Stata: Data Analysis and Statistical Software
Skip to main content Multivariate Analysis. Methods of Multivariate Analysis. Only 7 left in stock more on the way. I had this book as a textbook for a graduate level multivariate analysis course for environmental science. It's a comprehensive introduction that is clear and fairly concise. I find myself using it as reference all the time.
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Multivariate analysis is what people called many machine learning techniques before calling it machine learning became so lucrative. Traditional multivariate analysis emphasizes theory concerning the multivariate normal distribution, techniques based on the multivariate normal distribution, and techniques that don't require a distributional assumption, but had better work well for the multivariate normal distribution, such as: multivariate regression, classification, principal component analysis, ANOVA, ANCOVA, correspondence analysis, density estimation, etc. This book tries to cover a lot of ground. The subtitle Regression, Classification, and Manifold Learning spells out the foci of the book hypothesis testing is rather neglected. Izenman covers the classical techniques for these three tasks, such as multivariate regression, discriminant analysis, and principal component analysis, as well as many modern techniques, such as artificial neural networks, gradient boosting, and self-organizing maps.
is there a good, accessible book you might recommend she/he purchase? . Hands down best basic text on multivariate regression is (still).
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Practical Multivariate Analysis, Fifth Edition
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Statistics came well before computers. It would be very different if it were the other way around. The stats most people learn in high school or college come from the time when computations were done with pen and paper. There are better options. Tibsharani is a coauthor of both.