Datasets containing nonhomogenous groups of samples present a challenge to linear models. In particular, such datasets violate the assumption that there is a linear relationship between the independent and dependent variables. If the data is grouped into distinct clusters, linear models may predict responses that fall in between the clusters. These predictions can be quite far from the targets depending on how the data is structured. In this post, a method is presented for automatically handling nonhomogenous datasets using linear models.
A problem that frequently arises when applying linear models is that of multicollinearity. The term multicollinearity describes the phenomenon where one or more features in the data matrix can be accurately predicted using a linear model involving others of the features. The consequences of multicollinearity include numerical instability due to ill-conditioning, and difficulty in interpreting the regression coefficients. An approach to decorrelate features is presented using the Gram-Schmidt process.
This chapter explores recessions in the United States of America. Datasets are collected from a variety of locations including the Federal Reserve Economic Data (FRED) and from the website of Yale professor and Nobel laureate Dr. Robert J. Shiller. A classifier model is constructed which predicts recessions and this model is analyzed for useful insights.
Recently, I have been experimenting with windowing functions for time series data. While trying out my code, I came up with a nice and (somewhat) thought-provoking plot.
In this post, survey data collected from several speed dating events is analyzed. The events were conducted between 2002 and 2004 by two professors from Columbia University: Ray Fisman and Sheena Iyengar. In addition to questions about personal interests, the survey includes academic and occupational questions as well.
In this chapter, vital statistics for the United States of America are explored. The Center for Disease Control maintains several datasets containing vital statistics for the nation. These datasets contain records of deaths organized by year. Each record includes age, gender, race, cause of death, and other details. This chapter explores data for the year 2016.
In this chapter, forenames in the USA are considered. The United States Social Security Administration (SSA) makes available a dataset containing information about Social Security records. The dataset contains counts of the number of records that exist for a specific first name and birth year.