Bowie State University October 30, 2022
Department of Accounting, Finance, and Economics
ECON 483-557 – QUANTITATIVE METHODS FOR DECISION MAKING – Fall 2022
Instructor: Drs. Badibanga, Ntembe and Faux
DATA ANALYTICS CASE STUDY – ESTIMATING AND PREDICTING THE RESALE PRICE OF A CAR
Status: Required
Due Date: December 5, 2022
Project Goals
The main goal of this project is to help students to apply quantitative analysis techniques including statistical methods particularly predictive analytics to empirically analyze and predict the relationship between the resale price/value of a car and a set of covariates that affect such price/value. Each student is expected to analyze and predict the relationship, draw conclusions, make policy recommendations and compose a written report.
Learning objectives
Upon a successful completion of this research project, the student will be able to:
Describe the relationship between the resale price/value of a car and factors that affect it;
Develop a regression model to analyze the relationship between the resale price/value of a car and a set of covariates;
Collect relevant data and apply data analytics to describe empirically the relationship;
Use predetermined values of the covariates to predict the resale price of a car in the future;
Draw informed conclusions.
Problem Statement
Jennifer Stance is contemplating the purchase of a new or used car. One of her primary concerns is how well the car will maintain its value. In particular, she is wondering how certain options affect a car’s resale value,
One car Jennifer is considering is Chevrolet Camaro two-door coupe. She prefers a car with a five-speed manual transmission over one with an automatic transmission but would consider the automatic transmission if this significantly increased the car’s resale value. Living in Northern Maine, she is also somewhat indifferent about purchasing a car with air conditioning, but again would adding this option if it significantly increased the car’s resale value. Finally, Jennifer would really like to indulge a bit and upgrade to a leather interior, but she is not clear on the value of doing this.
To analyze this situation, Jennifer contacted in 2021 an old high school friend that works at a used car lot. He agreed to provide her with data on all two-door Camaro coupes that were sold this year. She entered this information into a spreadsheet attached to this project.
ASSIGNMENT
Task 1 – Data Cleaning
The dataset to be used for this project includes some variables that are not ready for the analysis. Your first task consists of converting them into formats that will support your analysis. Some variables are qualitative and consist mostly of names, labels or yes/no statements. Those variables are “Type of Transmission”, “Air Conditioning”, and “Leather Interior.” You need to redefine those variables such that:
Type of transmission is “1” if automatic and “0” otherwise (i.e. if manual.
Air conditioning is “1” if yes and “0” otherwise.
Leather Interior is “1” if yes and “0” otherwise.
The other variable that need some cleaning is the Age of the car. The age of the car is measured in the Year in which the car was made. You have to convert this Make Year variable into the number of years to measure the age of a car. For instance, a car made in 2018 is 3 year old .
Task 2 – Data Visualization
Use Excel or other software package to develop the scatter plot of the sales price versus mileage. Does there appear to be a linear relationship between these two variables?
Task 3 – Predictive Analytics
Develop and estimate a regression model to help Jennifer assess the factors that affect a Camaro’s resale value. The dependent variable is the price of the car while the independent variables are Air conditioning (Yes = 1; No = 0), Type of Transmission (Automatic = 1; Manual = 0), Leather (Yes = 1; No = 0), age of the car, mileage.
The model to be estimated is given by
where:
Y is Sales Price; is Mileage; is Age; is the dummy of Type of Transmission; is the dummy of the Air Conditioning (AC), and is the dummy for the Leather;
is the intercept;
(i = 1, …, 5) is the slope associated with the independent variable
Next, estimate the model and assess the quality of your results of estimation in term of the fitness of the regression model and the hypothesis test on each estimated coefficient .
Task 4 – Interpretation of Results
Explain in plain English to Jennifer how to use the regression model and what the estimated parameters mean .
Task 4 – Report
Write a paper to present the results of your analysis and make policy recommendations for the determination of the resale price/value of a car. Each project must include at least 5 pages excluding title page, cover page and references. It will be written using the following guidelines and contents:
Title page
Introduction: Problem of the proposed study, purpose and justification of the study (15%)
Data analytics – various calculations and estimations (45%)
Interpretation of results (15%)
Findings and conclusion. (10%)
Appendices: Tables and Figures. (5%)
References (5%)
The project will be written using the APA style.
