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WGU C207 Task 2 – Decision Tree 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 informed decisions.

In this task, you will address the business situation in the attached scenario. You will access the scenario and dataset by entering your student ID number in the “Start” tab of the “Decision Tree Analysis Resources” document found in the Supporting Documents section. The scenario and dataset are located in the “Decision Tree Scenario” tab. Using this dataset, you will perform a decision tree analysis and recommend a solution.

This recommendation will be included in a report you will write summarizing the key details of your analysis.

For full functionality of the scenario and data attachment, you must 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 supporting document, “Decision Tree 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 decision tree analysis and create a report by doing the following:

Note: The supporting document “Decision Tree Analysis Resources” contains a scenario, data set, and 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, “QUM3 Task 2 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 decision tree analysis and is derived from the scenario in “Decision Tree Analysis Resources.”

   2. Justify why decision tree analysis is the appropriate analysis technique, and include relevant details from the scenario to support your justification.

B. Identify the relevant data values required for your decision tree analysis, including the following:

• demands

• profits per unit

• probabilities

C. Report how you analyzed the data using decision tree analysis by completing a decision tree diagram that includes each of the following:

• state-of-nature nodes

• calculated payoffs, each expressed out to two decimal places

• expected values, each expressed out to two decimal places

Note: Include “Decision Tree Analysis Resources.” spreadsheet with your task submission for evidence of your calculations and decision tree diagram.

Note: Refer to “Prepare for the Performance Assessment Task 2″ in the course of study to examples of acceptable output.

D. Summarize the implications of your decision tree analysis by doing the following:

1. 1. Explain each step required to determine the expected value based on

   2. List one limitation for each of the following:

      • any one of the data values listed in part B

      • the decision tree analysis

E. Recommend a course of action that addresses the business question from part A and is based on the results of your decision tree analysis.

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

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

Step-by-Step Guide: WGU C207 Task 2 – Decision Tree Analysis

Before You Begin: Access Your Data

1. Download Decision Tree Analysis Resources.xlsx from the WGU course page
2. Open it in Microsoft Excel (not Google Sheets — it won’t work properly)
3. Go to the “Start” tab and enter your student ID number
4. Navigate to the “Decision Tree Scenario” tab to read your scenario and view your dataset
5. Also download “QUM3 Task 2 Getting Started.docx“ — it contains walkthrough guidance specific to your scenario
6. Note all key data values: the decisions available, demand levels, profits per unit, and probabilities

Understand the WGU C207 Task 2 MPC Scenario

Before you open Excel or write a single sentence, you need to understand what the C207 Task 2 scenario is actually asking. Every student’s dataset is personalized, your probability percentages, demand figures, and profit-per-unit values will differ slightly based on your student ID, but the core scenario, the company, the three decision alternatives, and the structure of the analysis are the same for everyone.

The Company: Major Pharmaceutical Company (MPC)

The scenario centers on Major Pharmaceutical Company (MPC), a drug manufacturer facing a strategic crossroads. A competitor has recently had a new drug approved by the FDA, and MPC’s leadership needs to respond. To support this decision, MPC has contracted with Drug Markets Analysts Inc. (DMA), a market research firm that has provided probability estimates and demand forecasts for each available course of action.

As a data analyst working with MPC’s leadership, your job is to evaluate DMA’s market research data using a decision tree analysis and recommend the option that produces the highest expected monetary value (EMV).

The Three Decision Alternatives

The entire task revolves around evaluating exactly three strategic options for MPC’s drug line. These are referred to throughout the rubric and your dataset:

1. Develop a New Drug Line MPC invests in creating a brand-new pharmaceutical product to compete directly with the competitor’s FDA-approved drug. This option typically carries moderate-to-high favorable market probabilities (commonly in the 71–77% range across student datasets), reflecting the potential upside of entering a new market segment. However, it also represents the highest development cost and risk.

