What Happens When You Omit Important Variables From Your Regression Model

We'll study the consequences of failing to include important variables in a linear regression model. Our goal will be to formulate a well-known result in statistical modeling called Omitted Variable Bias and to illustrate the calculation using the sample data set. The following data contains specifications of 205 automobiles taken from the 1985 edition of Ward's Automotive Yearbook. Each row contains a set of 26 specifications about a single vehicle. We'll consider a subset of this data consisting of the following variables: City_MPG Car_Volume Curb_Weight Engine_Size The Car_Volume variable is not present in the original data set.