PCA - rotation

Principal Component Analysis allows you to reduce the number of dimensions in a dataset, which speeds up calculation time without significant loss of informational value.

You may expect questions on PCA during the interview if your future role involves handling vast amounts of data.

Recall that in PCA the variables are transformed into principal components. The first principal component has the largest possible variance.

You will perform PCA using the cats dataset that you have already encountered in the previous exercises.

In this exercise, use prcomp() to perform the principal component analysis. The returned object can be used to predict the rotated variables.

Cet exercice fait partie du cours

Practicing Statistics Interview Questions in R

Afficher le cours

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

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

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

# Compute the summary
___(___)

Modifier et exécuter le code

Cet exercice fait partie du cours

Practicing Statistics Interview Questions in R

AvancéNiveau de compétence

4.8+

Commencer le cours gratuitement

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

Exercice en cours

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: 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