WGU C207 Task 1: Complete Guide + Worked Example (Linear Regression Analysis)

Updated on: July 7, 2026 

What WGU C207 Task 1 Actually Tests

C207 (Data-Driven Decision Making) has three graded pieces: Task 1 (linear regression), Task 2 (decision tree analysis), and an Objective Assessment. Task 1 fails more students than Task 2 because it requires running a real statistical model in Excel and then interpreting numbers that are different for every student — copying someone else’s R² or p-value is the fastest route to an originality flag.

The scenario is fixed: a hospital tracks 36 months of nurse program participation rate (X) against nurse attrition rate (Y) and wants to know whether the two are related.

The Assignment

Introduction

Managers are required to organize, interpret, and display data that is reliable and relevant to the real-world decisions they must make in their businesses. The use of analytical tools will improve your ability to use data to make these informed decisions.

In this task, you will address the business situation in the attached “Linear Regression Analysis Resources” scenario. You will access the scenario and dataset by entering your student ID number in the “Start” tab of the attachment, then continuing to the “Scenario” tab. Using this data set, you will perform a linear regression analysis and write a report in which you recommend a solution by summarizing the key details of your analysis.

For full functionality of the scenario and dataset attachment, you are strongly encouraged to use Microsoft Excel, which is available via the Microsoft Office 365 subscription service provided to all WGU students. It can be downloaded using the “Microsoft Office 365” link in the Web Links section.

Scenario

Refer to the scenario located in the attached “Linear Regression Analysis Resources.”

Requirements

Your submission must represent your original work and understanding of the course material. Most performance assessment submissions are automatically scanned through the WGU similarity checker. Students are strongly encouraged to wait for the similarity report to generate after uploading their work and then review it to ensure Academic Authenticity guidelines are met before submitting the file for evaluation. See Understanding Similarity Reports for more information.

You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course.

Complete your linear regression analysis and create a report by doing the following:

Note: The supporting document “Linear Regression Analysis Resources” contains a scenario, data set, and data analysis template. While you must use the scenario and data set provided in the supporting document, the template is optional. You are encouraged to use the template to complete your

analysis. Please see supporting document, “QUM Task 1 Getting Started,” for help accessing the scenario and dataset.

A. Summarize the business scenario by doing the following:

1. 1. Describe a business question that could be answered by applying linear regression analysis and is derived from the scenario in the attached “Linear Regression Analysis Resources.”
   2. State the null hypothesis for this linear regression analysis.
   3. Using relevant details from the scenario, the business question you proposed in part A1, and the null hypothesis you stated in part A2, justify why linear regression is the appropriate analysis technique.

B. Describe the data provided in the attached “Linear Regression Analysis Resources” by doing the following:

1. 1. 1. Describe the relevant data characteristics for your linear regression analysis, including each of the following:
         • independent variable or variables
         • dependent variable
         • level of measurement for each variable
         • sample size or number of observations

C. Report how you analyzed the data using linear regression by doing the following:

1. Provide the output and calculations of the linear regression analysis you performed.

Note: The output should include the output from the software you used to perform the analysis. Refer to “Prepare for the Performance Assessment Task 1” in the course of study to see examples of acceptable output.

2. Create a graphical display of the data using a scatter plot that includes each of the following:

• • • chart title
    • legend
    • axis titles

Note: This display should be a summary or representation of the data provided, not raw data.

D. Describe the implications of your data analysis from the scenario by doing the following:

1. Interpret the results of the data analysis by doing the following:

a. Discuss the goodness of fit with the supporting test statistic from your linear regression analysis output.

b. Discuss the significance of the independent variable(s) with support from your linear regression analysis results.

c. Create the linear equation, then describe how it can be applied to the scenario in future business decision-making.

2. Discuss a limitation of the research that could affect the recommended course of action.

3. Recommend a course of action that aligns with your linear regression analysis results.

Note: Your recommendation should focus on the results of your analytic technique output from part C1.

