Uji t berpasangan

Dalam video, Anda mempelajari bahwa uji t berpasangan bisa lebih kuat daripada uji t biasa. Pada latihan ini, Anda akan melihat contoh yang mendemonstrasikan hal tersebut. Langkah pertama adalah mensimulasikan data. Data serupa bisa berasal dari berat badan seseorang sebelum dan sesudah pengobatan, atau jumlah uang yang dibelanjakan pelanggan sebelum dan sesudah melihat iklan.

Data simulasi memungkinkan Anda mengetahui karakteristik data dan memeriksa apakah model berperilaku sesuai harapan. R menyediakan banyak sebaran termasuk normal. Data simulasi Anda akan memiliki varians yang tidak sama (yakni, simpangan bakunya berbeda). Langkah kedua adalah menganalisis data dengan uji t berpasangan dan uji t biasa. Terakhir, Anda akan ditanya tentang hasil dari uji t berpasangan.

Sebagai bagian dari langkah pertama, Anda akan "menetapkan seed" untuk pembangkit bilangan acak R. Ini memastikan Anda mendapatkan angka yang sama setiap kali menjalankan kode dan perangkat lunak DataCamp dapat menilai kode Anda dengan benar.

Latihan ini adalah bagian dari kursus

Model Hierarki dan Mixed Effects di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Set the seed to be 345659
set.seed(___)

# Model 10 individuals 
n_ind <- ___

# simulate before with mean of 0 and sd of 0.5
before <- rnorm(n = n_ind, mean = ___, sd = ___)
# simulate after with mean effect of 4.5 and standard devation of 5
after  <- ___ + rnorm(n = n_ind, mean = ___, sd = ___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model Hierarki dan Mixed Effects di R

SkillTag.level.advancedSkillTag.label

4.7+

Mulai Kursus 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: 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: 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: Pengantar repeated measures Exercise 2: Uji t berpasangan

Latihan Saat Ini

Exercise 3: Repeated measures ANOVA Exercise 4: Studi tidur Exercise 5: Mengeksplorasi data Exercise 6: Membangun model Exercise 7: Membandingkan regresi dan ANOVA Exercise 8: Memvisualisasikan hasil Exercise 9: Kebencian di negara bagian NY?Exercise 10: Menjelajahi data kejahatan kebencian di NY Exercise 11: Membangun model Exercise 12: Menafsirkan hasil model Exercise 13: Menampilkan hasil Exercise 14: Tinjauan model hierarkis di R Exercise 15: Kesimpulan