Step-by-Step Guide: QUM3 C207 Task 1 – Linear Regression Analysis

QUM3 C207 Task 1 – Linear Regression Analysis

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.

Grammarly Note:

Professional Communication will be automatically assessed through Grammarly for Education in most performance assessments before a student submits work for evaluation. Students are strongly encouraged to review the Grammarly for Education feedback prior to submitting work for evaluation, as the overall submission will not pass without this aspect passing. See Use Grammarly for Education Effectively for more information.

Microsoft Files Note:

Write your paper in Microsoft Word (.doc or .docx) unless another Microsoft product, or pdf, is specified in the task directions. Tasks may notbe submitted as cloud links, such as links to Google Docs, Google Slides, OneDrive, etc. All supporting documentation, such as screenshots and proof of experience, should be collected in a pdf file and submitted separately from the main file. For more information, please see Computer System and Technology Requirements.

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 “
   2. State the null hypothesis for this linear regression
   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

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

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

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

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

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

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

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

Step-by-Step Guide: WGU C207 Task 1 – Linear Regression Analysis

Before You Begin: Access Your Data

1. Download the Linear Regression Analysis Resources.xlsx file from the WGU course page
2. Open it in Microsoft Excel (not Google Sheets)
3. Go to the “Start” tab and enter your student ID number
4. Navigate to the “Scenario” tab to read your specific scenario and view your dataset
5. Note your assigned variables — the scenario will tell you what is being measured

Section A: Summarize the Business Scenario

A1 – Business Question

• Read your scenario carefully and identify what the manager/business is trying to predict or understand
• Frame it as a question that linear regression can answer
• Format example: “Does [independent variable] predict/influence [dependent variable]?”
• Make sure the question involves a continuous outcome and a measurable predictor

A2 – Null Hypothesis

• The null hypothesis for linear regression always states there is no relationship between the variables
• Standard format: “There is no statistically significant linear relationship between [independent variable] and [dependent variable].”
• Keep it simple and directly tied to your scenario variables

A3 – Justify Linear Regression

Explain why linear regression is the right tool by addressing all three of these points:

• Scenario fit: Tie your explanation back to the business context (e.g., predicting sales, costs, performance)
• Reference A1: Explain that the business question requires predicting a continuous dependent variable from one or more independent variables
• Reference A2: State that linear regression allows you to test the null hypothesis by determining whether a statistically significant linear relationship exists
• Mention that the variables are continuous/ratio level, which satisfies a key assumption of linear regression

Section B: Describe the Data

B1 – Data Characteristics

Open your dataset and identify and describe each of the following:

Write this as a short paragraph, not a table, in your actual paper.

Section C: Run the Analysis & Create Visuals

C1 – Run the Regression in Excel

Follow these steps exactly:

1. Click the Data tab in Excel
2. Click Data Analysis (far right) — if you don’t see it, go to File → Options → Add-ins → Analysis ToolPak → Enable
3. Select Regression → click OK
4. Set Input Y Range: highlight your dependent variable column (including header)
5. Set Input X Range: highlight your independent variable column (including header)
6. Check Labels (since you included headers)
7. Select New Worksheet Ply for output
8. Click OK

The output will generate a table with: Regression Statistics, ANOVA table, and Coefficients table. Screenshot or copy this entire output into your Word document.

C2 – Create the Scatter Plot

1. Select both columns of data (X and Y)
2. Go to Insert → Charts → Scatter (X,Y)
3. Choose the basic scatter plot option
4. Add the following — the rubric requires all three:
   • Chart title (descriptive, e.g., “Relationship Between [X] and [Y]”)
   • Axis titles (label both X and Y axes with variable names and units)
   • Legend (right-click chart → Add Legend)
5. Right-click on a data point → Add Trendline → select Linear → check Display Equation and Display R-squared
6. Screenshot or paste the chart into your Word document

Section D: Interpret and Apply Your Results

This is the most important section. Use the regression output table from C1 to answer each part.

D1a – Goodness of Fit

• Locate R Square in the Regression Statistics section of your output
• Report the value and interpret it: “The R² value of [X] indicates that [X%] of the variation in [dependent variable] is explained by [independent variable].”
• A higher R² (closer to 1.0) means a better fit
• Also reference the Significance F from the ANOVA table (should be < 0.05 to confirm the model is significant)

D1b – Significance of the Independent Variable

• Go to the Coefficients table in your output
• Find the p-value for your independent variable (listed in the last column, labeled “P-value”)
• Compare it to the significance level (α = 0.05)
• If p < 0.05: The independent variable is statistically significant; reject the null hypothesis
• If p > 0.05: Fail to reject the null hypothesis
• Write a clear statement: “The p-value for [independent variable] was [value], which is [less than/greater than] the significance level of 0.05, indicating that [independent variable] is [significant/not significant] in predicting [dependent variable].”

D1c – Linear Equation

• From the Coefficients table, locate:
  • Intercept value
  • Slope (coefficient for your independent variable)
• Write the equation: Ŷ = b₀ + b₁X
  • Example: Ŷ = 12.4 + 3.2X
• Then explain in plain language how a manager could use this equation to make decisions
  • Example: “For every one-unit increase in [X], [Y] is expected to increase by [slope value]. A manager could use this equation to forecast [Y] given a known value of [X].”

D2 – Limitation

Choose one realistic limitation, such as:

• Correlation ≠ causation: The analysis shows a relationship but cannot prove that one variable causes the other
• Limited sample size: A small dataset may not represent the full population
• Single predictor: The model uses only one independent variable; other factors not included in the model may also influence the outcome
• Outliers: Extreme data points can skew the regression line

Explain how this limitation could affect the reliability of the recommendation.

D3 – Recommended Course of Action

• Base your recommendation directly on the regression results (not general opinion)
• If the relationship is significant: Recommend the business use the linear equation for forecasting or resource planning
• If the relationship is not significant: Recommend the business investigate other variables or collect more data
• Keep it tied to the scenario context

Section E: Sources & Citations

• If you reference any textbook, course material, or external source, cite it in APA 7 format
• In-text: (Author, Year)
• Reference list at the end of the paper
• At minimum, cite your course textbook or any methodology source you reference when explaining linear regression concepts

Section F: Professional Communication

• Write in formal, third-person academic tone
• Run your paper through Grammarly for Education (available through WGU) before submitting — the rubric explicitly requires this
• Check for grammar, spelling, punctuation, and sentence fluency errors
• Use clear paragraph structure: one idea per paragraph

Final Checklist Before Submitting

• Student ID entered and correct scenario/data used
• Business question is scenario-specific and regression-appropriate
• Null hypothesis is clearly and correctly stated
• Regression output (full table) is pasted in the paper
• Scatter plot has title, legend, and both axis titles
• R² and p-value are discussed and correctly interpreted
• Linear equation is written out and applied to the scenario
• One limitation identified and explained
• Recommendation aligns with regression results
• APA in-text citations and reference list included
• Grammarly review completed
• File saved as .docx
• Similarity report reviewed before final submission