2. Exploit the Existing Drug Line (Modify for New Applications) Rather than developing something new, MPC explores new applications for its current drug — adapting it to address the competitive threat without a full product development cycle. This option is also described in some datasets as “exploiting a new application for the current drug” or “modifying the existing drug line.” Favorable market probabilities for this alternative typically range from 61–63%, with demand figures that often exceed the new drug option in both favorable and unfavorable markets.

3. Make No Changes (Continue Current Drug Line) MPC takes no strategic action and continues operating with its existing drug line as-is. This is the status quo option. It consistently shows the highest favorable market probability in student datasets (commonly 81–89%), reflecting the certainty of known demand — but the demand volumes are the lowest of the three alternatives, typically producing the smallest expected value.

The Two Market Conditions

Each decision alternative is evaluated against two possible states of the market:

• Favorable Market: Conditions are positive; demand is high, competition is manageable, and MPC’s drug offering performs well. This is the upside scenario.
• Unfavorable Market: Conditions work against MPC; lower consumer demand, stronger competition, or market saturation. This is the downside scenario.

Because the probabilities for favorable and unfavorable markets must always sum to 1.0 (100%), if your dataset gives you the favorable market probability, you calculate the unfavorable probability by subtracting from 1. For example, if the new drug line has a 71% favorable probability, the unfavorable probability is 29%.

The Role of Drug Markets Analysts Inc. (DMA)

Drug Markets Analysts Inc. (DMA) is the source of the market research data in your scenario. DMA provides MPC with the probability estimates and demand forecasts that appear in your Excel dataset. When you write your business question in Section A and justify your analysis in A2, you should reference DMA’s research as the source of the probability and demand values; this is the kind of specific scenario detail the rubric’s A2 criterion is looking for.

What Your Dataset Contains (Section B Data Values)

When you enter your student ID in the “Start” tab of the Decision Tree Analysis Resources spreadsheet, the “Decision Tree Scenario” tab populates with your personalized data. For each of the three decision alternatives, you will see:

Why the Scenario Is Designed This Way

The MPC scenario is specifically constructed so that the “obvious” safe choice, making no changes, is rarely the highest expected value option. The no-changes alternative almost always has the highest favorable market probability, which can mislead students into thinking it’s the best choice. The decision tree forces you to account for both probability and demand together through the expected value calculation, which usually reveals that exploiting the existing drug line or developing a new drug line generates a higher EMV despite lower individual probabilities.

This is the core lesson C207 Task 2 is designed to teach: that intuitive probability alone is not sufficient for business decision-making. Payoffs, demand, and expected value together tell the full story; and that’s exactly what the decision tree captures.

Section A: Summarize the Business Scenario

A1 – Business Question

• Read your scenario carefully and identify what decision the manager is trying to make
• The question should involve choosing between two or more alternatives under uncertain conditions
• Format example: “Which production/inventory/capacity option — [Option A] or [Option B] — should [Company] choose to maximize expected profit given uncertain demand?”
• Make sure the question is specific to your scenario’s context and clearly implies multiple decision paths with uncertain outcomes

A2 – Justify Decision Tree Analysis

Explain why decision tree analysis is the right tool using all three of these angles:

• Multiple decision alternatives: The scenario presents two or more options the manager must choose between (e.g., large vs. small facility, high vs. low production run)
• Uncertainty in outcomes: Demand or other outcomes are uncertain, with known probabilities assigned to each possible state — exactly what decision trees are designed to handle
• Quantifiable payoffs: Each combination of decision and demand outcome produces a calculable monetary payoff, which the tree organizes and compares
• Tie each point back to specific details from your scenario

Section B: Identify the Relevant Data Values

Open your Excel dataset and extract and list all three required elements clearly in your paper:

Demands

• These are the possible demand levels in the scenario (e.g., High Demand, Medium Demand, Low Demand)
• List the specific numerical demand quantities given in your dataset

Profits Per Unit

• These are the profit values associated with each decision alternative under each demand level
• Pull these directly from the dataset — express them accurately
• These will be used to calculate your payoffs

Probabilities

• These are the likelihoods assigned to each demand level (e.g., P(High) = 0.30, P(Medium) = 0.50, P(Low) = 0.20)
• Confirm they add up to 1.0 — if they don’t, recheck your data entry
• These are used to weight each payoff when calculating expected values

Present all three data categories in a clean, organized table or clearly labeled paragraph in your paper.

