How to Interpret Your WGU C207 Task 1 Regression Output

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How to Interpret Your WGU C207 Task 1 Regression Output

You’ve enabled the Analysis ToolPak, run the regression, and now you’re staring at three tables of numbers Excel generated for WGU C207 Task 1. This is the step where I see the most rubric points lost — not because students can’t run the tool, but because they don’t know which numbers in that output actually matter for the write-up, or how to translate them into the specific sentences the rubric is asking for.

This guide walks through exactly that: reading the output table, and turning each number into the sentence WGU expects.

The three tables Excel gives you

When you run Regression through the Analysis ToolPak, you get three separate tables:

1. Regression Statistics — includes R Square, Multiple R, and Standard Error
2. ANOVA — includes Significance F, degrees of freedom, and sum of squares
3. Coefficients table — includes your intercept, slope (X coefficient), standard errors, t-stats, and P-values

For Task 1, you’ll draw from all three, but the coefficients table and the R Square value are what most of your interpretation sentences will actually cite.

Reading R² (R Square)

R² tells you what percentage of the variation in your dependent variable is explained by your independent variable. It ranges from 0 to 1 (or is often expressed as a percentage).

Using the fictitious Crestview Regional Hospital example from the main Task 1 guide — a hospital examining wellness program participation rate against nurse attrition rate — an R² of 0.552 translates to:

Approximately 55.2% of the variation in nurse attrition rate can be explained by the wellness program participation rate, based on this regression model.

Two mistakes to avoid here: don’t call R² “accuracy” (it isn’t a measure of prediction accuracy in that sense), and don’t claim R² proves causation — it only describes how much variation the model accounts for statistically.

Reading the coefficients: slope and intercept

Your coefficients table gives you two numbers that define your regression line: the intercept and the slope (labeled with your X variable’s name).

Using the same fictitious example: intercept = 18.42, slope = -0.31. This translates into two separate sentences:

The regression equation is: Predicted Attrition Rate = 18.42 − 0.31 × (Participation Rate).

For every one-percentage-point increase in wellness program participation rate, nurse attrition rate is predicted to decrease by 0.31 percentage points, holding other factors constant.

The sign of the slope matters as much as the number itself — a negative slope means an inverse relationship (as one goes up, the other goes down), while a positive slope means they move together. Get the direction right in your sentence; a technically-correct magnitude with the wrong direction described in words is still a real error.

Reading the P-value and making the reject/fail-to-reject decision

This is the single most rubric-weighted sentence in the whole task, and the one I’d urge you to write first, before anything else in your interpretation section.

Your P-value lives in the coefficients table, in the row for your X variable (not the intercept row). Compare it against your stated significance level — almost always α = 0.05 for this task:

• P-value < 0.05 → reject the null hypothesis → statistically significant relationship
• P-value ≥ 0.05 → fail to reject the null hypothesis → not enough evidence of a relationship

With a fictitious P-value of 0.0002:

Because the P-value (0.0002) is less than the significance level (α = 0.05), we reject the null hypothesis. There is sufficient statistical evidence to conclude that wellness program participation rate has a significant effect on nurse attrition rate.

Notice this sentence does three things in order: states the comparison, states the decision (reject/fail to reject), and restates the practical conclusion in terms of your actual variables. Missing any one of those three pieces is a common way this section loses points even when the statistical logic is correct.

Related to the WGU C207 Task 1 Guide

WGU C207 Task 1: Complete Guide + Worked Example

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

How to Write a Null Hypothesis for WGU C207 Task 1

Common misinterpretations to avoid

• Confusing statistical significance with practical significance. A statistically significant relationship (low p-value) doesn’t automatically mean the effect is large or operationally important — that’s a separate judgment your business recommendation section should address.
• Treating a strong R² as proof of causation. Regression describes association, not cause and effect, regardless of how high R² is. This is worth stating explicitly in your limitations section.
• Reporting Significance F from the ANOVA table instead of the P-value from the coefficients table. For a simple linear regression with a single predictor, these values are mathematically equivalent, but reviewers expect you to cite the coefficients-table P-value specifically, since that’s the value tied to your actual hypothesis about β1.
• Rounding away meaningful precision. A P-value of 0.049 and 0.03 both round to “less than 0.05,” but reporting the actual value (not just “significant”) shows you’re reading the table rather than pattern-matching.

Third-Party Resources

• “Understanding the Null Hypothesis for Linear Regression” and related output-reading guides — Statology — plain-language walkthroughs of exactly these coefficient and p-value interpretation questions.
• Laerd Statistics — deeper reference on regression assumptions and output interpretation if you want to understand the “why” behind each number, not just how to report it.

Frequently Asked Questions

My R² is low (under 0.3). Does that mean I did something wrong? Not necessarily — a low R² just means your independent variable explains relatively little of the variation in your dependent variable. It’s still a valid result to report and interpret; you’d simply note in your limitations section that other unmeasured factors likely play a larger role.

Do I need to report every number in the output, or just the key ones? Focus your write-up on R², the slope and intercept, and the P-value for your X coefficient — those are what the rubric is checking for. You don’t need to narrate every cell in all three tables.

What’s the difference between “Multiple R” and “R Square” in the Regression Statistics table? Multiple R is the correlation coefficient (how strongly the two variables move together, from -1 to 1); R Square is that value squared, representing the proportion of variance explained. For Task 1’s write-up, R Square is the one you’ll cite most often.

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For the complete rubric breakdown, Excel walkthrough, and hypothesis-writing guidance for this task, see the full WGU C207 Task 1 guide.

If your output doesn’t look like what you expected, feel free to message me on WhatsApp — I’m happy to help you figure out what your specific numbers mean.

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.