E. Acknowledge sources, using in-text citations and references, for content that is quoted, paraphrased, or summarized.

F. Demonstrate professional communication in the content and presentation of your submission.

Setup: Fix This Before You Open the Rubric

The single most common stall point is the missing “Data Analysis” button in Excel. Three causes, in order of likelihood:

1. You’re on Excel Online, not the desktop app — the Analysis ToolPak doesn’t exist in the browser version.
2. The ToolPak is installed but not checked: File → Options → Add-ins → Manage: Excel Add-ins → Go → check Analysis ToolPak → OK.
3. On Mac, the path is Tools → Excel Add-ins instead of File → Options.

Confirm you’re running desktop Excel (free via the Microsoft 365 link in your course Web Links) before doing anything else.

Rubric Breakdown — What Each Part Actually Needs

A1 (Business question): Must name both variables and use “relationship” or “predict.” “Does the wellness program help nurses?” fails this — it’s not measurable. “Is there a statistically significant relationship between program participation rate and nurse attrition rate?” passes.

A2 (Null hypothesis): Always states no relationship. H₀: there is no significant relationship between participation rate and attrition rate. H₁ is the opposite.

A3 (Why regression): Justify with three facts — both variables are continuous ratio data (percentages), you’re testing one predictor against one outcome (simple linear regression), and 36 observations clears the 30-observation reliability threshold.

B1–B2 (Data + chart): Describe both variables, their measurement level, and sample size. The scatter plot needs a chart title, axis titles, and a legend — evaluators check for all three independently; a chart missing just the legend still loses points.

C1–C2 (Output): Paste the full Excel regression output (Regression Statistics, ANOVA, Coefficients) as a table, not a screenshot fragment.

D1a (Goodness of fit): State the R² value and what percentage of variation it explains — don’t just report the number.

D1b (Significance): State the p-value, compare it to 0.05, and explicitly say whether you reject or fail to reject the null hypothesis. Skipping the reject/fail-to-reject sentence is the single most common point loss in this section.

D1c (Linear equation): Build Y = mX + b from your intercept and X coefficient, then plug in a sample X value to show a real prediction — this is the part most submissions skip and evaluators specifically look for.

D2 (Limitation): Pick one and develop it in 2–3 sentences: confounding variables (pay, management, shift load), outlier sensitivity, or correlation-vs-causation.

D3 (Recommendation): Tie it directly back to your own p-value and R², not generic advice.

Community Tips: What Actually Gets Task 1 Passed on the First Try

• Run the ToolPak on your own dataset before writing a single sentence. Students who write the report first and try to fit their own numbers into it afterward are the ones who end up with a mismatched interpretation.
• Screenshot your regression output the moment it generates. Re-running the analysis (e.g., after adjusting a cell reference) can shift your numbers slightly — keep the version you’re actually writing about.
• Write the D1b reject/fail-to-reject sentence first, before the rest of the interpretation. It anchors everything else you write about significance, and it’s the single most-missed line according to the rubric breakdown above.
• Don’t skip the axis titles and legend on the scatter plot — evaluators check for chart title, legend, and both axis titles as three separate, independent criteria. Missing just the legend still costs points even if the rest of the chart is correct.
• If your similarity report comes back high, check your source citations first, not just your analysis text — an uncited paraphrase from the MindEdge module is a common, avoidable cause.

Third-Party Resources for C207 Task 1

• Understanding Similarity Reports — WGU’s own guidance on how the similarity checker works and what triggers a resubmission, linked from your course of study.
• MindEdge Course of Study — the required reading modules referenced throughout this guide; cite these in APA if you draw on them directly in your write-up.
• Microsoft Office 365 (via your WGU Web Links) — the free path to desktop Excel with the Analysis ToolPak, which is required for this task and not available in Excel Online.

Why This Example Won’t Get You Flagged

The numbers below are fictitious — your actual dataset is generated from your student ID, so your R², p-value, and coefficients will be different from every other student’s, including this example. That’s by design: WGU’s similarity checker is built to catch submissions that reuse someone else’s output numbers, which is exactly what happens when students copy a Studocu or Stuvia answer wholesale.

