R Tutorial: Multiple Linear Regression

Want to learn more? Take the full course at https://learn.datacamp.com/courses/machine-learning-for-marketing-analytics-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this video, you'll learn about how to use multiple linear regression. One threat to the accuracy of the simple linear regression from before is what's called "omitted variable bias". This occurs when a variable not included in the regression is correlated with both the explanatory variable and the response variable. Imagine we are looking at the relationship between the study time before an exam and the success achieved. If we just consider these two variables, we find a negative relationship: the more a person studies, the lower her exam score will be. Strange, isn't it? Since IQ is positively related to exam success and negatively related to study time we need to include this variable in the regression. Then, with the help of multiple regression, I now estimate the positive effect of study time. Let's estimate a multiple regression model using the lm function, including all the variables in the dataset. futureMargin is now modeled as a function of margin, nOrders, nItems, and so on; we save the model as multipleLM. Just as before, we use summary, now with multipleLM as an argument. That worked; although, we now encounter other problems. Multicollinearity is one threat to a multiple linear regression. This occurs whenever one explanatory variable can be explained by the remaining explanatory variables. Then, the regression coefficients become unstable and the standard errors reported by the linear model are underestimates. Due to high correlation between nOrders and nItems as well as marginPerOrder and marginPerItem, these variables are candidates for multicollinearity. To systematically check all variables in a model for multicollinearity, we calculate the variance inflation factors (VIFs) using the vif function from