After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
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!
Prerequisites
Curriculum
Course outline
1
Bootstrapping for estimating a parameter
In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.
- Welcome to the course!50 XP
- Generate bootstrap distribution for median100 XP
- Review percentile and standard error methods50 XP
- Calculate bootstrap interval using both methods100 XP
- Which method more appropriate: percentile or SE?50 XP
- Doctor visits during pregnancy50 XP
- Average number of doctor's visits100 XP
- SD of number of doctor's visits100 XP
- Re-centering a bootstrap distribution50 XP
- Test for median price of 1 BR apartments in Manhattan100 XP
- Conclude the hypothesis test on median50 XP
- Test for average weight of babies100 XP
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.
R
Inference for Numerical Data in R
Course
Complete
