Demand forecasting and revenue modeling in economics

Purpose: This written memo aims to quantify consumer behavior by developing a demand function and forecasting revenue under different pricing and market scenarios. You will apply quantitative tools such as elasticity estimation, revenue modeling, and regression analysis to build a data-driven understanding of demand patterns.

Goals: Model and interpret demand, forecast revenues, and evaluate potential sales scenarios.

Overall Task

Businesses rely on demand estimation to make pricing and production decisions that maximize revenue. For this research-supported Executive Memo, you will use ChatGPT to simulate realistic market data for a chosen product or service, then apply economic modeling to forecast revenues and evaluate potential sales scenarios. The memo will be in APA format with in-text citations and a reference list.

Task 1:

Your consulting firm has been engaged to evaluate the feasibility of a new company entering the market. This must be a different scenario than the one used in Executive Memo 1.

Putting AI to Work for Simulation

Using the free version of .

Personalize your dataset by defining your own product, market, and price range (bolded and [bracketed] content below).

ChatGPT Prompt to Simulate Data:

Create a realistic dataset showing the relationship between price (P) and quantity demanded (Q) for a product or service.

Use the following parameters:

Product or service: [enter product/service, e.g., iced coffee, gym memberships, concert tickets]

Market or city: [enter market, e.g., Austin, Chicago, or national]

Price range: $[min] to $[max]

Number of price points: [enter a number between 6 and 10]

Average monthly demand at lowest price: [enter an estimated number, e.g., 10,000 or 100,000 units]

Requirements:

Demand must decrease as price increases.
Include minor random variation to reflect realistic market noise.
Present results in a two-column table with headers: Price ($) and Quantity Demanded (units/month)
Make the data realistic and consistent with economic logic.

**Download the data and paste it into an Excel spreadsheet.**

Follow-up ChatGPT Prompt:

Compute the price elasticity of demand at the mean price and quantity
Estimate the linear demand equation Q=a-bP.
Compute Total Revenue (TR = P Q) and Marginal Revenue (MR = TR / Q).
Graph the demand, TR, and MR curves to visualize relationships between price and revenue.
Select two price intervals and compute elasticity using the midpoint method.
Do not include interpretations.

Important Reminder:

You are not permitted to ask ChatGPT to write the content. If the assignment is written by AI, a zero will be awarded, and no resubmission will be accepted. Faculty have access to Turnitin reports that assess AI Writing.
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Task 2 (Demand Function Construction):

Report the linear demand equation Q=a-bP. Explain what this equation means in economic termsspecifically, how price affects quantity demanded and how the parameters a and b reflect consumer behavior and market conditions. (Textbook research support required).

This section should be 1/2 page in length with in-text citations to support economic concepts.

Task 3 (Revenue Modeling):

Report Total Revenue (TR = P Q) and Marginal Revenue (MR = TR / Q).
Explain the economic meaning of each equation, describing how total revenue changes with price and quantity and how marginal revenue reflects the additional revenue from selling one more unit. (Textbook research support required).
Paste the ChatGPT demand, TR, and MR curves charts into the paper. Place this under each figure:
Figure #. Title of the chart in sentence case and italics.
Note. Created by the author using content generated by ChatGPT (OpenAI, personal communication, Month Day, Year).
Discuss the shapes and relationships of the demand, total revenue, and marginal revenue curves, explaining how they interact to illustrate price elasticity and firm pricing decisions. (Textbook research support required).

This section should be 1 page in length (excluding the graphs) with in-text citations to support economic concepts.

Task 4 (Regression and Forecasting):

Report the two price intervals and elasticity using the midpoint method.
Interpret whether demand is elastic, inelastic, or unit elastic based on the relationship between price and total revenue. Explain what each elasticity type indicates about consumer responsiveness to price changes and how this affects pricing and revenue decisions. (Textbook research support required).

This section should be 1/2 page in length with in-text citations to support economic concepts.

Task 5 (Price Elasticity Estimation):

Using the data you saved from ChatGPT, run a linear regression in Excel: Q as dependent variable (Y), P as independent variable (X). Use the Data Analysis Regression tool. Be sure to check Labels and set the Confidence level at 95%
You must submit the Excel file with your work.
Paste the Excel output into your paper.
Use the regression results to forecast demand and total revenue for a new price scenario, such as a 10% increase in price. Explain how the regression coefficients help estimate the expected change in quantity demanded and total revenue and interpret what the forecast implies about pricing strategy and market response. (Textbook research support required).

This section should be 1 page in length (excluding Regression data) with in-text citations to support economic concepts.

Task 6 (Forecast Evaluation):

Discuss the reliability of your forecast by examining key factors that affect accuracy. Interpret the R2 value and explain what it indicates about the models fit. Evaluate the potential impact of omitted variables such as income, seasonality, or competition on your results. Assess the realism of your simulated assumptions and how they influence the credibility of your demand and revenue predictions. (Textbook research support required).