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Learn R programming
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Module 11
Advanced Statistical Modeling
1
Understanding Mixed-Effects Models for Non-Independent Data
Explain the rationale for using mixed-effects models for non-independent data.
2
Fitting Random-Intercept Models with `lmer()`
Fit a random-intercept linear mixed-effects model using `lme4::lmer()`.
3
Understanding Mixed-Effects Model Output
Interpret fixed effects and variance components from a mixed-effects model summary.
4
Predicting with Regression Models
Generate predictions from a fitted regression or mixed-effects model.
5
Visualizing Predictions and Uncertainty
Visualize model predictions and uncertainty intervals.
6
Tidying `lmer` Model Results with `broom.mixed`
Extract and report results from `lmer` models using `broom.mixed::tidy()`.
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Regression Modeling
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Reproducible Project Management