Generalized Linear Models in R
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
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Course Description
Linear regression serves as a workhorse of statistics, but cannot handle some types of complex data. A generalized linear model (GLM) expands upon linear regression to include non-normal distributions including binomial and count data. Throughout this course, you will expand your data science toolkit to include GLMs in R. As part of learning about GLMs, you will learn how to fit model binomial data with logistic regression and count data with Poisson regression. You will also learn how to understand these results and plot them with ggplot2.
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GLMs, an extension of your regression toolbox
FreeThis chapter teaches you how generalized linear models are an extension of other models in your data science toolbox. The chapter also uses Poisson regression to introduce generalize linear models.
Limitations of linear models50 xpAssumptions of linear models50 xpRefresher on fitting linear models100 xpPoisson regression50 xpFitting a Poisson regression in R100 xpComparing linear and Poisson regression100 xpIntercepts-comparisons versus means100 xpBasic lm() functions with glm()50 xpApplying summary(), print(), and tidy() to glm100 xpExtracting coefficients from glm()100 xpPredicting with glm()100 xp - 2
Logistic Regression
This chapter covers running a logistic regression and examining the model outputs.
Overview of logistic regression50 xpFitting a logistic regression100 xpExamining & interpreting logistic regression outputs100 xpBernoulli versus binomial distribution50 xpBernoulli versus binomial50 xpSimulating binary data100 xpLong-form logistic regression input100 xpWide-form input logistic regression100 xpComparing logistic regression outputs50 xpLink functions-Probit compared to logit50 xpProbit versus logit50 xpFitting probits and logits100 xpSimulating a logit100 xpSimulating a probit100 xp - 3
Interpreting and visualizing GLMs
This chapter teaches you about interpreting GLM coefficients and plotting GLMs using ggplot2.
Poisson regression coefficients50 xpPoisson link50 xplm vs. Poisson coefficients100 xpPlotting Poisson regression50 xpPoisson regression plotting100 xpUnderstanding output from logistic regression50 xpUnderstanding odds ratios50 xpExtracting and interpreting odds-ratios100 xpOdds-ratios & confidence intervals in the Tidyverse100 xpggplot2 and binomial regression50 xpDefault trend lines100 xpMethods for trend lines100 xpComparing probits and logits100 xp - 4
Multiple regression with GLMs
In this chapter, you will learn how to do multiple regression with GLMs in R.
Multiple logistic regression50 xpFitting a multiple logistic regression100 xpBuilding two models100 xpComparing regression outputs50 xpComparing variable order100 xpFormulas in R50 xpMultiple slopes100 xpIntercepts100 xpMultiple intercepts100 xpAssumptions of multiple logistic regression50 xpSimpson's paradox100 xpNon-linear logistic regression100 xpConclusion50 xp
In the following tracks
StatisticianDatasets
Bus Commuter dataset
David Campos
Chester Ismay
Shon InouyePrerequisites
Intermediate Regression in R
Richard Erickson
Data Scientist
Richard helps people to experience and understand their increasingly numerical world. For his day job he develops new quantitative methods for monitoring and controlling invasive species as well as helping other scientists analyze and understand their data. He has worked on diverse datasets ranging from continent wide species distributions to pesticides in playa wetlands. After hours, he teaches SCUBA Diving as a NAUI Instructor. He has been a member of "UserR" since 2007.
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Lloyds Banking Group
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Harvard Business School
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Ronald Bowers
Decision Science Analytics, USAA
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