WGU C207 Task 1: Complete Guide to Passing Your Linear Regression Analysis
WGU C207 Task 1: Linear Regression Analysis
DATA-DRIVEN DECISION MAKING — C207 PRFA — QUM3
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.
- Summarize the business scenario by doing the following:
- 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.”
- State the null hypothesis for this linear regression analysis.
- 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.
- Describe the data provided in the attached “Linear Regression Analysis Resources” by doing the following:
- 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
- Describe the relevant data characteristics for your linear regression analysis, including each of the following:
- Report how you analyzed the data using linear regression by doing the following:
- 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.
-
- Create a graphical display of the data using a scatter plot that includes each of the following:
- chart title
- legend
- axis titles
- Create a graphical display of the data using a scatter plot that includes each of the following:
Note: This display should be a summary or representation of the data provided, not raw data.
- Describe the implications of your data analysis from the scenario by doing the following:
- Interpret the results of the data analysis by doing the following:
- Discuss the goodness of fit with the supporting test statistic from your linear regression analysis output.
- Discuss the significance of the independent variable(s) with support from your linear regression
analysis results.
- Create the linear equation, then describe how it can be applied to the scenario in future business decision-making.
- Discuss a limitation of the research that could affect the recommended course of action.
- 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.
- Acknowledge sources, using in-text citations and references, for content that is quoted, paraphrased, or summarized.
- Demonstrate professional communication in the content and presentation of your submission.
WGU C207 Task 1: Complete Guide to Passing Your Linear Regression Analysis
Struggling with WGU C207 Task 1? You’re not alone. Thousands of WGU students every term hit a wall when they open the “Linear Regression Analysis Resources” spreadsheet and don’t know where to start. This guide walks you through every section of the rubric, explains the statistical concepts in plain English, and shows you exactly what evaluators are looking for — so you can pass on the first attempt.
What Is WGU C207?
WGU C207 — officially titled Data-Driven Decision Making — is a core competency course in WGU’s MBA program and several other business degree paths. It teaches students how to use quantitative analytical tools to make informed business decisions rather than relying on gut instinct.
The course has three graded components:
| Component | What It Tests |
|---|---|
| Task 1 | Linear regression analysis (statistical prediction) |
| Task 2 | Decision tree analysis (probabilistic decision-making) |
| Objective Assessment (OA) | Multiple-choice exam covering statistical concepts, chart interpretation, p-values, R², histograms, and more |
Most students find Task 1 the most technically challenging because it requires running a real statistical analysis in Excel and interpreting the output accurately. This guide focuses entirely on Task 1.
C207 Task 1 Overview
What is Task 1 asking you to do?
Task 1 requires you to complete a linear regression analysis using a dataset provided by WGU, then write a business report that explains:
- The business question your analysis answers
- What the data represents (variables, type, relevance)
- How you ran the analysis and created visualizations
- What the statistical results mean for the business
The supporting document you’ll use is called “Linear Regression Analysis Resources.” You download this Excel file using your WGU student ID — every student gets a slightly different dataset, so your specific numbers (R², p-value, coefficients) will vary from examples you find online. The structure, however, is identical for everyone.
Time estimate: Plan for 4–8 hours across 1–2 days, including reading Modules 1–3 of the MindEdge textbook first.
File format required: Microsoft Word (.doc or .docx). Submit as an attachment — no Google Docs, OneDrive links, or cloud links are accepted.
Before You Start: Setup Checklist
Before writing a single word, complete these steps:
- [ ] Read MindEdge Modules 1, 2, and 3 (they directly support the task concepts)
- [ ] Watch the WGU Express Cohort videos for C207 — especially Modules 2 and 3
- [ ] Download your personalized Excel dataset using your WGU student ID
- [ ] Verify you have the desktop version of Microsoft Excel installed (not the web version — you need the Data Analysis ToolPak)
- [ ] Enable the Analysis ToolPak add-in in Excel (File → Options → Add-ins → Analysis ToolPak → Go → Check box → OK)
- [ ] Download and review the Task 1 rubric from your WGU course page
- [ ] Open the task in a separate window and keep the rubric visible while writing
Important: You need the desktop version of Microsoft Excel — this is a free download for all WGU students through the Microsoft Office 365 link in your course’s Web Links section. The web version of Excel does NOT include the Data Analysis ToolPak needed for regression.