Section C: Build the Decision Tree Diagram

This is the core deliverable. You will complete the diagram inside the Excel template provided, then include it in your Word report.

Step 1 – Understand the Tree Structure

A decision tree has two types of nodes:

• Decision node (square □): Represents a choice the manager makes (e.g., Option A vs. Option B)
• State-of-nature node (circle ○): Represents uncertain outcomes (e.g., High, Medium, Low demand) — the rubric specifically requires these to be labeled

Step 2 – Calculate Each Payoff

For every branch (each combination of decision + demand level), calculate the payoff:

Payoff = Profit per unit × Demand quantity

• Calculate each payoff and round to two decimal places (rubric requirement)
• Enter these at the end of each branch on the tree

Step 3 – Calculate Expected Values (EV)

For each decision alternative, multiply each payoff by its probability, then sum them:

EV = (Payoff₁ × P₁) + (Payoff₂ × P₂) + (Payoff₃ × P₃)

• Calculate the EV for each decision alternative
• Round each EV to two decimal places (rubric requirement)
• The decision with the highest EV is the recommended choice

Step 4 – Complete the Diagram

Your completed decision tree must include all of the following (rubric-required):

• State-of-nature nodes clearly labeled
• All calculated payoffs (to two decimal places) at each branch end
• Expected values (to two decimal places) at each state-of-nature node

Important: Submit the completed Excel spreadsheet alongside your Word document as evidence of your calculations.

C207 Task 2 Worked Example for Section C

What this example is: This is a teaching walkthrough for illustration, not a submittable assignment. The company, the alternatives, and every number below are fictitious and built only to illustrate how the calculations and write-up work.

The Fictitious Scenario (for illustration only)

Riverside Outdoor Co. is a fictional consumer goods company deciding how to respond to a competitor’s new product launch. Leadership is weighing three alternatives and wants to know which one maximizes expected profit. A market research firm has provided probability and demand estimates for each alternative under two market conditions: favorable and unfavorable.

• Alternative 1: Expand the current product line
• Alternative 2: License the product to a regional partner
• Alternative 3: Maintain the current product line with no changes

These are stand-ins for whatever alternatives appear in your actual assigned scenario; the structure of the analysis is what carries over, not these specific options.

Report How You Analyzed the Data Using Decision Tree Analysis

Rubric requirement reminder: your diagram must include state-of-nature nodes, calculated payoffs (two decimal places), and expected values (two decimal places).

Step 1: Lay Out the Tree Structure

A decision tree has two types of nodes. The decision node (drawn as a square) is the single point where the choice is made among the alternatives. From there, each alternative branches to a state-of-nature node (drawn as a circle), which splits again into the favorable and unfavorable market outcomes. Each end branch terminates in a payoff.

Why this matters for grading A tree that shows only the three alternatives and their final dollar amounts, with no circles in between, is the most common reason this section gets marked incomplete. The state-of-nature node has to be visibly present and labeled for each alternative; it represents the uncertainty the whole method is built around.

Step 2: Identify the Inputs for Each Alternative

Using the fictitious dataset for Riverside Outdoor Co.:

Alternative	P(Favorable)	P(Unfavorable)	Profit/Unit	Demand (Fav.)	Demand (Unfav.)
Expand Product Line	0.68	0.32	$4.50	32,000	11,000
License to Partner	0.55	0.45	$2.75	41,000	19,000
Maintain Current Line	0.82	0.18	$1.90	21,000	14,500

Note: Probabilities for favorable and unfavorable conditions must sum to 1.0 for each alternative. Check this first — if your dataset gives you only the favorable probability, the unfavorable probability is 1 minus that figure.

Step 3: Calculate Each Payoff

Payoff = Profit per Unit × Demand. Calculate this separately for the favorable and unfavorable outcome of each alternative, and round to two decimal places.

