Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Ed

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Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest. An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date. Fully revised and expanded Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from astronomical surveys Uses a freely available Python codebase throughout Ideal for graduate students, advanced undergraduates, and working astronomers

Business Statistics and Analytics in Practice (9版)

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書名：Business Statistics and Analytics in Practice 9/E 作者：Bowerman 出版社：McGraw-Hill 出版日期：2019/00/00 ISBN：9781260287844 內容簡介 1. A modern, practical and unique framework for teaching an introductory course in Business Statistics. 2.  Employs realistic examples, continuing case studies and a business improvement theme to teach the material. 3. Features more concise and lucid explanations, an improved topic flow and a sensible use of the best and most compelling examples. 目錄 Ch 1 An Introduction to Business Statistics and Analytics Ch 2 Descriptive Statistics and Analytics: Tabular and Graphical Methods Ch 3 Descriptive Statistics and Analytics: Numerical Methods Ch 4 Probability and Probability Models Ch 5 Predictive Analytics I: Trees, k-Nearest Neighbors, Naive Bayes’, and Ensemble Estimates Ch 6 Discrete Random Variables Ch 7 Continuous Random Variables Ch 8 Sampling Distributions Ch 9 Confidence Intervals Ch10 Hypothesis Testing Ch11 Statistical Inferences Based on Two Samples Ch12 Experimental Design and Analysis of Variance Ch13 Chi-Square Tests Ch14 Simple Linear Regression Analysis Ch15 Multiple Regression and Model Building Ch16 Predictive Analytics II: Logis¬tic Regression, Discriminate Analysis, and Neural Networks Ch17 Time Series Forecasting and Index Numbers Ch18 Nonparametric Methods Ch19 Decision Theory Ch20 (Online) Process Improvement Using Control Charts for Website Appendix A Statistical Tables Appendix B (Online) Chapter by Chapter MegaStat