill and trend options on the home ribbon.
Forecasting with TREND1:48
Explore forecasting with the trend function in Microsoft Excel, using known y's and x's to create a best-fit forecast and predict future quarters with new x values.
Forecasting With LINEST1:46
Forecast future results in Excel by using the LINEST function to obtain the slope and y-intercept, then apply y = mx + b and extend the forecast.
Case Study; Trend Analysis & Forecasting for Seasonal Sales - Part 15:17
Apply regression and trend analysis to seasonal sales in excel by building monthly data, a 12-month moving average, seasonal indexes, and a three-year forecast.
Case Study; Trend Analysis & Forecasting for Seasonal Sales - Part 23:27
Visualize sales seasonality with graphs to reveal the seasonal index, then compute trends and true trend for forecast; practice building the sales chart.
Case Study; Trend Analysis & Forecasting for Seasonal Sales - Part 32:31
De-season sales data using the monthly index table to remove seasonal effects and reveal normal performance. Apply the de-seasoned actuals in regression and forecast workbooks to project future sales.
Case Study; Trend Analysis & Forecasting for Seasonal Sales - Part 41:55
Explore case study forecasting by applying the trend function to DCs and actuals, convert to a seasonal trend using a monthly index, and assess correlation to guide future forecasts.
Case Study; Trend Analysis & Forecasting for Seasonal Sales - Part 59:51
Learn to forecast with regression in Excel by analyzing trend, seasonality, de-seasoning, and re-seasoning, then project future sales from the seasonally adjusted trend.
Using Simple Regression on Nonlinear Data2:51
Explore applying simple regression to nonlinear data by modeling exponential growth with an exponential trend, visualized with a scatterplot and smooth line in Microsoft Excel.
Plotting an Exponential Trendline1:17
Plot an exponential trendline in Excel to forecast growth, display its equation, and assess fit with R-squared, using the same steps as linear trendlines.
Exponential Trending and Forecasting using the GROWTH function1:38
Exponential Trending and Forecasting LOGEST Function3:35
Learn to forecast exponential trends in excel using the logest function by modeling y = b × m1^x, selecting known x and y, and interpreting r squared and related outputs.
Working with a Logarithmic Trend2:29
Explore the logarithmic trend in Excel, where the slope is steep early and flattens over time, and learn to forecast using LN and regression with a smooth line chart.
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
Forecast monthly values with a logarithmic regression in Excel using y = m log x + b, compute m and b with linest, then apply the line of best fit.
Plotting a Power Trendline2:15
Explore plotting a power trendline in Excel to model nonlinear relationships, compare with exponential and logarithmic trends, and use R-squared to forecast future values.
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
Learn to compute power trend forecasts in Excel by applying logarithms to units sold and list price, estimate B and M1, then derive M via exponentiation to forecast future sales.
Using Polynomial Regression Analysis3:34
Use polynomial regression in Excel to forecast fluctuating profits, trying different orders to capture rises and falls, and practice with the polynomial trends worksheet to compare R-squared values.
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
Forecast unit sold by applying multiple regression with advertising and list price, using known y's and x's to generate forecasts and r-squared statistics via linest.

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.