[size=10.000000pt]This book began as the notes for 36-402, Advanced Data Analysis, at Carnegie Mel-lon University. This is the methodological capstone of the core statistics sequencetaken by our undergraduate majors (usually in their third year), and by undergradu-ate students from a range of other departments. By this point, students have takenclasses in introductory statistics and data analysis, probability theory, mathematicalstatistics, and modern linear regression (“401”). This book does not presume that youonce learned but have forgotten the material from the pre-requisites; it presumes thatyou [size=10.000000pt]know [size=10.000000pt]that material and can go beyond it. The book also presumes a firm graspon linear algebra and multivariable calculus, and that you can read and write simplefunctions in R. If you are lacking in any of these areas, now would be an excellenttime to leave.
                                        [size=10.000000pt]ADA is a class in [size=10.000000pt]statistical methodology[size=10.000000pt]: its aim is to get students to understandsomething of the range of modern[size=7.000000pt]1 [size=10.000000pt]methods of data analysis, and of the consider-ations which go into choosing the right method for the job at hand (rather thandistorting the problem to fit the methods you happen to know). Statistical theory iskept to a minimum, and largely introduced as needed. Since ADA is also a class in[size=10.000000pt]data analysis[size=10.000000pt], there are a lot of assignments in which large, real data sets are analyzedwith the new methods.