Course
Inference for Numerical Data in R
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Prerequisites
Foundations of Inference in RBootstrapping for estimating a parameter
Introducing the t-distribution
Inference for difference in two parameters
Comparing many means
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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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