Understanding the Scenario
Every student’s Task 1 is built around the same core scenario:
A hospital is experiencing a high nurse attrition (turnover) rate. Nurses report job-related stress and unfavorable working conditions. To address this, the hospital developed an employee well-being program. HR has collected 36 months of data tracking:
- Independent Variable (X): Program Participation Rate (%) — the percentage of nurses enrolled in the well-being program each month
- Dependent Variable (Y): Nurse Attrition Rate (%) — the percentage of nurses who left the hospital each month
The hospital wants to know: Is there a statistically significant relationship between program participation and nurse attrition? Your linear regression analysis answers this question.
Understanding this scenario deeply before writing is critical. The scenario tells you:
- Why linear regression is appropriate
- What the null hypothesis should be
- How to frame your business question
- How to contextualize your recommendations
Full Rubric Breakdown
This is the most important section of this guide. The WGU evaluator grades your submission entirely against the rubric. Each prompt below must be answered completely and accurately.
Section A: Business Scenario Summary
A1 — Describe a Business Question for Linear Regression
You must formulate a clear, answerable business question derived from the scenario that can be addressed using linear regression.
What evaluators want: A specific, measurable question that identifies both variables and implies a predictive or correlational relationship.
Strong example:
“Is there a statistically significant relationship between the monthly nurse participation rate in the employee well-being program (independent variable) and the monthly nurse attrition rate (dependent variable) over the 36-month study period?”
Weak example (avoid):
“Does the wellness program help nurses?” — Too vague; no mention of variables or measurability.
Key tip: Your business question must include the word “relationship” or reference prediction. Linear regression analyzes relationships between continuous variables — your question must reflect that.
A2 — State the Null Hypothesis
The null hypothesis (H₀) is the default assumption that there is no significant relationship between your variables. You are testing whether the data provides enough evidence to reject this assumption.
Standard null hypothesis for this scenario:
“H₀: There is no significant relationship between the nurse participation rate in the employee well-being program and the nurse attrition rate.”
Alternative hypothesis (H₁):
“H₁: There is a significant relationship between the nurse participation rate in the employee well-being program and the nurse attrition rate.”
Common mistake: Students confuse the null and alternative hypothesis or phrase the null hypothesis as a positive claim. The null hypothesis always states no effect, no relationship, or no difference.
A3 — Justify Why Linear Regression Is Appropriate
This is a conceptual justification section. You must explain why linear regression — and not another statistical test — is the right tool for this scenario.
Your justification should cover:
- Variable types: Both variables are continuous numerical data (percentages), making linear regression appropriate. Linear regression requires numerical continuous data on both the X and Y axes.
- Relationship type: The analysis examines the relationship between one independent variable and one dependent variable (simple linear regression).
- Purpose: Linear regression is used to predict or estimate the value of a dependent variable based on the value of an independent variable — which directly matches the hospital’s goal of predicting attrition based on participation.
- Sample size: The dataset contains 36 monthly data points. The minimum recommended for reliable continuous data analysis is 30 observations, so the dataset meets this threshold.
Sample language:
“Linear regression is appropriate for this analysis because both the program participation rate and nurse attrition rate are continuous numerical variables expressed as percentages (ratio data). Simple linear regression allows us to examine the predictive relationship between one independent variable (X) and one dependent variable (Y), which aligns with the hospital’s goal of determining whether participation in the well-being program can predict or explain changes in nurse attrition. The 36-month dataset meets the minimum sample size requirement for reliable regression analysis.”
Section B: Data Description
B1 — Describe the Relevant Data
Identify and describe the data used in your analysis.
