Click-through su Internet

Un tema ricorrente in tutto il corso è la struttura annidata dei dati. In questo case study, esaminerai il numero di utenti che fanno click-through su un link verso un sito web prima e dopo un redesign. Questi utenti sono annidati all'interno di gruppi di test. Poi esaminerai i risultati e formulerai una raccomandazione al cliente.

Per questo redesign, analizzerai il comportamento sul web di 10 persone in 4 diversi focus group, per un totale di 40 persone. Vuoi sapere se il numero di clicks su diverse pagine è cambiato passando dalla vecchia alla nuova webpage, tenendo conto dei groups.

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Modelli gerarchici e a effetti misti in R

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# Load lmerTest
library(lmerTest)

# Fit a Poisson glmer
glmer_out <- ___

# Examine output with summary()
___

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Questo esercizio fa parte del corso

Modelli gerarchici e a effetti misti in R

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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: What is a hierarchical model?Exercise 2: Examples of hierarchical datasets Exercise 3: Multi-level student data Exercise 4: Exploring multiple-levels: Classrooms and schools Exercise 5: Parts of a regression Exercise 6: Intercepts Exercise 7: Slopes and multiple regression Exercise 8: Random-effects in regressions with school data Exercise 9: Random-effect intercepts Exercise 10: Random-effect slopes Exercise 11: Building the school model Exercise 12: Interpreting the school model

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: Corso intensivo sui GLM Exercise 2: Regressione logistica Exercise 3: Regressione di Poisson Exercise 4: Rappresentare graficamente i GLM Exercise 5: Dati binomiali Exercise 6: Dati di tossicologia Exercise 7: Esempio di marketing Exercise 8: Calcolare gli odds-ratio Exercise 9: Dati di conteggio Exercise 10: Click-through su Internet

Esercizio in corso

Exercise 11: Clamidia per fascia d’età e contea Exercise 12: Visualizzare i risultati sulla clamidia

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