Course
Inference for Numerical Data in R
AdvancedSkill Level
Updated 09/2020RProbability & Statistics4 hr15 videos49 Exercises3,650 XP14,172Statement of Accomplishment
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Prerequisites
Foundations of Inference in R1
Bootstrapping for estimating a parameter
In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.
2
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.
3
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.
4
Comparing many means
In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.
Inference for Numerical Data in R
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FAQs
What statistical methods does this course teach?
You learn bootstrapping, permutation tests, t-tests, confidence intervals, and ANOVA. Both resampling-based and theoretical t-distribution approaches are covered for each task.
Is this course suitable for intermediate R users?
This is an advanced course with extensive prerequisites including hypothesis testing, regression, sampling, and inference foundations in R. Solid intermediate R knowledge is expected.
Why does the course teach two different approaches to inference?
It compares bootstrapping and permutation methods with Central Limit Theorem based methods using the t-distribution, so you understand when each approach is appropriate.
Does this course cover comparing more than two groups?
Yes. Chapter 4 teaches ANOVA (analysis of variance) for testing whether there is a difference in means across multiple groups simultaneously.
How many exercises are included in this course?
The course contains 64 exercises across 4 chapters. Most learners finish in about 2.3 hours, though the estimated time is 240 minutes.
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