Alternative	Favorable Payoff	Unfavorable Payoff
Expand Product Line	$4.50 × 32,000 = $144,000.00	$4.50 × 11,000 = $49,500.00
License to Partner	$2.75 × 41,000 = $112,750.00	$2.75 × 19,000 = $52,250.00
Maintain Current Line	$1.90 × 21,000 = $39,900.00	$1.90 × 14,500 = $27,550.00

Step 4: Calculate the Expected Value (EV) for Each Alternative

EV = (Favorable Payoff × P(Favorable)) + (Unfavorable Payoff × P(Unfavorable)). Round each EV to two decimal places.

Alternative	EV Calculation	Expected Value
Expand Product Line	($144,000.00 × 0.68) + ($49,500.00 × 0.32)	$113,760.00
License to Partner	($112,750.00 × 0.55) + ($52,250.00 × 0.45)	$85,525.00
Maintain Current Line	($39,900.00 × 0.82) + ($27,550.00 × 0.18)	$37,677.00

Step 5: What the Completed Diagram Should Show

In your actual Excel template, the finished tree for one alternative (Expand Product Line, shown here as an example) reads like this from left to right:

• Decision node (square) → branch labeled “Expand Product Line”
• State-of-nature node (circle), clearly labeled, splitting into two branches
• Branch 1: “Favorable Market, P = 0.68” → ends in payoff $144,000.00
• Branch 2: “Unfavorable Market, P = 0.32” → ends in payoff $49,500.00
• Expected value of $113,760.00 written at the state-of-nature node for this alternative

Repeat this same structure for each of the other two alternatives off the same decision node. All three expected values should be visible on the completed diagram so the comparison in Section D and the recommendation in Section E are easy to trace back to the tree.

Section D: Summarize the Implications

D1 – Explain Each Step to Determine Expected Value

Walk through the process step by step in plain language. Cover all of the following:

1. Identify payoffs — Explain that each payoff is calculated by multiplying profit per unit by the demand quantity for that branch
2. Apply probabilities — Explain that each payoff is then multiplied by the probability of that demand state occurring
3. Sum the weighted payoffs — Explain that the products from step 2 are added together to produce the expected value for that decision alternative
4. Compare EVs — Explain that the alternative with the highest expected value is selected as the optimal decision

Be specific; reference actual numbers from your analysis where possible.

D2 – Identify Two Limitations

The rubric requires one limitation for a data value and one limitation of the decision tree analysis method itself. Here are strong options for each:

Limitation of a data value (choose one):

• Probabilities are often estimated based on historical data or managerial judgment and may not accurately reflect future conditions — if the probabilities are wrong, the expected values will be misleading
• Profit per unit figures may not account for variable costs, seasonal changes, or market shifts, making them an oversimplification
• Demand figures may be based on forecasts that carry inherent uncertainty

Limitation of decision tree analysis (choose one):

• Decision trees assume static probabilities and payoffs — in reality, these values may shift over time as market conditions change
• The model does not account for risk tolerance — two alternatives with similar EVs may carry very different levels of financial risk
• Decision trees can become overly complex when there are many decision points, making them harder to interpret accurately

C207 Task 2 Worked Example for Section D

What this example is: This is a teaching walkthrough for illustration, not a submittable assignment. The company, the alternatives, and every number below are fictitious and built only to illustrate how the calculations and write-up work.

In Reference to Fictitious Scenario (for illustration only) Under Worked Example for Section C

Summarize the Implications of Your Decision Tree Analysis

D1: Explain Each Step Required to Determine the Expected Value

This section asks you to narrate the process in plain language, using your own numbers as the example. A strong D1 walks through all four steps explicitly:

1. Identify the payoff for each outcome

Each payoff is calculated by multiplying the profit per unit by the demand quantity for that specific branch. For Riverside Outdoor Co.’s “Expand Product Line” alternative, the favorable payoff is $4.50 × 32,000 units, or $144,000.00.

2. Apply the probability to each payoff

Each payoff is then multiplied by the probability of that market condition occurring. The $144,000.00 favorable payoff is weighted by its 0.68 probability, and the $49,500.00 unfavorable payoff is weighted by its 0.32 probability.

