Membandingkan kelompok

Dalam latihan ini, Anda akan membandingkan distribusi data antar kelompok.

Himpunan data PlantGrowth dari paket datasets memuat hasil dari sebuah percobaan tentang hasil panen. Himpunan data ini berisi dua variabel:

group - menunjukkan apakah hasil tersebut berasal dari kelompok kontrol atau salah satu dari dua kelompok perlakuan,
weight - bobot kering tanaman yang digunakan untuk mengukur hasil panen.

Ingat bahwa tapply() dapat digunakan untuk menghitung metrik antar kelompok.

Sebagai contoh,

tapply(df$x, df$grp, FUN = median)

mengembalikan nilai median x pada setiap grp dari data frame df.

Tugas Anda adalah menghitung mean dan memvisualisasikan lima statistik umum dari bobot hasil panen. Visualisasi ini akan membantu Anda membandingkan perbedaan pada variabel respons antar kelompok.

Latihan ini adalah bagian dari kursus

Latihan Pertanyaan Wawancara Statistik di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate means across groups
tapply(___, PlantGrowth$group, FUN = ___)

# Graphically compare statistics across groups
___(___ ~ group, data = ___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Latihan Pertanyaan Wawancara Statistik di R

SkillTag.level.advancedSkillTag.label

4.7+

Mulai Kursus Gratis

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Exercise 1: Discrete distributions Exercise 2: Probability functions Exercise 3: Bernoulli trials Exercise 4: Binomial distribution Exercise 5: Continuous distributions Exercise 6: Uniform distribution Exercise 7: Shape of normal distribution Exercise 8: Sample from normal distribution Exercise 9: Central limit theorem Exercise 10: Law of large numbers Exercise 11: Simulating central limit theorem

If the job description appeals to you review descriptive statistics before the interview. In this chapter, you will practice exploratory data analysis (EDA) using natural gas prices and data from a survey analysis.

Exercise 1: Descriptive statistics Exercise 2: Centrality measures Exercise 3: Variability measures Exercise 4: Categorical data Exercise 5: Survey analysis Exercise 6: Data encoding Exercise 7: Time series Exercise 8: Time series object Exercise 9: Wrangling time series Exercise 10: Principal Component Analysis Exercise 11: PCA - rotation Exercise 12: PCA - dimension reduction

March confidently into your job interview after reviewing confidence intervals. We'll review the t-test, ANOVA, and normality tests to prepare you for statistics-based coding questions.

Exercise 1: Uji normalitas Exercise 2: Uji Shapiro–Wilk Exercise 3: Plot Q-Q Exercise 4: Inferensi untuk sebuah mean Exercise 5: Interval kepercayaan Exercise 6: Uji t satu sampel Exercise 7: Membandingkan dua mean Exercise 8: Uji t dua sampel Exercise 9: Uji berpasangan Exercise 10: ANOVA Exercise 11: Membandingkan kelompok

Latihan Saat Ini

Exercise 12: ANOVA untuk pertumbuhan tanaman

Is your potential employer planning to test your R skills? Make sure you’re prepared and practice model evaluation beforehand. In this chapter, we will fit and evaluate linear and logistic regression models using various biomedical datasets. By the end of this chapter, you’ll be fully prepared to answer any question the interviewer throws your way!

Exercise 1: Covariance and correlation Exercise 2: Covariance by hand Exercise 3: Linear relationship Exercise 4: Nonlinear relationship Exercise 5: Linear regression model Exercise 6: Fitting linear models Exercise 7: Predicting with linear models Exercise 8: Logistic regression model Exercise 9: Fitting logistic models Exercise 10: Predicting with logistic models Exercise 11: Model evaluation Exercise 12: Validation set approach Exercise 13: Regression evaluation Exercise 14: Classification evaluation Exercise 15: Wrapping up