PREDICTING HOUSEHOLD INCOME IN NYC
The goal of this collaborative project was to use census data to create two sensible models for predicting median household income and income per capita in New York City and the surrounding area.
Two analytical approaches, Stepwise Selection and Shrinkage Methods, were used to select a subset of predictors. Using the variables selected, we performed multiple linear regression to model median household income and income per capita as a linear combination of the potential predictors. Starting from that baseline model, we added interaction terms and performed transformations on variables based on exploratory analysis and model diagnosis. Finally, an interpretation for each model was provided.
Client: Self
Date: 2018
Class: Statistical Learning, Brown University
Collaborators: Yiwen Shen and Zhiwei Zhang
Instructor: Alice Paul, PhD
The goal of this collaborative project was to use census data to create two sensible models for predicting median household income and income per capita in New York City and the surrounding area.
Two analytical approaches, Stepwise Selection and Shrinkage Methods, were used to select a subset of predictors. Using the variables selected, we performed multiple linear regression to model median household income and income per capita as a linear combination of the potential predictors. Starting from that baseline model, we added interaction terms and performed transformations on variables based on exploratory analysis and model diagnosis. Finally, an interpretation for each model was provided.
Client: Self
Date: 2018
Class: Statistical Learning, Brown University
Collaborators: Yiwen Shen and Zhiwei Zhang
Instructor: Alice Paul, PhD