Multiple Linear Regression III | Statistics for Applied Epidemiology | Tutorial 4

Multiple Linear Regression III: Different approaches to fitting a regression model, understand interaction and effect modifications and more. Find 'HERS' Dataset: (https://bit.ly/2rOfgEJ); Linear Regression Concept and with R (https://bit.ly/2z8fXg1); More Statistics and R Programming Tutorials: (https://bit.ly/2Fhu9XU) In this statistics tutorial we will ► Learn when to use a linear regression model ► Understand the different approaches/goals of fitting a regression model ► Interpret the intercept and the estimates for continuous and categorical x-variables in a simple/multiple linear regression model ● Learn to calculate a predicted outcome for a a given set of x-variables based on a model ● Interpret estimates with an interaction term in the model ► Understand what confounders and effect modifier are ► Decide when to keep or drop variables from the model ● Identify a confounder, effect modifier, another significant predictor, collinear variable ► Run a test to determine which model fits best (nested or non-nested) ► Check the assumptions of the linear regression model ● Identify steps that can be taken if assumptions are violated ►► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►R Tutorials for Data Science 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 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 This statistics tutorial is prepared to support SPPH 500: Analytic Methods in Applied Epidemiology course offered in the School of Population and Public Health at the University of British Columbia (UBC). These videos are created as part of #marinstatslectures video tutorial series to support some courses at 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!