Cover these points:
- What the dataset contains (36 months of hospital HR data)
- The two variables: program participation rate (X) and nurse attrition rate (Y)
- Data type: ratio data (percentages)
- The source: extracted from program enrollment and HR employee databases
- Number of data points: 72 total (36 for each variable), with 36 pairs analyzed
B2 — Create a Graphical Display
You must create a scatter plot with a trendline in Excel. The chart must include:
- Chart title (e.g., “Program Participation Rate vs. Nurse Attrition Rate — 36-Month Analysis”)
- Legend identifying both data series
- Axis titles: X-axis = “Program Participation Rate (%)” | Y-axis = “Nurse Attrition Rate (%)”
- Trendline (linear) added to the scatter plot
The scatter plot visually shows the direction and strength of the relationship between the two variables. A downward-sloping trendline indicates a negative correlation — as participation increases, attrition decreases.
See the Excel instructions section below for the exact steps to create this chart.
Section C: Analysis Report
C1 — Provide the Linear Regression Analysis Output
This is where you paste your actual Excel regression output table into your Word document. Your output will include:
- Regression Statistics (Multiple R, R Square, Adjusted R Square, Standard Error, Observations)
- ANOVA table (df, SS, MS, F, Significance F)
- Coefficients table (Intercept, X variable coefficient, standard error, t stat, p-value, confidence intervals)
Copy your full regression output from Excel and paste it as a table in your Word document. Label it clearly.
C2 — Justify Why Linear Regression Is the Appropriate Technique
(Note: This is different from A3. Here you justify based on your actual results, not just the scenario.)
Reference your specific output to reinforce the justification. For example, mention that the data shows a linear pattern on the scatter plot, confirming that linear regression is the right model.
Section D: Implications of Data Analysis
This is the most heavily weighted section. This is where you interpret what your numbers actually mean.
D1a — Discuss Goodness of Fit (R-Squared)
R-squared (R²) measures how well the independent variable explains the variation in the dependent variable. It ranges from 0 to 1:
| R² Value | Interpretation |
|---|---|
| 0.0 – 0.3 | Weak fit — the model explains little of the variation |
| 0.3 – 0.6 | Moderate fit — the model explains a reasonable amount of variation |
| 0.6 – 1.0 | Strong fit — the model explains most of the variation |
How to write this section:
“The R-squared value for this analysis is [your value], indicating a [weak/moderate/strong] goodness of fit. This means that approximately [R² × 100]% of the variation in the nurse attrition rate can be explained by the program participation rate. The remaining [100 − (R² × 100)]% is explained by other factors not captured in this model.”
Critical mistake to avoid: Do not just state the R² number. Explain what it means in the context of the scenario.
D1b — Discuss Significance of the Independent Variable (P-Value)
The p-value tests whether the relationship between X and Y is statistically significant or due to random chance.
- If p-value < 0.05: The relationship IS statistically significant. Reject the null hypothesis.
- If p-value ≥ 0.05: The relationship is NOT statistically significant. Fail to reject the null hypothesis.
Most students’ datasets produce a very small p-value (well below 0.05), which means they reject the null hypothesis — there IS a significant relationship.
How to write this section:
“The p-value for the program participation rate (independent variable) is [your value], which is [less than/greater than] the standard significance threshold of 0.05. Because the p-value is [less than/greater than] 0.05, the null hypothesis is [rejected/not rejected]. This indicates that there [is/is not] a statistically significant relationship between the program participation rate and the nurse attrition rate.”
Critical mistake: Never say a low p-value “proves” the program causes lower attrition. Correlation does not imply causation. Say “correlates with” not “causes.”
D1c — Create the Linear Equation and Explain Its Application
The linear equation takes the form: Y = mX + b, where:
- Y = predicted nurse attrition rate (dependent variable)
- X = program participation rate (independent variable)
- m = slope coefficient (found in your Excel output under “X Variable 1”)
- b = intercept (found in your Excel output under “Intercept”)
Example: If your intercept is 5.83 and your X coefficient is -0.09, your equation is:
Y = -0.09X + 5.83
What the slope tells you: A negative slope means that as program participation increases, nurse attrition decreases. A positive slope means the opposite.
How to apply this to business decisions:
“Using this linear equation, hospital administrators can estimate the expected nurse attrition rate at any given program participation level. For example, if program participation reaches 60%, the predicted attrition rate would be Y = -0.09(60) + 5.83 = 0.43%. This predictive capability allows HR to set participation targets and forecast staffing needs, enabling more proactive resource allocation.”
