Paired t-Test in R with Examples | R Tutorial 4.7 | MarinStatsLectures

Paired t-Test in R with Examples: Learn how to conduct the paired t-test (matched pairs t-test) and calculate confidence interval in R for means of two paired or dependent groups; 📝 Find R practice dataset (BloodPressure) here: (https://statslectures.com/r-scripts-datasets) 👉🏼Related: Paired t Test in Statistics Video: https://youtu.be/Q0V7WpzICI8 ; Wilcoxon Signed Rank Test in Statistics Video: https://youtu.be/v4ZHlTbTOK8 👍🏼Best Statistics & R Programming Language Tutorials: ( https://goo.gl/4vDQzT ) ►► Want to support us? You can Donate (https://bit.ly/2CWxnP2), Share Our Videos, Leave Comments or Give a us Like! In this R video tutorial, we will learn how to conduct the paired t-test (matched pairs t-test or dependent t test) in R. This test is used to compare the means of two paired or dependent populations. It essentially becomes the univariate (one sample) t test, by taking the difference in observations in the 2 groups, and then conducting a test on the mean difference. The paired t-test and confidence interval in statistics are parametric methods appropriate for examining the difference in means for 2 populations that are paired or dependent on one another. We will learn to use various functions and arguments in R statistical software such as "t.test”, "paired", "conf.level", "boxplot", “mu”, and more to perform this test. These video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio. Table of Content: 0:00:09 When should we use the paired t-test and confidence interval in statistics and in research? 0:00:55 how to access the help menu in R for paired t-test 0:01:05 how to use boxplots in R to visualize and interpret the difference in means for two populations that are paired or dependent on one another 0:01:21 how to use scatterplots in R programming language to visualize the data as paired or the changes in individuals 0:01:39 how to add a line for X=Y (eg. before= after) in a paired data plot using the "abline" function in R 0:01:58 how to interpret the scatterplot of paired or dependent data 0:02:32 how to do the paired t-test in R using "t.test" command/ function 0:02:40 how to ask R to test if the mean difference is 0 in a paired t-test using the "mu" argument 0:02:47 how to conduct a two-sided t-test in R using the "alt" argument 0:02:54 how to ask R if the data is paired when conducting t-test using "paired" argument 0:03:02 how to specify the level of confidence interval in R statistical software for the paired t-test using the "conf.level" argument 0:03:35 how does the order that X and Y are entered in "t.test" command/function in R for a paired-t-test changes the results ► ► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►Data Science with R https://bit.ly/1A1Pixc ►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 ►ANOVA Concept and with R https://bit.ly/2zBwjgL ►Hypothesis Testing: https://bit.ly/2Ff3J9e ►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1 Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook: https://goo.gl/qYQavS Twitter: https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support some Statistics and R Programming courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn! #rprogramming #statistics