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This course begins by reviewing slopes and intercepts in linear regressions before moving on to random-effects. You'll learn what a random effect is and how to use one to model your data. Next, the course covers linear mixed-effect regressions. These powerful models will allow you to explore data with a more complicated structure than a standard linear regression. The course then teaches generalized linear mixed-effect regressions. Generalized linear mixed-effects models allow you to model more kinds of data, including binary responses and count data. Lastly, the course goes over repeated-measures analysis as a special case of mixed-effect modeling. This kind of data appears when subjects are followed over time and measurements are collected at intervals. Throughout the course you'll work with real data to answer interesting questions using mixed-effects models.
Overview and Introduction to Hierarchical and Mixed ModelsFree
The first chapter provides an example of when to use a mixed-effect and also describes the parts of a regression. The chapter also examines a student test-score dataset with a nested structure to demonstrate mixed-effects.What is a hierarchical model?50 xpExamples of hierarchical datasets100 xpMulti-level student data100 xpExploring multiple-levels: Classrooms and schools100 xpParts of a regression50 xpIntercepts100 xpSlopes and multiple regression100 xpRandom-effects in regressions with school data50 xpRandom-effect intercepts100 xpRandom-effect slopes100 xpBuilding the school model100 xpInterpreting the school model100 xp
Linear Mixed Effect Models
This chapter providers an introduction to linear mixed-effects models. It covers different types of random-effects, describes how to understand the results for linear mixed-effects models, and goes over different methods for statistical inference with mixed-effects models using crime data from Maryland.Linear mixed effect model- Birth rates data50 xpBuilding a lmer model with random effects100 xpIncluding a fixed effect100 xpRandom-effect slopes100 xpUncorrelated random-effect slope100 xpFixed- and random-effect predictor100 xpUnderstanding and reporting the outputs of a lmer50 xpComparing print and summary output50 xpExtracting coefficients100 xpDisplaying the results from a lmer model100 xpStatistical inference with Maryland crime data50 xpVisualizing Maryland crime data100 xpRescaling slopes100 xpNull hypothesis testing100 xpControversies around P-values50 xpModel comparison with ANOVA100 xp
Generalized Linear Mixed Effect Models
This chapter extends linear mixed-effects models to include non-normal error terms using generalized linear mixed-effects models. By altering the model to include a non-normal error term, you are able to model more kinds of data with non-linear responses. After reviewing generalized linear models, the chapter examines binomial data and count data in the context of mixed-effects models.Crash course on GLMs50 xpLogistic regression100 xpPoisson Regression100 xpPlotting GLMs100 xpBinomial data50 xpToxicology data100 xpMarketing example100 xpCalculating odds-ratios100 xpCount data50 xpInternet click-throughs100 xpChlamydia by age-group and county100 xpDisplaying chlamydia results100 xp
This chapter shows how repeated-measures analysis is a special case of mixed-effect modeling. The chapter begins by reviewing paired t-tests and repeated measures ANOVA. Next, the chapter uses a linear mixed-effect model to examine sleep study data. Lastly, the chapter uses a generalized linear mixed-effect model to examine hate crime data from New York state through time.An introduction to repeated measures50 xpPaired t-test100 xpRepeated measures ANOVA100 xpSleep study50 xpExploring the data100 xpBuilding models100 xpComparing regressions and ANOVAs100 xpPlotting results100 xpHate in NY state?50 xpExploring NY hate data100 xpBuilding the model100 xpInterpreting model results100 xpDisplaying the results100 xpHierarchical models in R review100 xpConclusion50 xp
In the following tracksStatistician
DatasetsIllinois chlamydia dataMaryland crime dataClassroom dataBirth rate dataNew York hate crime data
PrerequisitesGeneralized Linear Models in R
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.