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Inference for Numerical Data in R

In this course you'll learn techniques for performing statistical inference on numerical data.

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4 Hours15 Videos49 Exercises9,756 Learners3650 XPStatistical Inference TrackStatistician Track

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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!

  1. 1

    Bootstrapping for estimating a parameter


    In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.

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    Welcome to the course!
    50 xp
    Generate bootstrap distribution for median
    100 xp
    Review percentile and standard error methods
    50 xp
    Calculate bootstrap interval using both methods
    100 xp
    Which method more appropriate: percentile or SE?
    50 xp
    Doctor visits during pregnancy
    50 xp
    Average number of doctor's visits
    100 xp
    SD of number of doctor's visits
    100 xp
    Re-centering a bootstrap distribution
    50 xp
    Test for median price of 1 BR apartments in Manhattan
    100 xp
    Conclude the hypothesis test on median
    50 xp
    Test for average weight of babies
    100 xp

In the following tracks

Statistical InferenceStatistician


n10iNick CarchedinicksolomonNick Solomon
Mine Cetinkaya-Rundel Headshot

Mine Cetinkaya-Rundel

Associate Professor at Duke University & Data Scientist and Professional Educator at RStudio

Mine is the Director of Undergraduate Studies and an Associate Professor of the Practice in the Department of Statistical Science at Duke University as well as a Professional Educator at RStudio. Her work focuses on innovation in statistics pedagogy, with an emphasis on computation, reproducible research, open-source education, and student-centered learning. She is the author of three open-source introductory statistics textbooks as part of the OpenIntro project and teaches the popular Statistics with R MOOC on Coursera.
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