Interactive Course

Inference for Numerical Data in R

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

4 hours
15 Videos
49 Exercises
4,367 Participants
3,650 XP

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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!

In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical 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'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 will use ANOVA (analysis of variance) to test for a difference in means across many groups.

1
Bootstrapping for estimating a parameter
Free

In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.
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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.
View Chapter Details Play Chapter Now
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.
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Comparing many means

In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.
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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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Inference for Numerical Data in R

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Course Description

Bootstrapping for estimating a parameter

Welcome to the course!

Generate bootstrap distribution for median

Review percentile and standard error methods

Calculate bootstrap interval using both methods

Which method more appropriate: percentile or SE?

Doctor visits during pregnancy

Average number of doctor's visits

SD of number of doctor's visits

Re-centering a bootstrap distribution

Test for median price of 1 BR apartments in Manhattan

Conclude the hypothesis test on median

Test for average weight of babies

Inference for difference in two parameters

Hypothesis testing for comparing two means

Evaluating the effectiveness of stem cell treatment

Evaluating the effectiveness of stem cell treatment (cont.)

Conclusion of the hypothesis test

Evaluating the relationship between smoking during pregnancy and birth weight

Bootstrap CI for difference in two means

Quantifying the relationship between smoking during pregnancy and birth weight

Median lengths of pregnancies for smoking and non-smoking mothers

Comparing means with a t-test

Hourly pay vs. citizenship status

Estimating the difference of two means using a t-interval

Introducing the t-distribution

t-distribution

When to t?

Probabilities under the t-distribution

Cutoffs under the t-distribution

Estimating a mean with a t-interval

Average commute time of Americans

Average number of hours worked

t-interval for paired data

t-interval at various levels

Understanding confidence intervals

Testing a mean with a t-test

Estimate the median difference in textbook prices

Test for a difference in median test scores

Interpret the p-value

Comparing many means

Vocabulary score vary between vs. (self identified) social class

EDA for vocabulary score vs. social class

Comparing many means, visually

ANOVA

ANOVA for vocabulary score vs. (self identified) social class

Conditions for ANOVA

Checking the normality condition

Checking the constant variance condition

Post-hoc testing

Calculate alpha*

Compare pairwise means

Congratulations!