# Practicing Statistics Interview Questions in R

In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

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## Course Description

Are you job interview ready? You may know everything there is to know about your target company, but have you practiced the classic R statistical interview questions? If not, we have you covered. In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more. You’ll sharpen your skills using datasets including Parkinson’s disease data and gas prices. This course is purposely more challenging than a typical DataCamp course to make sure that when it comes to interviewing time you’re ready to confidently tackle any statistics interview question in R.

- 1
### Probability Distributions

**Free**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.

Discrete distributions50 xpProbability functions100 xpBernoulli trials100 xpBinomial distribution100 xpContinuous distributions50 xpUniform distribution100 xpShape of normal distribution100 xpSample from normal distribution100 xpCentral limit theorem50 xpLaw of large numbers100 xpSimulating central limit theorem100 xp - 2
### Exploratory Data Analysis

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.

- 3
### Statistical Tests

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.

- 4
### Regression Models

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!

Covariance and correlation50 xpCovariance by hand100 xpLinear relationship100 xpNonlinear relationship100 xpLinear regression model50 xpFitting linear models100 xpPredicting with linear models100 xpLogistic regression model50 xpFitting logistic models100 xpPredicting with logistic models100 xpModel evaluation50 xpValidation set approach100 xpRegression evaluation100 xpClassification evaluation100 xpWrapping up50 xp

Collaborators

#### Zuzanna Chmielewska

Actuary

Zuzanna is a life insurance actuary and works as an actuarial consultant. In her work, she develops mathematical models for life insurance products in R. Zuzanna obtained her Master's degree in Quantitative Methods in Economics and Information Systems at the Warsaw School of Economics (Poland).

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