This practical application is something most student submissions miss — and it impresses evaluators.
D2 — Discuss a Limitation of the Research
You must identify at least one meaningful limitation of the linear regression analysis that could affect the recommended course of action.
Strong limitations to cite (pick one or two):
- Confounding variables: The model only captures two variables. Other factors that could affect nurse attrition — such as pay rates, management quality, overtime hours, shift assignments, or the broader healthcare labor market — are not included in the model. This limits the model’s explanatory power.
- Sensitivity to outliers: Linear regression is sensitive to outlier data points. A single month with an unusually high or low attrition or participation rate can significantly skew the regression line and distort predictions.
- Temporal limitations: The 36 months of data represents a fixed historical period. Conditions may have changed since then (e.g., post-pandemic healthcare landscape), making predictions less reliable for future planning.
- Correlation vs. causation: The analysis identifies a statistical relationship but cannot establish that the well-being program directly caused the change in attrition. Other initiatives happening simultaneously at the hospital could be responsible.
- Single independent variable: Real-world attrition is driven by many factors. A model using only program participation rate as the predictor is an oversimplification.
D3 — Recommend a Course of Action
Based on your analysis, what should the hospital do?
If your results show a statistically significant negative relationship (as most datasets do), your recommendation should be to continue and expand the well-being program, since higher participation correlates with lower attrition.
Strong recommendation example:
“Based on the linear regression analysis, the data supports a statistically significant negative relationship between nurse program participation and attrition rates (p-value < 0.05, R² = [your value]). I recommend that hospital administration continue and expand the employee well-being program. Efforts should focus on increasing monthly participation rates, as the linear equation predicts that higher participation correlates with measurable reductions in attrition. Additionally, HR should monitor additional variables — such as compensation competitiveness and shift scheduling — to address the portion of attrition variance not explained by this model alone.”
How to Run Linear Regression in Excel
This is the step many students skip over or do incorrectly. Follow these exact steps:
Step 1: Enable the Analysis ToolPak
- Open Excel → File → Options → Add-ins
- At the bottom, select “Excel Add-ins” from the Manage dropdown → Click Go
- Check “Analysis ToolPak” → OK
Step 2: Open Your Dataset
- Open your personalized “Linear Regression Analysis Resources” Excel file
- Your data has two columns: Column 1 = Program Participation Rate (X), Column 2 = Nurse Attrition Rate (Y)
Step 3: Run the Regression
- Click the Data tab in the Excel ribbon
- Click Data Analysis (far right)
- Select Regression from the list → Click OK
Step 4: Configure the Regression Dialog Box
- Input Y Range: Select your Nurse Attrition Rate column (including the header)
- Input X Range: Select your Program Participation Rate column (including the header)
- Check Labels (if you included the header row)
- Check Line Fit Plots (this generates your scatter plot)
- Check Residuals (optional but useful)
- Select Output Range or New Worksheet for your results
- Click OK
Step 5: Review Your Output Excel will generate a new sheet or range with your regression statistics, ANOVA table, and coefficients table. Copy this entire output into your Word document.
How to Create the Scatter Plot
The rubric requires a scatter plot with specific elements. Even if the Line Fit Plot from the regression step generates a chart, you may want to create a cleaner, properly labeled scatter plot:
Step 1: Select both data columns (X and Y) in your Excel sheet
Step 2: Click Insert → Charts → Scatter → Scatter (first option, dots only)
Step 3: Add a Trendline
- Click on any data point in the chart
- Right-click → Add Trendline → Linear → Check “Display Equation on chart” and “Display R-squared value on chart”
Step 4: Add Chart Title
- Click the chart → Chart Design → Add Chart Element → Chart Title → Above Chart
- Title: “Program Participation Rate vs. Nurse Attrition Rate”
Step 5: Add Axis Titles
- Chart Design → Add Chart Element → Axis Titles
- X-axis: “Program Participation Rate (%)”
- Y-axis: “Nurse Attrition Rate (%)”
Step 6: Add a Legend
- Chart Design → Add Chart Element → Legend → Bottom (or Right)
Step 7: Copy the chart and paste it into your Word document as an image (Paste Special → Picture).
