- Use the multivariate dataset named Auto MPG from the UCI Machine Learning repository (reference 3 in the References section of this chapter). The dataset can be downloaded from the website at https://archive.ics.uci.edu/ml/datasets/Auto+MPG. The dataset describes automobile fuel consumption in miles per gallon (mpg) for cars running in American cities. From the folder containing the datasets, download two files:
auto-mpg.dataandauto-mpg.names. Theauto-mpg.datafile contains the data and it is in space-separated format. Theauto-mpg.namesfile has several details about the dataset, including variable names for each column. Build a regression model for the fuel efficiency, as a function displacement (disp), horse power (hp), weight (wt), and acceleration (accel), using both OLS and Bayesian GLM. Predict the values for mpg in the test dataset using both the OLS model and Bayesian GLM model (using thebayesglmfunction). Find the Root Mean Square Error (RMSE) values for OLS and Bayesian GLM and compare the accuracy and prediction intervals for both the methods.
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