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?

Examples of hierarchical datasets

Multi-level student data

Exploring multiple-levels: Classrooms and schools

Parts of a regression

Intercepts

Slopes and multiple regression

Random-effects in regressions with school data

Random-effect intercepts

Random-effect slopes

Building the school model

Interpreting the school model

Overview and Introduction to Hierarchical and Mixed 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 data

Building a lmer model with random effects

Including a fixed effect

Uncorrelated random-effect slope

Fixed- and random-effect predictor

Understanding and reporting the outputs of a lmer

Comparing print and summary output

Extracting coefficients

Displaying the results from a lmer model

Statistical inference with Maryland crime data

Visualizing Maryland crime data

Rescaling slopes

Null hypothesis testing

Controversies around P-values

Model comparison with ANOVA

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 GLMs

Logistic regression

Poisson Regression

Plotting GLMs

Binomial data

Toxicology data

Marketing example

Calculating odds-ratios

Count data

Internet click-throughs

Chlamydia by age-group and county

Displaying chlamydia results

Generalized Linear Mixed Effect Models

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 measures

Paired t-test

Repeated measures ANOVA

Sleep study

Exploring the data

Building models

Comparing regressions and ANOVAs

Plotting results

Hate in NY state?

Exploring NY hate data

Building the model

Interpreting model results

Displaying the results

Options

Fixed-effect models

Mixed-effect models

Hierarchical models in R review

Conclusion

Repeated Measures

Illinois chlamydia data

Maryland crime data

Classroom data

Birth rate data

New York hate crime data

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.

Generalized Linear Models in R

Discover what a random effect is and how to use one, as well as how to use generalized linear mixed-effect regressions and repeated-measures analysis.

Hierarchical and Mixed Effect Models in R

In this course you will learn to fit hierarchical models with random effects.

Hierarchical and Mixed Effects Models in R

Estadístico en R

Data Scientist

Repeated measures

Paired t-test in R

Repeated measures in R

Extension  to lmer and glmer

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_6056/subtitles/course_6056_913c8872cb2b1e39df34c962fabec038.vtt

Repeated Measures - An introduction to repeated measures

An introduction to repeated measures

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