Interpreting Your Output
Here is a quick-reference guide to the key numbers in your regression output and what to say about each one:
Multiple R (Correlation Coefficient)
- Ranges from -1 to +1
- A negative value means a negative relationship (as X goes up, Y goes down)
- A positive value means a positive relationship
- Closer to -1 or +1 = stronger relationship; closer to 0 = weaker
R-Squared (Coefficient of Determination)
- Ranges from 0 to 1
- Multiply by 100 to get the percentage of Y’s variation explained by X
- Example: R² = 0.67 means 67% of the variation in nurse attrition is explained by program participation
F-Statistic and Significance F (ANOVA Table)
- A large F-value and a Significance F below 0.05 confirm that the regression model as a whole is statistically significant
P-Value (Coefficients Table)
- Check the p-value next to your X Variable
- Below 0.05 = statistically significant → reject the null hypothesis
- Above 0.05 = not statistically significant → fail to reject the null hypothesis
Intercept and X Coefficient
- These are your b and m values in Y = mX + b
- Use them to write your linear equation and plug in sample values for real predictions
Common Mistakes That Cause Task 1 Failures
Based on patterns from returned tasks, these are the most frequent reasons students fail C207 Task 1:
❌ Mistake 1: Stating Causation Instead of Correlation
Wrong: “The well-being program causes lower nurse attrition.” Right: “The well-being program participation rate is significantly correlated with lower nurse attrition rates.”
This single mistake has caused many task returns. Linear regression identifies correlation — it cannot prove causation.
❌ Mistake 2: Missing Required Chart Elements
Many students create a scatter plot but forget to include the chart title, axis titles, or legend. The rubric checks all three. Every element is required.
❌ Mistake 3: Not Addressing the Null Hypothesis in D1b
When discussing the p-value, you MUST state whether you reject or fail to reject the null hypothesis. Describing the p-value without connecting it back to the null hypothesis is incomplete.
❌ Mistake 4: Using the Wrong Excel Version
The Analysis ToolPak is only available in the desktop version of Microsoft Excel. Running your regression in Excel Online or Google Sheets will not produce the required output tables.
❌ Mistake 5: Providing a Vague Business Question
“Is the well-being program effective?” is not an acceptable business question for linear regression. Your question must identify the specific independent and dependent variables and imply a measurable relationship.
❌ Mistake 6: Copying Another Student’s Numbers
Every student’s dataset is personalized. If you use someone else’s R², p-value, or equation, your numbers won’t match your Excel file — and this is a fast path to an originality flag and task return.
❌ Mistake 7: Failing the Grammarly Check
WGU automatically runs your submission through Grammarly for Education before evaluator review. If professional communication standards aren’t met, the task will not pass. Always review and fix Grammarly feedback before submitting.
❌ Mistake 8: Ignoring the Limitation Section
Section D2 is graded. Students often write one weak sentence here. Write 2–3 sentences explaining a meaningful limitation and how it could affect the recommended course of action.
C207 Task 1 vs. Task 2: Key Differences
Many students are confused about how Tasks 1 and 2 differ. Here’s a clear comparison:
| Feature | Task 1 | Task 2 |
|---|---|---|
| Analysis Type | Linear Regression | Decision Tree Analysis |
| Scenario | Nurse well-being program & attrition | Major Pharmaceutical Company (MPC) drug line decision |
| Business Question | Is there a relationship between two continuous variables? | Which option yields the highest expected value? |
| Key Statistics | R², p-value, linear equation | Probabilities, payoffs, expected values (EV) |
| Primary Tool in Excel | Data Analysis ToolPak → Regression | Decision Tree diagram + Expected Value calculations |
| Output | Regression table, scatter plot | Decision tree diagram, payoff table |
| Decision Focus | Predict/correlate | Choose between alternatives |
For Task 2: You’ll be recommending whether MPC should develop a new drug line, modify the existing one, or make no changes — based on expected value (probability × payoff) calculations. Task 2 also requires an Excel spreadsheet as a supporting file.
