Centrality measures

The primary purpose of descriptive statistics is to provide a summary of the data. Descriptive statistics might prove useful if you need to quickly get a grasp of the data at hand during the interview.

The most recognized types of descriptive statistics are measures of central tendency, also known as centrality measures.

In this exercise, you will work with the cats dataset from the MASS package. The cats dataset contains the following variables:

Sex - sex,
Bwt - body weight, and
Hwt - heart weight

of domestic cats.

By calculating central tendency measures, you'll be able to answer questions about the skewness of the data.

Recall the relationship between the mean and the median in skewed distributions:

The dataset has been preloaded as cats.

Diese Übung ist Teil des Kurses

Practicing Statistics Interview Questions in R

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Compute the average of Hwt
___(cats$Hwt)

# Compute the median of Hwt
___(___)

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

Practicing Statistics Interview Questions in R

Hohe SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

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

Aktuelle Übung

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: 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