Modeling in R
Modeling in R


The linear model, and its extensions, forms the backbone of statistical analysis. In this course we cover Linear Regression using `lm`, Generalized Linear Models using `glm` and model assessment using `AIC`, `BIC` and other measures. The focus will be mainly on applied programming, though theoretical properties and derivations will be taught where appropriate. Attendees should already have a basic knowledge of linear models and have R and RStudio installed, along with the `UsingR`, `ggplot2` and `coefplot` packages. Linear Models: Learn about the best fit line, Understand the formula interface in R, Understand the design matrix, Fit Models with `lm`, Visualize the coefficients with `coefplot`, Make predictions on new data. Generalized Linear Models: Learn about Logistic Regression for classification, Learn about Poisson Regression for count data, Fit models with `glm`, Visualize the coefficients with `coefplot`, Model Assessment, Compare models, `AIC`,’BIC`


Jared Lander is the Founder and CEO of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fund raising to finance and humanitarian relief efforts.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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