How to Pass C207: Tips Beyond the Tasks
Complete Both Tasks Before the OA
The performance tasks build conceptual understanding that the Objective Assessment (OA) tests. Students who rush straight to the OA without completing the tasks struggle significantly with the multiple-choice questions.
Know These Concepts for the OA
The OA tests conceptual understanding, not just definitions. You need to understand:
- The difference between correlation and causation
- When to use a t-test vs. regression vs. decision tree
- How to read and interpret p-values, R², histograms, and scatter plots
- Statistical terms: mean, median, mode, standard deviation, outliers, skewness
Use the “Correlation vs. Causation” Rule Everywhere
This principle is tested on the OA and in the tasks. Never say one variable causes another based solely on regression analysis.
Read the Rubric Multiple Times
The rubric is your grading checklist. Every requirement in the rubric must be addressed. Read it before writing, while writing, and before submitting.
Don’t Skip the Express Cohort Videos
Multiple community members consistently praise the Express Cohort videos — especially Module 2 (regression concepts) and Module 3 (quantitative tools). These videos are available inside your WGU course and directly address task concepts in accessible language.
Originality & Grammarly Requirements
WGU has strict academic integrity standards for C207. Here’s what you need to know:
Similarity Threshold: No more than 30% of your total submission can match other sources, and no more than 10% can match any single source. After uploading your draft, WGU generates a similarity report — review it before clicking “Submit for Evaluation.”
Your own words: Since datasets are personalized, your analysis interpretation must be in your own words. Using a sample paper as a template is risky because paraphrasing too closely will trigger similarity flags.
Grammarly for Education: WGU automatically runs your submission through Grammarly before it reaches an evaluator. If the professional communication check does not pass, your task will be returned without evaluation. Fix all Grammarly suggestions — especially grammar, sentence structure, and spelling — before submitting.
APA Citations: If you reference the MindEdge textbook or any outside source, include proper APA citations. The MindEdge modules are commonly cited in C207 submissions.
Frequently Asked Questions
Q: Can I use a different scenario for Task 1 instead of the nurse attrition one? A: The standard version of Task 1 uses the nurse well-being program scenario with the provided Excel dataset. Some older versions of the course allowed custom scenarios, but the current version uses the provided dataset. Always check your specific task prompt.
Q: My R² value seems very low. Did I do something wrong? A: Not necessarily. R² values vary by dataset. A moderate R² (0.4–0.7) is common and perfectly valid. What matters is that you accurately interpret whatever value your analysis produces. Don’t alter your data to get a “better-looking” result.
Q: Do I need to include the full Excel output or just parts of it? A: Include the full regression output table in your Word document. The evaluator needs to see the Regression Statistics, ANOVA, and Coefficients table to verify your analysis.
Q: How long should Task 1 be? A: There’s no stated page minimum, but most passing submissions are 4–7 pages, not counting the Excel output table and scatter plot. Focus on depth and completeness of each rubric section rather than hitting a page count.
Q: What if my p-value is greater than 0.05? A: Some datasets may produce a non-significant p-value. If so, you would fail to reject the null hypothesis and state there is insufficient evidence of a significant relationship. Your recommendation section should then suggest additional research or alternative variables to study. Follow the data — don’t change your numbers.
Q: Can I submit my Excel file separately? A: Yes — and you should. The Excel file with your regression analysis and charts should be submitted as a supporting attachment. Your main Word document is the report; the Excel file provides evidence of your analysis.
Q: I got a “Not Competent” (task return). What do I do? A: Read the evaluator’s feedback carefully. They will identify exactly which rubric aspects were not met. Revise specifically those sections, address every comment, and resubmit. You can resubmit C207 tasks — just make sure each resubmission is a genuine improvement, not a cosmetic change.
Need Personalized Help?
C207 Task 1 is manageable — but it becomes significantly easier when you have step-by-step guidance tailored to your specific dataset and situation.
If you’re:
- Staring at your Excel output and not sure how to interpret your specific numbers
- Struggling to write the business question, null hypothesis, or limitations sections
- Worried about the similarity checker or Grammarly requirements
- Working under a tight deadline and need structured support
- On your second or third attempt and unsure what the evaluator wants
You don’t have to figure this out alone.
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