3. Sum the weighted payoffs

The two weighted figures are added together to produce the expected value for that alternative: ($144,000.00 × 0.68) + ($49,500.00 × 0.32) = $113,760.00.

4. Compare expected values across alternatives

The same four-step process is repeated for every alternative, and the one with the highest expected value is identified as the data-supported choice. In this fictitious example, Expand Product Line’s $113,760.00 is higher than both License to Partner ($85,525.00) and Maintain Current Line ($37,677.00).

Why this matters for grading Evaluators are looking for the steps themselves, explained in order, with your actual numbers plugged in as evidence — not just a definition of what an expected value is. A D1 that defines EV correctly but never shows the calculation steps applied to the scenario’s own data is a common reason this part gets returned.

D2: Identify Two Limitations

This part requires exactly two limitations, and the two need to be clearly different in kind: one limitation tied to a specific data value used in the analysis, and one limitation tied to decision tree analysis as a method. Mixing these up, or writing two that both critique the data, is a frequent reason this section is sent back.

Limitation Of a Data Value

The favorable and unfavorable market probabilities used in this analysis (for example, 0.68 and 0.32 for the Expand Product Line alternative) are estimates provided by outside market research and are not certainties. If actual market conditions diverge from these estimated probabilities, the expected values calculated here — and therefore the recommendation — may not hold up once real demand is observed.

Limitation Of The Method (Decision Tree Analysis)

Decision tree analysis treats each alternative’s probabilities and payoffs as fixed at a single point in time. It does not account for how market conditions, costs, or competitor behavior might shift after the decision is made, and it does not factor in the company’s tolerance for risk — two alternatives with similar expected values could carry very different levels of financial exposure that the EV figure alone doesn’t capture.

What Students Say; For Part D

Section E: Recommend a Course of Action

• Identify which decision alternative had the highest expected value from your tree
• Recommend that alternative clearly and directly
• Tie the recommendation back to the business question from A1
• Reference the specific EV figures to support your recommendation
• Example format: “Based on the decision tree analysis, [Option X] yields the highest expected value of $[XX.XX] and is therefore recommended as the optimal course of action. This directly addresses the business question of which option maximizes expected profit under uncertain demand conditions.”

Section F: Sources & Citations

• Cite any course textbook, methodology reference, or external source used in APA 7 format
• In-text: (Author, Year)
• Full reference list at the end of the paper
• At minimum, cite any source you reference when explaining decision tree methodology or expected value calculations

Section G: Professional Communication

• Write in formal academic tone throughout
• Run your paper through Grammarly for Education before submitting — the rubric explicitly requires this step and the paper will not pass without it
• Check grammar, punctuation, sentence fluency, and contextual spelling
• Use clear paragraph breaks — one main idea per paragraph

Why C207 Task 2 Gets Sent Back for Revision

Task 2 has one of the higher revision rates among C207’s performance assessments — not because the math is hard, but because the rubric is graded on specific, literal criteria that are easy to gloss over when you’re confident in your numbers. Here’s what evaluators flag most often, in order of frequency:

1. Payoffs or expected values not rounded to two decimal places

This is the single most common reason Task 2 bounces back. WGU’s rubric is explicit about two decimal places — not “approximately,” not rounded to the nearest dollar. If your Excel formulas carry more decimal places into your Word report, go back and round every payoff and EV figure manually before you transcribe them.

2. State-of-nature nodes missing or unlabeled on the diagram

It’s easy to build a tree that’s mathematically correct but visually incomplete. Evaluators are checking for the circles (state-of-nature nodes) to be clearly present and labeled — not just implied by the branching structure. If your diagram only shows the decision node and the end payoffs, it will come back.

3. A2 justification that doesn’t reference your specific scenario

A generic justification — “decision tree analysis is appropriate because there is uncertainty and multiple options” — technically touches the rubric language but won’t pass. Evaluators want to see your justification anchored to the MPC scenario specifically: the three drug-line alternatives, the DMA probability data, the favorable/unfavorable market split. Name the actual options and data source in your justification, not just the concept.

