Menghitung kovarians secara manual

Di R, Anda dapat dengan cepat menghitung berbagai ukuran statistik menggunakan fungsi. Pewawancara mungkin tertarik melihat apakah Anda dapat mereplikasi perhitungan fungsi-fungsi tersebut untuk memastikan pemahaman Anda tentang apa yang dikerjakannya.

Pada latihan ini, Anda akan menghitung kovarians secara manual. Kovarians mengukur variabilitas dua peubah acak.

Ingat kembali bahwa rumus kovarians untuk sampel adalah:

\( \sum_{i=1}^{n} \frac{(x_i - \overline{x})(y_i - \overline{y})}{n-1}\)

Data frame df tersedia di lingkungan Anda dengan variabel x dan y.

Latihan ini adalah bagian dari kursus

Latihan Pertanyaan Wawancara Statistik di R

Petunjuk latihan

Tetapkan jumlah observasi dalam df ke n.
Hitung kovarians antara x dan y secara manual.
Hitung kovarians antara x dan y menggunakan fungsi dari paket stats.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# The number of observations
___ <- nrow(___)

# Compute covariance by hand
___((df$x-___(df$x))*(___-mean(df$y)))/(___-1)

# Compute covariance with function
___(df$x, ___)

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: 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: 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: Kovarians dan korelasi Exercise 2: Menghitung kovarians secara manual

Latihan Saat Ini

Exercise 3: Hubungan linear Exercise 4: Hubungan nonlinier Exercise 5: Model regresi linear Exercise 6: Mengestimasi model linear Exercise 7: Memprediksi dengan model linear Exercise 8: Model regresi logistik Exercise 9: Mencocokkan model logistik Exercise 10: Memprediksi dengan model logistik Exercise 11: Evaluasi model Exercise 12: Pendekatan validation set Exercise 13: Evaluasi regresi Exercise 14: Evaluasi klasifikasi Exercise 15: Penutup