Inference for Numerical Data in R

Overview

Get Certified in Python, SQL & AI — 50% Off This May

690+ courses, career tracks, and industry-leading certifications trusted by 19M+ learners and 80% of the Fortune 1000.

In this course you'll learn techniques for performing statistical inference on numerical data.

In this course, you'll learn how to use statistical techniques to make inferences and estimations using numerical data. This course uses two approaches to these common tasks. The first makes use of bootstrapping and permutation to create resample based tests and confidence intervals. The second uses theoretical results and the t-distribution to achieve the same result. You'll learn how (and when) to perform a t-test, create a confidence interval, and do an ANOVA!

Syllabus

Bootstrapping for estimating a parameter

In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.

Introducing the t-distribution

In this chapter you'll use Central Limit Theorem based techniques to estimate a single parameter from a numerical distribution. You will do this using the t-distribution.

Inference for difference in two parameters

In this chapter you'll extend what you have learned so far to use both simulation and CLT based techniques for inference on the difference between two parameters from two independent numerical distributions.