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Inference for Numerical Data in R

via Datacamp


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!


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.

Comparing many means
-In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.

Taught by

Mine Cetinkaya-Rundel

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