Statistics and Statistics with R Tutorials (All Videos) | MarinStatsLectures - Two-Sample t Test in R (Independent Groups) with Example | R Tutorial 4.2 | MarinStatsLectures

Two-Sample t Test in R (Independent Groups) with Example: Learn how to conduct the independent two-sample t-test and calculate confidence interval with R Statistical Software; 👉🏼Related: Two Sample t Test in Statistics Video https://youtu.be/mBiVCrW2vSU 📝 R practice dataset (LungCapData): https://statslectures.com/r-scripts-datasets 👍🏼Best Statistics & R Programming Language Tutorials: ( https://goo.gl/4vDQzT ) ►► Like to support us? You can Donate (https://bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Like ! Either way We Thank You! In this R video tutorial, we will learn how to conduct the independent two-sample t-test and confidence interval for the difference in means of two populations. We will use various R functions and arguments to perform two sample t-test such as "t.test" function, "boxplot", "var.eq" and "leveneTest" and more. Independent two sample t-test and confidence interval are parametric methods appropriate for examining the difference in means for two populations. These are ways of examining the relationship between a numeric outcome variable (Y variable) and a categorical explanatory variable (X variable with 2 levels) Table of Content: 0:00:10 when should we use the independent two sample t-test and confidence interval in statistics and in research 0:00:53 how to access the help menu in R for t-test 0:01:04 how to visually examine the relationship between two variables in R 0:01:18 how to conduct an independent two-sided t-test with non-equal population variances in R using the "t.test" function 0:01:56 introducing the null and alternative hypothesis, the confidence interval, and variance assumption with example 0:03:06 how to use the "mu" argument in two-sided t-test 0:03:12 how to use the "alt" argument to do a one-sided t-test 0:03:18 how to use the "conf" argument to change the confidence level for the t-test 0:03:24 how to use the "var.eq" argument to assume equal population variances for t-test 0:03:29 how to let R know that groups are paired or dependent using the "paired" argument 0:03:37 two different ways for separating the groups in "t.test" command/function in R 0:04:18 how to decide if we should assume equal or non-equal variances using boxplot 0:04:38 how to decide if we should assume equal or non equal variances comparing the actual variances 0:05:02 how to test the null hypothesis "that the population variances are equal" using Levene's test using "leveneTest" function ► ► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►R Programming for Beginners Course 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 ), 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! #statistics #r_programming