Learning Statistics with Real Data
Learning Statistics with Real Data

Author: Bruce E. Trumbo

Publisher: Brooks/Cole

Published: 2002

Total Pages: 324

ISBN-13: 9780534362133

DOWNLOAD EBOOK

Designed to teach students to apply statistical methods to real problems (a universal need), Bruce Trumbo's concise new book teaches basic statistical principles through their application to real data. The data sets are chosen from fields to which all students can relate, such as marketing, industrial safety, anthropology, psychology, banking, biology, linguistics, public health, geography, physics, sports, geology, and medicine. Throughout the book, the emphasis is on how statistical ideas and methods can be used to illuminate the data, rather than on how the data can be used to illustrate particular statistical methods. Some of the basic statistical methods that prove to be useful include graphical displays, confidence intervals, one and two-sample t-tests, chi-squared analyses of contingency tables, simple and multiple linear regression, correlation, one-way ANOVAs, and block designs. For each data set, students are guided through some basic procedures, usually using MINITAB(tm), then invited to explore the data more extensively on their own, with answers and possible approaches.

Learning Statistics with R
Learning Statistics with R

Author: Daniel Navarro

Publisher: Lulu.com

Published: 2013-01-13

Total Pages: 617

ISBN-13: 1326189727

DOWNLOAD EBOOK

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

The Art of Statistics
The Art of Statistics

Author: David Spiegelhalter

Publisher: Basic Books

Published: 2019-09-03

Total Pages: 359

ISBN-13: 1541618521

DOWNLOAD EBOOK

In this "important and comprehensive" guide to statistical thinking (New Yorker), discover how data literacy is changing the world and gives you a better understanding of life’s biggest problems. Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

Statistical Thinking from Scratch
Statistical Thinking from Scratch

Author: M. D. Edge

Publisher:

Published: 2019

Total Pages: 318

ISBN-13: 0198827628

DOWNLOAD EBOOK

Focuses on detailed instruction in a single statistical technique, simple linear regression (SLR), with the goal of gaining tools, understanding, and intuition that can be applied to other contexts.

Teaching Statistics
Teaching Statistics

Author: Andrew Gelman

Publisher: OUP Oxford

Published: 2002-08-08

Total Pages: 353

ISBN-13: 0191606995

DOWNLOAD EBOOK

Students in the sciences, economics, psychology, social sciences, and medicine take introductory statistics. Statistics is increasingly offered at the high school level as well. However, statistics can be notoriously difficult to teach as it is seen by many students as difficult and boring, if not irrelevant to their subject of choice. To help dispel these misconceptions, Gelman and Nolan have put together this fascinating and thought-provoking book. Based on years of teaching experience the book provides a wealth of demonstrations, examples and projects that involve active student participation. Part I of the book presents a large selection of activities for introductory statistics courses and combines chapters such as, 'First week of class', with exercises to break the ice and get students talking; then 'Descriptive statistics' , collecting and displaying data; then follows the traditional topics - linear regression, data collection, probability and inference. Part II gives tips on what does and what doesn't work in class: how to set up effective demonstrations and examples, how to encourage students to participate in class and work effectively in group projects. A sample course plan is provided. Part III presents material for more advanced courses on topics such as decision theory, Bayesian statistics and sampling.

Learning Statistics Using R
Learning Statistics Using R

Author: Randall E. Schumacker

Publisher: SAGE Publications

Published: 2014-01-28

Total Pages: 648

ISBN-13: 148332477X

DOWNLOAD EBOOK

Providing easy-to-use R script programs that teach descriptive statistics, graphing, and other statistical methods, Learning Statistics Using R shows readers how to run and utilize R, a free integrated statistical suite that has an extensive library of functions. Randall E. Schumacker’s comprehensive book describes in detail the processing of variables in statistical procedures. Covering a wide range of topics, from probability and sampling distribution to statistical theorems and chi-square, this introductory book helps readers learn not only how to use formulae to calculate statistics, but also how specific statistics fit into the overall research process. Learning Statistics Using R covers data input from vectors, arrays, matrices and data frames, as well as the input of data sets from SPSS, SAS, STATA and other software packages. Schumacker’s text provides the freedom to effectively calculate, manipulate, and graphically display data, using R, on different computer operating systems without the expense of commercial software. Learning Statistics Using R places statistics within the framework of conducting research, where statistical research hypotheses can be directly addressed. Each chapter includes discussion and explanations, tables and graphs, and R functions and outputs to enrich readers′ understanding of statistics through statistical computing and modeling.

Learn R for Applied Statistics
Learn R for Applied Statistics

Author: Eric Goh Ming Hui

Publisher: Apress

Published: 2018-11-30

Total Pages: 254

ISBN-13: 1484242009

DOWNLOAD EBOOK

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. What You Will LearnDiscover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work with descriptive statistics Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.

How to Lie with Statistics
How to Lie with Statistics

Author: Darrell Huff

Publisher: W. W. Norton & Company

Published: 2010-12-07

Total Pages: 144

ISBN-13: 0393070875

DOWNLOAD EBOOK

If you want to outsmart a crook, learn his tricks—Darrell Huff explains exactly how in the classic How to Lie with Statistics. From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff’s lively and engaging primer clarifies the basic principles of statistics and explains how they’re used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled.

Practical Statistics for Data Scientists
Practical Statistics for Data Scientists

Author: Peter Bruce

Publisher: "O'Reilly Media, Inc."

Published: 2017-05-10

Total Pages: 322

ISBN-13: 1491952911

DOWNLOAD EBOOK

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. With this book, you’ll learn: 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