Thinking Statistically

Thinking Statistically is the "sharp little book" that shows you how to think like a statistician, without worrying about formal statistical techniques. Along the way we learn how selection bias can explain why your boss doesn't know he sucks (even when everyone else does); how to use Bayes' Theorem to decide if your partner is cheating on you; and why Mark Zuckerberg should never be used as an example for anything. See the world in a whole new light, and make better decisions and judgements without ever going near a t-test. Think. Think Statistically.

How statistical thinking and methodology can help you make crucial business decisions Straightforward and insightful, Statistical Thinking: Improving Business Performance, Second Edition, prepares you for business leadership by developing your capacity to apply statistical thinking to improve business processes. Unique and compelling, this book shows you how to derive actionable conclusions from data analysis, solve real problems, and improve real processes. Here, you'll discover how to implement statistical thinking and methodology in your work to improve business performance. Explores why statistical thinking is necessary and helpful Provides case studies that illustrate how to integrate several statistical tools into the decision-making process Facilitates and encourages an experiential learning environment to enable you to apply material to actual problems With an in-depth discussion of JMP® software, the new edition of this important book focuses on skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, and understanding the limitations of statistical analyses.

Statistical statements and results are read and used in everyday life and are also presented in the form of charts and diagrams. This book covers basic introductory statistics and brings to light how these statistical results are arrived at, starting with the collection of the data, its organization and summarisation, followed by the mathematical tools used both graphical and analytical to depict, describe, analyse and interpret the data. The book begins with a chapter that discusses what is data, types of data, and examines the difference between data collected for a population as opposed to a sample. The chapters that follow go on to discuss the organisation and summarisation of data in the form of frequency distributions to make it clearer and more manageable to deal with. Also covered are various graphical devices such as bar graphs, pie charts, histograms, frequency polygons, by which data may be depicted to make it easier to visualize and reveal any patterns or trends. Attention is then directed to analytical methods used to describe and characterise data sets by calculating some special numbers called numerical descriptive measures which include measures of central tendency (mean, median, mode), measures of dispersion (range, standard variation, variance), measures of relative standing (percentiles, quartiles, z- scores) and measures of association (coefficient of correlation). The book concludes with a chapter presenting some basic methods of fitting a straight line to a data set that shows a somewhat linear relationship between two variables thus allowing predictions to be made about the value of one variable given the value of the other variable. Within each chapter wherever new statistical terms are introduced for the first time they are identified in bold type and at the end of each chapter these key terms are summarised and defined or explained. There are also various detailed worked examples in each chapter to help the reader improve his/her understanding of the material discussed.

Simply put, Thinking Through Statistics is a primer on how to maintain rigorous data standards in social science work, and one that makes a strong case for revising the way that we try to use statistics to support our theories. But don’t let that daunt you. With clever examples and witty takeaways, John Levi Martin proves himself to be a most affable tour guide through these scholarly waters. Martin argues that the task of social statistics isn't to estimate parameters, but to reject false theory. He illustrates common pitfalls that can keep researchers from doing just that using a combination of visualizations, re-analyses, and simulations. Thinking Through Statistics gives social science practitioners accessible insight into troves of wisdom that would normally have to be earned through arduous trial and error, and it does so with a lighthearted approach that ensures this field guide is anything but stodgy.

Introduction to Statistical ThinkingBy Benjamin Yakir

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems