Introductory Statistics For Data Analysis

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Introduction To Statistics And Data Analysis

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Author : Christian Heumann
language : en
Publisher: Springer
Release Date : 2017-01-26

Introduction To Statistics And Data Analysis written by Christian Heumann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with Mathematics categories.

This introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital. The text is primarily intended for undergraduate students in disciplines like business administration, the social sciences, medicine, politics, macroeconomics, etc. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R as well as supplementary material that will enable the reader to quickly adapt all methods to their own applications.

Introductory Statistics For Data Analysis

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Author : Warren J. Ewens
language : en
Publisher: Springer Nature
Release Date : 2023-05-12

Introductory Statistics For Data Analysis written by Warren J. Ewens and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-12 with Mathematics categories.

This book describes the probability theory associated with frequently used statistical procedures and the relation between probability theory and statistical inference. The first third of the book is dedicated to probability theory including topics relating to events, random variables, and the Central Limit Theorem. Statistical topics then include parameter estimation with confidence intervals, hypothesis testing, chi-square tests, t tests, and several non-parametric tests. Flow charts are frequently used to facilitate an understanding of the material considered. The examples and problems in the book all concern simple data sets which can be analyzed with a simple calculator; however, the R code required to complete many examples and problems is provided as well for those that are interested.

Introductory Statistics With R

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Author : Peter Dalgaard
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-06-27

Introductory Statistics With R written by Peter Dalgaard and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-27 with Mathematics categories.

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

Introductory Statistics And Analytics

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Author : Peter C. Bruce
language : en
Publisher: John Wiley & Sons
Release Date : 2014-12-31

Introductory Statistics And Analytics written by Peter C. Bruce and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-31 with Mathematics categories.

Concise, thoroughly class-tested primer that features basic statistical concepts in the concepts in the context of analytics, resampling, and the bootstrap A uniquely developed presentation of key statistical topics, Introductory Statistics and Analytics: A Resampling Perspective provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers with various levels of exposure to basic probability and statistics. Originally class-tested at one of the first online learning companies in the discipline, www.statistics.com, the book primarily focuses on applications of statistical concepts developed via resampling, with a background discussion of mathematical theory. This feature stresses statistical literacy and understanding, which demonstrates the fundamental basis for statistical inference and demystifies traditional formulas. The book begins with illustrations that have the essential statistical topics interwoven throughout before moving on to demonstrate the proper design of studies. Meeting all of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) requirements for an introductory statistics course, Introductory Statistics and Analytics: A Resampling Perspective also includes: Over 300 “Try It Yourself” exercises and intermittent practice questions, which challenge readers at multiple levels to investigate and explore key statistical concepts Numerous interactive links designed to provide solutions to exercises and further information on crucial concepts Linkages that connect statistics to the rapidly growing field of data science Multiple discussions of various software systems, such as Microsoft Office Excel®, StatCrunch, and R, to develop and analyze data Areas of concern and/or contrasting points-of-view indicated through the use of “Caution” icons Introductory Statistics and Analytics: A Resampling Perspective is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.

Statistics And Data Analysis

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Author : Andrew F. Siegel
language : en
Publisher: Wiley
Release Date : 1998-02-12

Statistics And Data Analysis written by Andrew F. Siegel and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-02-12 with Mathematics categories.

Introductory statistics book for the non-technical person that integrates the traditional foundations of statistical inference with the more modern ideas of data analysis. The book is divided into three parts. Part One is concerned with data in general and with describing groups of numbers. Part Two develops the ideas of randomness, probability, and statistical inference. Part Three moves forward, applying these ideas to more complex data structures and the analysis of relationships.

Statistics For Data Science And Analytics

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Author : Peter C. Bruce
language : en
Publisher: John Wiley & Sons
Release Date : 2024-08-06

Statistics For Data Science And Analytics written by Peter C. Bruce and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-06 with Computers categories.

Introductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets Experiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data Specialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from them Statistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.

Statistics For Data Science And Analytics

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Author : Peter C. Bruce
language : en
Publisher: John Wiley & Sons
Release Date : 2024-11-05

Statistics For Data Science And Analytics written by Peter C. Bruce and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-05 with Computers categories.

Introductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets Experiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data Specialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from them Statistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.

An Introduction To Statistics And Data Analysis Using Stata

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Author : Lisa Daniels
language : en
Publisher: SAGE Publications
Release Date : 2019-01-11

An Introduction To Statistics And Data Analysis Using Stata written by Lisa Daniels and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-11 with Social Science categories.

An Introduction to Statistics and Data Analysis Using Stata® by Lisa Daniels and Nicholas Minot provides a step-by-step introduction for statistics, data analysis, or research methods classes with Stata. Concise descriptions emphasize the concepts behind statistics for students rather than the derivations of the formulas. With real-world examples from a variety of disciplines and extensive detail on the commands in Stata, this text provides an integrated approach to research design, statistical analysis, and report writing for social science students.

Introduction To Statistical Data Analysis For The Life Sciences Second Edition

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Author : Claus Thorn Ekstrom
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2017-11-15

Introduction To Statistical Data Analysis For The Life Sciences Second Edition written by Claus Thorn Ekstrom and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-15 with categories.

A Hands-On Approach to Teaching Introductory Statistics Expanded with over 100 more pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition presents the right balance of data examples, statistical theory, and computing to teach introductory statistics to students in the life sciences. This popular textbook covers the mathematics underlying classical statistical analysis, the modeling aspects of statistical analysis and the biological interpretation of results, and the application of statistical software in analyzing real-world problems and datasets. New to the Second Edition A new chapter on non-linear regression models A new chapter that contains examples of complete data analyses, illustrating how a full-fledged statistical analysis is undertaken Additional exercises in most chapters A summary of statistical formulas related to the specific designs used to teach the statistical concepts This text provides a computational toolbox that enables students to analyze real datasets and gain the confidence and skills to undertake more sophisticated analyses. Although accessible with any statistical software, the text encourages a reliance on R. For those new to R, an introduction to the software is available in an appendix. The book also includes end-of-chapter exercises as well as an entire chapter of case exercises that help students apply their knowledge to larger datasets and learn more about approaches specific to the life sciences.

Introductory Statistics

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Author : Jay L. Devore
language : en
Publisher: West Publishing Company
Release Date : 1994

Introductory Statistics written by Jay L. Devore and has been published by West Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Mathematics categories.

This text combines traditional coverage of beginning probability and statistics with emphasis on real applications taken from a wide variety of published sources. Designed for a one-semester course, it emphasizes concepts and an intuitive presentation of core methodology using a wide variety of applications. While not presupposing the use of a statistical computer package, the role of the computer in data analysis is illustrated with examples that show output from Minitab "RM", SPSS "RM", and SAS. It includes: -- Many worked-out examples -- An exercise set at the end of each section -- Supplementary exercises and a summary of key concepts and formulas at the end of each chapter