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!
In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical 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.
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.
In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.
DatasetsChp1-vid1-boot-dist-noaxes-paranthesesChp1-vid1-bootsamp-bootpop.001Chp1-vid1-manhattan-rentsChp1-vid2-boot-dist-withaxesChp1-vid2-perc-method.001Chp1-vid2-perc-method.002Chp1-vid3-boot-test.001Chp3-vid3-hrly-rate-citizen-smallerChp3-vid3-hrly-rate-citizenChp4-vid1-class-barChp4-vid1-wodrsum-histGss moredaysGSS dataManhattan rent dataRunners.001Tdistcomparetonormaldist
PrerequisitesFoundations of Inference
Associate Professor at Duke University & Data Scientist and Professional Educator at RStudio
“I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyds Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Decision Science Analytics, USAA