PCA - rotasi

Principal Component Analysis memungkinkan Anda mengurangi jumlah dimensi dalam sebuah himpunan data, sehingga mempercepat waktu perhitungan tanpa kehilangan nilai informasi yang signifikan.

Anda mungkin akan mendapat pertanyaan tentang PCA saat wawancara jika peran Anda nantinya melibatkan penanganan data dalam jumlah besar.

Ingat bahwa pada PCA variabel diubah menjadi komponen utama. Komponen utama pertama memiliki ragam (variance) terbesar yang mungkin.

Anda akan melakukan PCA menggunakan himpunan data cats yang sudah Anda jumpai pada latihan sebelumnya.

Dalam latihan ini, gunakan prcomp() untuk melakukan analisis komponen utama. Objek yang dikembalikan dapat digunakan untuk predict variabel yang telah dirotasi.

Latihan ini adalah bagian dari kursus

Latihan Pertanyaan Wawancara Statistik di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot the unrotated data
___(___ ~ Hwt, data = ___)

# Perform PCA
pca_cats <- ___(~ ___ + Hwt, data = ___)

# Compute the summary
___(___)

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

Want to increase your odds of acing your job interview? If so, brush up on your knowledge of probability theory. In this chapter, we'll roll dice and shoot baskets to explain probabilities using real-life examples.

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: Statistika deskriptif Exercise 2: Ukuran kepekaan pusat Exercise 3: Ukuran variabilitas Exercise 4: Data kategorikal Exercise 5: Analisis survei Exercise 6: Pengodean data Exercise 7: Deret waktu Exercise 8: Objek deret waktu Exercise 9: Membersihkan time series Exercise 10: Analisis Komponen Utama Exercise 11: PCA - rotasi

Latihan Saat Ini

Exercise 12: PCA - reduksi dimensi

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: Normality tests Exercise 2: Shapiro-Wilk test Exercise 3: Q-Q plot Exercise 4: Inference for a mean Exercise 5: Confidence interval Exercise 6: One-sample t-test Exercise 7: Comparing two means Exercise 8: Two-sample t-test Exercise 9: Paired test Exercise 10: ANOVA Exercise 11: Comparing groups Exercise 12: ANOVA for plant growth

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