Use this example to see how a complete, correctly structured answer reads — the business question phrasing, the null hypothesis format, the goodness-of-fit sentence — then plug in your own numbers once you’ve run the regression on your own data.

C207 Task 1 Worked Example (Fictitious Data — Reference Only)

The numbers below are illustrative. Your dataset is unique to your student ID — use this only to see how a complete, correctly-structured answer reads, not as content to copy.

Scenario recap: Fictitious “Crestview Regional Hospital” tracked monthly well-being program participation (X) against nurse attrition (Y) over 36 months.

Business question: Is there a statistically significant relationship between the monthly nurse participation rate in Crestview’s employee well-being program and the monthly nurse attrition rate over a 36-month period?

Null hypothesis (H₀): There is no significant relationship between program participation rate and nurse attrition rate.

Alternative (H₁): There is a significant relationship between the two.

Data description: The dataset contains 36 monthly observations for each variable (72 data points total), drawn from Crestview’s HR and program-enrollment records. The independent variable, program participation rate, is ratio-level data expressed as a percentage of eligible nurses enrolled each month. The dependent variable, nurse attrition rate, is also ratio-level data expressed as the percentage of nursing staff who left each month. Both variables are continuous and measured on the same monthly cadence, which is what makes simple linear regression applicable here.

Scatter plot:

Chart title, legend, and both axis titles are required rubric elements — all three are included above.

Regression Statistics

ANOVA

Coefficients

Interpretation:

R² = 0.717, meaning roughly 72% of the variation in nurse attrition rate is explained by program participation rate; the remaining 28% is attributable to factors outside the model.

The p-value for the X coefficient (0.0000) is well below 0.05, so the null hypothesis is rejected — there is a statistically significant relationship between participation rate and attrition rate.

Linear equation: Y = -0.09X + 5.83

At 60% participation: Y = -0.09(60) + 5.83 = 0.43% predicted attrition rate, versus an estimated 5.83% at 0% participation — a meaningful spread for staffing forecasts.

Limitation: The model uses a single predictor. Pay competitiveness, shift scheduling, and management turnover all plausibly affect attrition and aren’t captured here, which limits how much weight the recommendation can carry on its own.

Recommendation: Given the significant negative relationship (p < 0.05, R² = 0.717), Crestview should expand the well-being program and track participation alongside compensation and scheduling data to account for the unexplained 28% of variance.

Why linear regression held up against the actual results: The scatter plot shows a clear downward-sloping pattern with points clustering tightly around the trendline, not a curved or random spread — confirming a linear model was the right fit rather than the conceptual fit assumed at the outset. The high R² (0.717) and significant F-statistic (86.21, p < 0.0001) reinforce that a straight-line relationship explains the data well, so no alternative technique (e.g., logistic or polynomial regression) was warranted here.

Source note: Where MindEdge module content or outside research informs your interpretation (e.g., citing established attrition-driver research), include an APA in-text citation and matching reference entry — this section is graded separately from the analysis itself.

WGU C207 Task 1 FAQs

Task 1 vs. Task 2

Once Task 1 is submitted, the C207 Task 2 guide walks through the decision tree and expected-value calculations using the same rubric-breakdown structure.

Related to the WGU C207 Task 1 Guide

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

How to Write a Null Hypothesis for WGU C207 Task 1

How to Interpret Your WGU C207 Task 1 Regression Output

Watch This Alongside the Guide

How to Pass WGU MBA C207 Data-Driven Decision Making walks the Excel steps in real time — useful if you want to see the ToolPak output generated live rather than just read about it. Pair it with the rubric breakdown above so you’re matching what’s on screen to what’s actually graded.

Before You Submit

• Similarity: under 30% total, under 10% from any one source — check your report before clicking submit.
• Grammarly for Education runs automatically; an unresolved flag returns the task without evaluator review.
• Cite the MindEdge modules in APA if referenced.

Author Bio

Dan Palmer, MBA, writes WGU MBA course guides for Gradevia, focusing on the quantitative and analytics-heavy courses (C207, C211, C213, C214) where students most often get stuck on tooling setup and rubric-letter interpretation rather than the underlying concept. Connect on LinkedIn.