4. D2 limitations that don’t clearly separate “data value” from “method”

The rubric asks for two distinct limitations: one tied to a specific data value (probability, profit per unit, or demand) and one tied to decision tree analysis as a method. A common mistake is writing two limitations that both sound like data critiques, or being vague enough that it’s unclear which is which. Label them explicitly if you need to — clarity here matters more than elegant prose.

5. Excel file not submitted alongside the Word document

Easy to forget under deadline pressure, but the rubric requires your completed Decision Tree Analysis Resources spreadsheet as evidence of your calculations. A Word report alone, even a strong one, is an incomplete submission.

What To Submit

You have two files to submit for this task:

Final Checklist Before Submitting

• Student ID entered and correct scenario/data loaded in Excel
• Business question is scenario-specific and decision-tree-appropriate
• Decision tree justification references specific scenario details
• All three data values listed: demands, profits per unit, and probabilities
• Probabilities sum to 1.0
• All payoffs calculated and expressed to two decimal places
• All expected values calculated and expressed to two decimal places
• State-of-nature nodes labeled on the diagram
• Each step of the EV calculation process explained in writing
• One data value limitation and one decision tree limitation included
• Recommendation tied directly to the highest EV result
• APA in-text citations and reference list included
• Grammarly review completed
• Word file saved as .docx
• Excel file saved and ready to submit alongside the Word file
• Similarity report reviewed before final submission

FAQ

What is WGU C207 Task 2?

WGU C207 Task 2 is a decision tree analysis performance assessment in the Data-Driven Decision Making course. Students analyze a pharmaceutical company scenario (MPC) to determine whether developing a new drug line, exploiting an existing drug line, or making no changes produces the highest expected profit. The submission requires a written Word report covering sections A through G, plus a completed Excel decision tree diagram submitted as a separate file.

What scenario is used in WGU C207 Task 2?

C207 Task 2 uses the Major Pharmaceutical Company (MPC) scenario, in which a drug company must choose between three strategic options: developing a new drug line, exploiting an existing drug line, or making no changes to the current product line. Each option is evaluated against favorable and unfavorable market conditions using probabilities, demand figures, and profit-per-unit values that are personalized to your student ID in the Excel dataset.

How do you calculate expected value in WGU C207 Task 2?

The expected value (EV) for each decision alternative is calculated by multiplying each payoff by its associated probability, then summing the results. The payoff itself is calculated by multiplying profit per unit by the demand quantity for that branch. For example: EV = (Payoff_favorable × P_favorable) + (Payoff_unfavorable × P_unfavorable). The decision alternative with the highest EV is the recommended course of action. All payoffs and EVs must be expressed to two decimal places per the rubric.

How long is WGU C207 Task 2?

WGU does not specify a page minimum for C207 Task 2, but a complete rubric-aligned submission typically runs 3 to 5 pages in the Word document, plus the Excel file with the completed decision tree diagram. The written report must cover all seven sections (A through G), and each section should include enough detail to address the rubric criteria — particularly sections A2 (justification), D1 (EV steps), and D2 (limitations), which are the most frequently returned.

What are the most common reasons C207 Task 2 gets returned for revision?

The most common revision triggers for C207 Task 2 are: payoffs or expected values not rounded to two decimal places, state-of-nature nodes missing from the decision tree diagram, the justification in A2 not referencing specific scenario details, and the limitations in D2 being too vague or not clearly distinguishing between a data value limitation and a method limitation. Submitting the Excel file with the decision tree visible is also frequently overlooked — both files are required.

Do you need Excel for WGU C207 Task 2?

Yes — Microsoft Excel is required for WGU C207 Task 2 and cannot be substituted with Google Sheets or another spreadsheet tool. The “Decision Tree Analysis Resources” file only functions correctly in the desktop version of Excel, which is available free to all WGU students through Microsoft Office 365. You must enter your student ID in the “Start” tab to load your personalized scenario and dataset before beginning the analysis.