Het schoolmodel interpreteren

Eerder heb je een model gebouwd om de groei in wiskundekennis van leerlingen te voorspellen. Nu ga je de coëfficiënten uit het model halen en bekijken.

Deze schattingen van coëfficiënten bevatten onzekerheid en een 95%-betrouwbaarheidsinterval (BI) geeft die onzekerheid weer. Als het 95%-betrouwbaarheidsinterval van een parameter geen nul bevat, dan wijkt die parameter waarschijnlijk statistisch af van nul. De DataCamp-cursus Foundations of Inference gaat dieper in op het interpreteren van modellen.

Deze oefening maakt deel uit van de cursus

Hiërarchische en Mixed-Effects-modellen in R

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# Extract coefficents
___ <-
    tidy(___, conf.int = ___)

# Print coefficents
print(___)

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Deze oefening maakt deel uit van de cursus

Hiërarchische en Mixed-Effects-modellen in R

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4.7+

Begin de cursus gratis

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.

Exercise 1: Wat is een hiërarchisch model?Exercise 2: Voorbeelden van hiërarchische gegevenssets Exercise 3: Studentgegevens op meerdere niveaus Exercise 4: Meerdere niveaus verkennen: Klassen en scholen Exercise 5: Onderdelen van een regressie Exercise 6: Intercepts Exercise 7: Helling en multiple regression Exercise 8: Random-effects in regressies met schoolgegevens Exercise 9: Intercepts met random effects Exercise 10: Random-effect hellingen Exercise 11: Een schoolmodel bouwen Exercise 12: Het schoolmodel interpreteren

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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.

Exercise 1: Linear mixed effect model- Birth rates data Exercise 2: Building a lmer model with random effects Exercise 3: Including a fixed effect Exercise 4: Random-effect slopes Exercise 5: Uncorrelated random-effect slope Exercise 6: Fixed- and random-effect predictor Exercise 7: Understanding and reporting the outputs of a lmer Exercise 8: Comparing print and summary output Exercise 9: Extracting coefficients Exercise 10: Displaying the results from a lmer model Exercise 11: Statistical inference with Maryland crime data Exercise 12: Visualizing Maryland crime data Exercise 13: Rescaling slopes Exercise 14: Null hypothesis testing Exercise 15: Controversies around P-values Exercise 16: Model comparison with ANOVA

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.

Exercise 1: Crash course on GLMs Exercise 2: Logistic regression Exercise 3: Poisson Regression Exercise 4: Plotting GLMs Exercise 5: Binomial data Exercise 6: Toxicology data Exercise 7: Marketing example Exercise 8: Calculating odds-ratios Exercise 9: Count data Exercise 10: Internet click-throughs Exercise 11: Chlamydia by age-group and county Exercise 12: Displaying chlamydia results

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.

Exercise 1: An introduction to repeated measures Exercise 2: Paired t-test Exercise 3: Repeated measures ANOVA Exercise 4: Sleep study Exercise 5: Exploring the data Exercise 6: Building models Exercise 7: Comparing regressions and ANOVAs Exercise 8: Plotting results Exercise 9: Hate in NY state?Exercise 10: Exploring NY hate data Exercise 11: Building the model Exercise 12: Interpreting model results Exercise 13: Displaying the results Exercise 14: Hierarchical models in R review Exercise 15: Conclusion