Statistics and Statistics with R Tutorials (All Videos) | MarinStatsLectures - Change Reference (Baseline) Category in Regression with R | R Tutorial 5.6 | MarinStatsLectures

Change Reference or Baseline Category for a Categorical Variable in Regression with R: Learn how to use the relevel command in R to change the reference/baseline category for a factor or categorical/qualitative variable in a linear regression model (reparameterize). Practice Dataset (LungCapData): https://statslectures.com; 👍🏼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 or Write us a Review! Either way We Thank You! ►In a linear regression model the intercept or constant term refers to the estimated mean Y-value for the reference or baseline group, and the model coefficients or parameters refer to expected changes in the mean Y-value relative to the reference group. For example, if X is a categorical variable (or a factor) with levels A, B, C, then the intercept will refer to level A, and there will be a coefficient for level B (b_B) which estimates the change in the mean for level B relative to level A, and there will be a coefficient for level C (b_C) which estimates the change in the mean for level C relative to level A. By default, R chooses the category that comes first alphabetically or numerically (alphanumerically) as the reference category. In this video, we show how to use the relevel command (function) to change the reference category in R statistical software for a categorical variable (a factor). In this example, we may wish to make level B (or level C) the reference category. ■Table of Content: 0:00:13 what is the interpretation of the intercept or constant term? 0:00:21 what is the interpretation of the model coefficients or parameters? 0:00:46 using the relevel command to change the reference or baseline category of a categorical variable 0:00:51 how to access the help menu in R 0:01:01 how to fit a linear regression model in R relating one outcome variable to two explanatory variables 0:01:21 how to interpret the fitted regression model output and model coefficients or parameters in R 0:01:26 how to interpret the regression model intercept 0:01:39 how to interpret the model coefficient for a numeric variable in R 0:01:56.5 how to interpret the model coefficient for a categorical variable in R 0:02:25 how to change the reference or baseline group in R 0:02:31 how does R choose the reference or baseline category? 0:02:50 how to use the "relevel" command to change the reference or baseline category in R 0:03:15 fit a model where we have changed the reference category The video provides a tutorial for programming in R Statistical Software for beginners using RStudio. ►► 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 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!