Welcome to Introduction to R for Data Science Session 7: Multiple Regression + Dummy Coding, Partial and Part Correlations [Multiple Linear Regression in R. Dummy coding: various ways to do it in R. Factors. Inspecting the multiple regression model: regression coefficients and their interpretation, confidence intervals, predictions. Introducing {lattice} plots + ggplot2. Assumptions: multicolinearity and testing it from the {car} package. Predictive models with categorical and continuous predictors. Influence plot. Partial and part (semi-partial) correlation in R.] The course is co-organized by Data Science Serbia and Startit. You will find all course material (R scripts, data sets, SlideShare presentations, readings) on these pages. Check out the Course Overview to acess the learning material presented thus far. Data Science Serbia Course Pages [in Serbian] Startit Course Pages [in Serbian] Lecturers dipl. ing Branko Kovač, Data Analyst at CUBE, Data Science Mentor at Springboard, Data Science Serbia Goran S. Milovanović, Phd, DataScientist@DiploFoundation, Data Science Serbia Summary of Session 7, 09. June 2016 :: Multiple Regression + Dummy Coding, Partial and Part Correlations. Multiple Regression + Dummy Coding, Partial and Part Correlations. Multiple Linear Regression in R. Dummy coding: various ways to do it in R. Factors. Inspecting the multiple regression model: regression coefficients and their interpretation, confidence intervals, predictions. Introducing {lattice} plots + ggplot2. Assumptions: multicolinearity and testing it from the {car} package. Predictive models with categorical and continuous predictors. Influence plot. Partial and part (semi-partial) correlation in R. Session 7 SlideShare Session 7 R Script Further Readings Intro to R for Data Science SlideShare :: Session 7 Introduction to R for Data Science :: Session 7 [Multiple Linear Regression in R] from Goran S. Milovanovic R script :: Session 7 ######################################################## # Introduction to R for Data Science # SESSION 7 :: 9 June, 2016 # Multiple Linear Regression in R # Data Science Community Serbia + Startit # :: Goran S. Milovanović and Branko Kovač :: ######################################################## # clear rm(list=ls()) #### read data library(datasets) library(broom) library(ggplot2) library(lattice) library(QuantPsyc) #### load data(iris) str(iris) #### simple linear regression: Sepal Length vs Petal Lenth # Predictor vs Criterion {ggplot2} ggplot(data = iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point(size = 2, colour = "black") + geom_point(size = 1, colour = "white") + geom_smooth(aes(colour = "black"), method='lm') + ggtitle("Sepal Length vs Petal Length") + xlab("Sepal Length") + ylab("Petal Length") + theme(legend.position = "none")Created by Pretty R at inside-R.org # What is wrong here? # let's see... reg