•  #### Learning Bayesian Models with R #### Overview of this book

Learning Bayesian Models with R Credits   www.PacktPub.com Preface  Free Chapter
Introducing the Probability Theory The R Environment Introducing Bayesian Inference Machine Learning Using Bayesian Inference Bayesian Regression Models Bayesian Classification Models Bayesian Models for Unsupervised Learning Bayesian Neural Networks Bayesian Modeling at Big Data Scale Index ## Exercises

For the following exercises in this chapter, we use the Auto MPG dataset from the UCI Machine Learning repository (references 5 and 6 in the References section of this chapter). The dataset can be downloaded from https://archive.ics.uci.edu/ml/datasets.html. The dataset contains the fuel consumption of cars in the US measured during 1970-1982. Along with consumption values, there are attribute variables, such as the number of cylinders, displacement, horse power, weight, acceleration, year, origin, and the name of the car:

• Load the dataset into R using the `read.table()` function.

• Produce a box plot of mpg values for each car name.

• Write a function that will compute the scaled value (subtract the mean and divide by standard deviation) of a column whose name is given as an argument of the function.

• Use the `lapply()` function to compute scaled values for all variables.

• Produce a scatter plot of mgp versus acceleration for each car name using `coplot()`. Use legends to annotate the graph.