Bootstrap Hypothesis Testing in R with Examples: Learn how to conduct a hypothesis test by building a bootstrap approach (Re-sampling) with R statistical software without a package, step by step. 👉🏼Related: Bootstrap Hypothesis Testing in Statistics Video: https://bit.ly/2USN1Se 📝 Find R practice dataset (chickdata) and R Script here: (https://statslectures.com/r-scripts-datasets)
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►In this R video tutorial, we will learn how to use R to perform a hypothesis test using a bootstrap approach.
► Bootstrapping in statistics is a resampling based approach useful for estimating the sampling distribution and standard error of an estimate.
► Bootstrapping in statistics and in research provides an alternative approach to approaches based on large sample theory (you may recall that many approaches rely on having a large n in order to carry out the method). It becomes particularly useful when dealing with more complicated estimates, where their sampling distribution and/or standard error may not be easily calculated
► We will focus on comparing the means (and medians) of two different groups, although we present the approach in a more general way, so that you can test a hypothesis about any other estimate/statistic calculated from your data.
► An R package does exist for bootstrap hypothesis testing (package name: boot), although the package is limited in the sorts of estimates/statistics it can conduct a bootstrap approach for. Our goal is to show you how to build the bootstrap approach yourself, so that you can make changes to the sorts of statistics/estimates you conduct tests for. You can practice building the test yourself, and then compare the results to what you get using the "boot" package in R. Note that if you do this, numeric values will differ slightly because you and the package will end up with a different set of bootstrap samples, and so there will be a slight numeric difference in results.
■Table of Content:
0:00:33 import the data into R
0:00:39 exploring the dataset used for performing a bootstrap hypothesis testing in R
0:01:12 How to visually compare the two groups in our dataset in R? Creating side by side box plots in R
0:01:30 Introducing the first test statistic for Bootstrap in R: the absolute value of the difference in the mean weight for the two diets (a two-sided two-sample t-tests)
0:01:43 Introducing the second test statistic for Bootstrap in R: absolute value of the difference in median weights for the two diets
0:02:09 Steps to calculating the two test statistics: 1) calculate the mean for each of the two different feed types with R programming language
0:02:40 calculate the test statistic 1 using the' with' R command (function) as well as a 'tapply', in R
0:03:07 calculate test statistic 2, using the with function and tapply function in R
Bootstrapping in R Step by Step:
0:04:27 setting a seed in R
0:04:32 why should you set a seed for bootstrapping in R
0:05:04 setting the number of observations, the number of bootstrap resamples and the variable in R
0:06:07 How to ask R programming language to resample with replacement from our variable
0:06:46 checking the bootstrap matrix produced in R
0:07:03 calculating the test statistic 1 and test statistic 2 for each of the n bootstrap resamples using a loop in R statistical software
0:09:31 reminder of the definition or the calculation of the p-value a
0:09:51 interpreting the generated test statistics for our bootstrap hypothesis testing
0:10:43 how to us R programming language to check the generated test statistic
► ► Watch More:
►Bootstrapping Statistics & Bootstrapping in R https://bit.ly/2GL6AYS
► Intro to Statistics Course: https://bit.ly/2SQOxDH
►Getting Started with R (Series 1): https://bit.ly/2PkTneg
►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg
►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI
►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi
►Linear Regression in R (Series 5): https://bit.ly/1iytAtm
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Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
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Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!