This case deals with the creation of a predictive model to estimate if the professor is receiving a fair quote for an engagement ring.
Download case via Rice’s student package. You can preview the case via this attached PDF
Before you build a model, you need to conduct extensive exploratory data analysis (EDA).
In your approach and EDA you need to account for the following:
1️⃣ Dummy Variable Trap
The Dummy Variable trap is a scenario in which the independent variables are multicollinear - a scenario in which two or more variables are highly correlated; in simple terms one variable can be predicted from the others.
2️⃣ Structural breaks in the data
In statistics, a structural break is an unexpected change over time in the parameters of regression models, which can lead to huge forecasting errors and unreliability of the model in general.
3️⃣ Goodness of Fit vs Model Correctness
Goodness of fit is how well a model can "predict" data points you've already used to estimate regression parameters. It provides predictive accuracy and describes how well a model can predict new data points, for which it hasn't yet seen the true value of the dependent variable.
Models correctness accounts for any forecasting errors as well as bias that make a model unreliable.
This is an Excel project. Meaning, the EDA and model need to be completed in Excel
The case will be presented and defended in teams by
Presentations should include the following sections :
The “what is the problem” section
What is the problem you are trying to solve?
What is the outcome and answer to the problem
The “what is the data” section
What data are you using?
Size, dimensions, and data types
Data quality, integrity, completeness?
Summary statistics on numeric variables
Description of categorical variables
The “what is analytics approach” section
How did you solve the problem?
Analytics models used
The “business implications & recommendations” section
What are the implications and interpretation of your findings