Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. ALAN AGRESTI is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He has presented short courses on categorical data methods in thirty countries.

Mathematical Statistics: Basic Ideas and Selected Topics, Volume I, Second Edition presents fundamental, classical statistical concepts at the doctorate level. It covers estimation, prediction, testing, confidence sets, Bayesian analysis, and the general approach of decision theory. This edition gives careful proofs of major results and explains how the theory sheds light on the properties of practical methods. The book first discusses non- and semiparametric models before covering parameters and parametric models. It then offers a detailed treatment of maximum likelihood estimates (MLEs) and examines the theory of testing and confidence regions, including optimality theory for estimation and elementary robustness considerations. It next presents basic asymptotic approximations with one-dimensional parameter models as examples. The book also describes inference in multivariate (multiparameter) models, exploring asymptotic normality and optimality of MLEs, Wald and Rao statistics, generalized linear models, and more.

Modelling Survival Data in Medical Research

RISE University出的关于统计学的巨著! 具体目录为: 1.Sampling and Data 2.Descriptive Statistics 3.Probability Topics 4.Discrete Random Variables 5.Continuous Random Variables 6.The Normal Distribution 7.The Central Limit Theorem 8.Confidence Intervals 9.Hypothesis Testing with One Sample ......

IBM SPSS Statistics V21.0产品授权激活码

Probability, Statistics, and Random Processes For Electrical Engineering, 3ed，概率，统计和随机过程在电子工程中的应用

《爱上统计学》英文原版！ 【内容简介】 在经过不断地摸索以及少量成功大量失败的尝试之后，我已经学会了以某种方式教授统计学，我和我的许多学生认为这种方式不会让人感到害怕，同时能够传递大量的信息。 为什么《爱上统计学》这本书不增加更多理论内容?很简单，初学者不需要。这并不是我认为理论不重要，而是在学习的这个阶段，我想提供的是我认为通过一定程度的努力可以理解和掌握的资料，同时又不会让你感到害怕而放弃将来选修更多的课程。我和其他老师都希望你能成功。 因此，如果你想详细了解方差分析中F值的含义，可以从Sage出版社查找其他的好书(我愿意向你推荐书目)。但是如果你想了解统计学为什么以及如何为你所用，这本书很合适。这本书能帮助你理解在专业文章中看到的资料，解释许多统计分析结果的意义，并且能教你运用基本的统计过程。 祝大家好运，希望你们能让我知道如何修订这本书才能更好地满足初学统计学的学生的需求。

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher quality dataset, even with big data; How the principles of experimental design yield definitive answers to questions; How to use regression to estimate outcomes and detect anomalies; Key classification techniques for predicting which categories a record belongs to; Statistical machine learning methods that "learn" from data; Unsupervised learning methods for extracting meaning from unlabeled data.

统计预测与决策（第五版）上海财经大学出版社 徐国祥配套PPT

非打印版，文字可选取 [目录] 1. Generalities. 2. The Weak Topology and Its Metrization. 3. The Basic Types of Estimates. 4. Asymptotic Minimax Theory for Estimating a Location Parameter. 5. Scale Estimates. 6. Multiparameter Problems, In Particular Joint Estimation of Location and Scale. 7. Regression. 8. Robust Covariance and Correlation Matrices. 9. Rubustness of Design. 10. Exact Finite Sample Results. 11. Miscellaneous Topics. References. Index.