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How to Run a Linear Regression in Excel for WGU C207 Task 1

If you’ve read through the full WGU C207 Task 1 guide and understand what the task is asking for, the next hurdle is usually mechanical: actually getting Excel to produce the regression output you need. This is the part I get the most follow-up questions about, because the instructions assume you already know where the Analysis ToolPak lives — and if you’ve never used it, that’s not obvious at all.

This walkthrough covers exactly that: enabling the ToolPak, running the regression, and reading the raw output table well enough to know you did it correctly before you start writing anything.

Step 1: Confirm you have desktop Excel, not Excel Online

This trips up more students than anything else in Task 1. The Analysis ToolPak’s Regression tool is not available in Excel for the web — you can view a regression that was already run, but you can’t create one. If you’ve only ever used the browser version of Excel through your WGU portal or a shared Google/Microsoft account, you’ll need the desktop application instead. WGU students get free access to Microsoft 365 desktop apps through the school’s Office 365 benefit — install the desktop version of Excel before doing anything else.

Step 2: Enable the Analysis ToolPak

The ToolPak ships with every version of Excel but isn’t turned on by default. To enable it:

On Windows:

Click File > Options
Select Add-ins from the left sidebar
In the Manage box at the bottom, make sure “Excel Add-ins” is selected, then click Go
Check the box next to Analysis ToolPak, then click OK

On Mac:

From the top menu bar, go to Tools > Excel Add-ins
Check the box next to Analysis ToolPak, then click OK

If Analysis ToolPak doesn’t appear in the list at all, click Browse to locate it manually, or select Yes if Excel prompts you to install it. Once enabled, you’ll see a new Data Analysis button under the Data tab — that’s your confirmation it worked.

Step 3: Set up your data correctly before you run anything

Excel’s regression tool has one quirk that causes a lot of avoidable frustration: your independent variable (X) and dependent variable (Y) don’t need to be in any particular column order, but each variable’s data needs to be in a single contiguous column. If your dataset has extra columns in between the ones you need, either rearrange your columns first or copy the two you need into a fresh, adjacent pair of columns before running the tool.

For the Task 1 scenario, that means:

One column for your independent variable (e.g., wellness program participation rate)
One column for your dependent variable (e.g., nurse attrition rate)
A header label in row 1 of each column, if you plan to check the “Labels” box later

Step 4: Run the regression

On the Data tab, click Data Analysis (on the far right of the ribbon)
Select Regression from the list, then click OK
In the Input Y Range field, select your dependent variable column (including the header if you’re using labels)
In the Input X Range field, select your independent variable column
Check the Labels box if your columns have header text in row 1
Under Output options, choose New Worksheet Ply so your output doesn’t overwrite your source data
Click OK

Excel will generate a new worksheet with three tables: Regression Statistics, ANOVA, and a coefficients table. That coefficients table is where your slope, intercept, and p-values live — the numbers you’ll actually be interpreting and writing about in Task 1.

A worked example: Crestview Regional Hospital

To make this concrete, here’s how it looks using the same fictitious Crestview Regional Hospital scenario used in the main Task 1 guide; a hospital tracking the relationship between its nurse wellness program participation rate (X) and its nurse attrition rate (Y) across 36 months.

After selecting Y Range = attrition rate column, X Range = participation rate column, checking Labels, and running the tool, the output includes:

Statistic	Value
R Square	0.552
Multiple R (correlation coefficient)	-0.743
Intercept	18.42
X Coefficient (slope)	-0.31
P-value (X coefficient)	0.0002
Degrees of Freedom	34

Reading this table: the negative slope (-0.31) tells you attrition rate decreases as participation rate increases, the R² of 0.552 tells you the model explains about 55% of the variation in attrition rate, and the p-value well below 0.05 tells you this relationship is statistically significant — not just something that happened by chance in this particular sample.

(These numbers are illustrative only — your actual output will be generated from your own student-specific dataset and will look different. The point of this example is to show you what a correctly run regression output looks like structurally, not to hand you numbers to reuse.)

Related to the WGU C207 Task 1 Guide

WGU C207 Task 1: Complete Guide + Worked Example

How to Interpret Your WGU C207 Task 1 Regression Output

How to Write a Null Hypothesis for WGU C207 Task 1

Common mistakes that mess up the output

Selecting the wrong range — including a stray blank row or an extra column shifts every downstream calculation. If your R² looks unreasonably low or the tool throws an error, check your ranges first.
Forgetting to check Labels when your columns have headers — Excel will try to treat your header text as a data point, which throws off every statistic in the output.
Running the regression more than once and mixing up which output is current — each run creates a new sheet or overwrites the previous one depending on your output setting. Label or date your output sheet so you know which version you’re writing about.
Confusing “Significance F” in the ANOVA table with the “P-value” in the coefficients table — for a simple linear regression with one predictor, these will be nearly identical, but the one you cite in your write-up is the P-value next to your X variable’s coefficient.

Third-Party Resources

Use the Analysis ToolPak to perform complex data analysis — Microsoft’s own documentation on what each ToolPak tool does and how the Regression tool specifically works.
Load the Analysis ToolPak in Excel — step-by-step enabling instructions for both Windows and Mac, if the steps above don’t match what you’re seeing.

Frequently Asked Questions

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“text”: “Google Sheets doesn’t include the Analysis ToolPak. While Sheets has its own regression capabilities through add-ons or formulas, Task 1’s expected output format is built around Excel’s specific ToolPak layout, so it’s worth using desktop Excel if at all possible.”
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Do I need Excel for this, or can I use Google Sheets? Google Sheets doesn’t include the Analysis ToolPak. While Sheets has its own regression capabilities through add-ons or formulas, Task 1’s expected output format is built around Excel’s specific ToolPak layout, so it’s worth using desktop Excel if at all possible.

What if my school laptop won’t let me install the ToolPak? Try the Mac/Windows steps above first — the ToolPak is usually already present, just not enabled, rather than missing entirely. If your IT-managed device blocks add-in installation, WGU’s Office 365 benefit lets you install Excel on a personal device instead.

My R² came out very low or negative-looking. What did I do wrong? A properly run linear regression will never produce a negative R², so a strange result almost always traces back to a range-selection error — most often including a header row as data, or selecting a column that isn’t fully contiguous with your other variable.

For the complete rubric breakdown, business-question framing, and null hypothesis writing guidance for this task, see the full WGU C207 Task 1 guide.

If you’re stuck on your specific output, feel free to message me on WhatsApp; happy to help you make sense of what your numbers are telling you.

Dan Palmer, MBA, writes WGU MBA course guides for Gradevia, focusing on the quantitative and analytics-heavy courses (C207, C211, C213, C214). Connect on LinkedIn.