Statistics and Statistics with R Tutorials (All Videos) | MarinStatsLectures - One-Sample t Test & Confidence Interval in R with Example | R Tutorial 4.1| MarinStatsLectures

One-Sample t Test and Confidence Interval in R with Example: Learn how to conduct the one-sample t-test and calculate the confidence interval in R (using RStudio).📝Correction (0:01:10): Ho(Null Hypothesis): mu=8 (or more specifically, mu equal or greater than 8) and Ha (Alternative Hypothesis): mu less than 8 (Thanks to @ShairozSohail for catching this). Find the R practice dataset (LungCapData) here: (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 lecture, we will learn how to conduct the one-sample t-test and confidence interval for the mean of a single variable. Here we will use various R functions and arguments such as "t.test", "boxplot", "attributes", "$", “conf”, “alt” and more. One sample t test in statistics is also know as single sample t test. The one sample t test and confidence interval are parametric methods appropriate for examining a single numeric variable. ■Table of Content: 0:00:11 when do we use one sample t-test and confidence interval? 0:00:35 how to conduct the one-sample t-test and the confidence interval in R 0:00:41 how to access the Help menu in R for the one sample t-test 0:01:05 how to test a null and one-sided alternative hypothesis for the mean with a one-sided confidence interval in R using 0:02:40 how to produce a two-sided hypothesis test and confidence interval in R 0:03:16 how to create a 99 percent confidence interval in R using the "conf" argument 0:03:46 how to see different attributes of an object in R using 0:03:59 how to extract specific attributes of an object in R ► ► 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 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 #rprogramming