Statistics in a Nutshel,
Sarah Boslaugh and Paul Andrew Watters
Need to learn statistics as part of your job, or want some help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference that's perfect for anyone with no previous background in the subject. This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts.
Advances in Data Analysis , Reinhold Decker Hans-J. Lenz
The book focuses on exploratory data analysis, learning of latent structures in datasets, and unscrambling of knowledge. It covers a broad range of methods from multivariate statistics, clustering and classification, visualization and scaling as well as from data and time series analysis. It provides new approaches for information retrieval and data mining. Furthermore, the book reports challenging applications in marketing and management science, banking
Biostatistics: A guide to design, analysis and discovery , by Ronald N. Forthofer, Eun Sul Lee , Mike Hernandez
This textbook does not require extensive background in mathematics, making it user-friendly for all students in the public health sciences field. Instead of highlighting derivations of formulas, the authors provide rationales for the formulas, allowing students to grasp a better understanding of the freeduan.com link between biology and statistics. The material on life tables and survival analysis allows students to better understand the recent literature in the health field, particularly in the study of chronic disease treatment. Biostatistics now includes a companion website to demonstrate the different applications of computer packages for performing the various analyses presented in this text.
Resampling Methods - A Practical Guide to Data Analysis , Phillip I. Good
Uses resampling approach to introduction statistics A practical presentation that covers all three resampling methods - bootstrap, density-estimation, and permutations Includes systematic guide to help one select correct procedure for a particular application Detailed coverage of all three statistical methodologies - classification, estimation, and hypothesis testing. Numerous practical examples in most popular computer programs such as SASTM, StataTM, and StatXactTM Useful appendices with computer programs and code to develop own methods With its accessible style and intuitive topic development, the book is an excellent basic resource and guide to the power, simplicity, and versatility of bootstrap, cross-validation and permutation tests.
Common Errors in Statistics (and How to Avoid Them) , by Phillip I. Good , James W. Hardin
This handy field guide analyzes common mistakes, debunks popular myths, and helps readers to choose the best and most effective statistical technique for each of their tasks.
Written for both the newly minted academic and the professional who uses statistics in their work, the book covers creating a research plan, formulating a hypothesis, specifying sample size, checking assumptions, interpreting p-values and confidence intervals, building a model, data mining, Bayes' Theorem, the bootstrap, and many other topics.
Statistical Methods for Categorical Data Analysis, b y Daniel A. PowersDescription :
Statistical Methods for Categorical Data Analysis is designed as an accessible reference work and textbook about categorical data.Two features distinguish this book from other analysis of categorical data. First, the authors present both the transformational and latent variable approaches and so synthesize similar methods in statistical and econometric literatures. Second, the book has an applied orientation and features actual examples from social science research. The authors keep discussions of theory to a minimum.