How to Do Regression Analysis in Excel with Multiple Variables

Running a regression analysis with multiple variables can feel daunting, but Excel has a powerful, built-in tool that makes it surprisingly straightforward. A multiple regression can help you understand how several different factors - like your ad spend, website traffic, and sales team's activity - collectively impact a key outcome, such as your total revenue. This article will walk you through the practical steps to enable Excel's Analysis ToolPak, prepare your data, run the analysis, and interpret the results to find meaningful insights.

What Exactly Is Multiple Regression Analysis?

At its core, multiple regression analysis is a statistical technique used to understand the relationship between one dependent variable and two or more independent variables. In simple terms, you’re trying to predict or explain one thing based on the influence of other things.

• Dependent Variable (Y): This is the main outcome you want to understand or predict. Examples include Monthly Sales, Customer Lifetime Value, or Website Conversion Rate.
• Independent Variables (X1, X2, X3...): These are the factors you believe might have an impact on your dependent variable. Examples include Ad Spend, Number of Sales Calls, Email Open Rate, or a product's price.

For example, an e-commerce store might want to predict its monthly sales (the dependent variable). They could use independent variables like their monthly ad spend on Facebook, their spend on Google Ads, the number of emails sent to their subscriber list, and the number of blog posts they published. A multiple regression analysis would not only help them build a model for making future predictions but also show which marketing channel has the most impact, giving them confidence over where they should allocate their future budget.

Prepare Your Data for Analysis

Before you can run a regression, your data needs to be structured in a way that Excel understands. This is the most crucial step, as poor data organization will lead to errors or incorrect results.

Your data should be organized in columns, with each variable having its own column. The column for your dependent variable (what you're trying to predict) can be anywhere, but your independent variables (the factors you're testing) must be in adjacent, contiguous columns. Excel requires this to run the analysis correctly.

An Example of Well-Structured Data:

Let's say a real estate company wants to predict house prices. Their data might look like this:

A quick reminder on your dataset: Everything needs to be numerical. If you have categorical data like "Yes/No," you should convert it into a numerical format, such as 1 for "Yes" and 0 for "No," also known as "dummy variables," for them to be used as part of your dataset for this analysis. Once your data is ready, you'll want to ensure the feature we are going to use is available as an Excel Add-in.

Enable the Data Analysis ToolPak Add-in

Excel’s regression analysis function isn’t enabled by default. It’s part of a special set of tools called the Analysis ToolPak, which you need to activate first. Don't worry, it’s a standard Excel AddIn that you can enable with just a few clicks.

For Windows Users:

1. Click on File in the top-left corner, and then select Options at the bottom of the left menu.
2. In the Excel Options dialogue box, click on Add-ins on the left.
3. Near the bottom of the window, you'll see a drop-down menu next to "Manage." Make sure Excel Add-ins is selected, and then click Go...
4. In the Add-ins box that appears, check the box for Analysis ToolPak and click OK.

For Mac Users:

1. Click on Tools in the top menu bar.
2. Select Excel Add-ins... from the dropdown menu.
3. In the Add-ins box that appears, check the box for Analysis ToolPak and click OK.

Once you’ve done this, you'll see a new button called Data Analysis appear under the Data tab in your Excel ribbon. Now you're ready to perform the regression analysis.

How to Do Regression Analysis in Excel with Multiple Variables

Now, to the important part: performing the regression. The following steps below will use real estate prices.

Step 1: Open the Data Analysis Window

Navigate to the Data tab and click on the Data Analysis button on the far right. This brings up a window listing various analysis tools.

Step 2: Select the Regression Tool

Scroll down the list and select Regression, then click OK. This opens the regression dialog box where you'll configure your analysis.

Step 3: Define Your Y and X Ranges

This is where you tell Excel what variables to analyze.

• Input Y Range: In this space, select only your single-column data containing your dependent variable. So, we'll choose the “House Price” column.
• Input X Range: For the X axis, select multiple columns containing the data of your independent variables, since they need to be adjacent, select all of them at once. Hence, we'll pick the “Square Footage” and all other additional variables we prepared our dataset with.
• Labels: Make sure this is check-marked if you've included your table's column headings. Your regression output tables will become easier to read using this option. Otherwise, Excel will treat your headers as another set of numerical data and it will end in an error.

Step 4: Configure the Output

You’ll decide next how to show the results of your report.

• Confidence Level: For most analyses, 95% is what is recommended, as it allows your output to show a standard confidence interval.
• Output options:
• Residuals section: For starters, you can go on with this analysis without checking any of the boxes here. Although they are invaluable for specialists running this analysis, for learning and practicing for the first time, you may avoid going further with these statistical analysis details.

Step 5: Run the Report Generation

After clicking OK, you can expect from Excel a complete regression results report that may expand from a few cells of your worksheet to beyond your regular monitor viewpoint. For your first regression analysis practice and as part of the instructions for easy result readability of this tutorial article, outputting this on a new worksheet is the recommended option for starters and what we'll continue our explanations with to facilitate a seamless data analysis interpretation practice in the best format Excel can prepare for you.

Decoding Your Multiple Regression Output

Excel's regression output is packed with information, but it can be confusing and intimidating if you don't know what the numbers mean for your statistical information. For the following section, you will know exactly what to do upon generating an automated report and that these sections and their sub-sections will suffice to go on with your regression analysis interpretation task:

The "Regression Statistics” Section

This main table measures how well your model fits and how trustworthy it is.

• R Square: In simple terms, this statistic shows you the proportion that your independent variables account for in relation to your dependent variable. Values range from 0 to 1, and the closer to 1, the stronger your model is. In our hypothetical example, you might expect an R Square value of "89%", indicating a significant predictive capability.
• Adjusted R Square: This is a more precise version that accounts for each variable you’ve added, adjusting the overall effect to prevent an inflated or misleading model strength. It’s crucial for ensuring statistical results accurately reflect the data used.

The "ANOVA" Section

Short for "Analysis of Variance,” this part checks the statistical significance of your independent variables. The crucial value here is:

• Significance F: This number represents the p-Value of the linear combination of variables used. If this is lower than 0.05, your model is considered statistically significant, indicating reliability for decision-making.

The "Coefficients and P Values” section

This section is essential for business professionals or managers seeking insights from advanced data reports.

• Coefficients: These values show how a one-unit increase in each independent variable affects the dependent variable, assuming others are held constant. For example, with house prices, a coefficient might indicate how much the price increases per additional square footage.
• P Value: This indicates the statistical significance of each independent variable. A value less than 0.05 confirms the reliability of the variable within your model.

You can combine these sections into a regression modeling equation for predictions or hypothetical scenarios. In our real estate example, it might look like:

House Price = $50,500 + ($75 * Square Footage) + ($35,000 * Nb. of Bedrooms) - ($300 * Age of House)

Final Thoughts

By using the “Analysis ToolPak,” you leverage Excel's built-in functionalities for advanced reporting. With guides like this, you can interpret automated reports and extract valuable insights. Manual statistical reporting can be complex, but it’s essential for professionals in data analysis or business intelligence. Tools like Graphed provide intuitive solutions, creating comprehensive dashboards and helping teams stay informed across platforms, aligning with modern business standards.