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