| basic statistics for data analysis: The Art of Data Analysis Kristin H. Jarman, 2013-05-13 A friendly and accessible approach to applying statistics in the real world With an emphasis on critical thinking, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics presents fun and unique examples, guides readers through the entire data collection and analysis process, and introduces basic statistical concepts along the way. Leaving proofs and complicated mathematics behind, the author portrays the more engaging side of statistics and emphasizes its role as a problem-solving tool. In addition, light-hearted case studies illustrate the application of statistics to real data analyses, highlighting the strengths and weaknesses of commonly used techniques. Written for the growing academic and industrial population that uses statistics in everyday life, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics highlights important issues that often arise when collecting and sifting through data. Featured concepts include: • Descriptive statistics • Analysis of variance • Probability and sample distributions • Confidence intervals • Hypothesis tests • Regression • Statistical correlation • Data collection • Statistical analysis with graphs Fun and inviting from beginning to end, The Art of Data Analysis is an ideal book for students as well as managers and researchers in industry, medicine, or government who face statistical questions and are in need of an intuitive understanding of basic statistical reasoning. |
| basic statistics for data analysis: Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce, 2017-05-10 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 |
| basic statistics for data analysis: Statistical Thinking from Scratch M. D. Edge, 2019 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. |
| basic statistics for data analysis: Beyond Basic Statistics Kristin H. Jarman, 2015-04-22 Features basic statistical concepts as a tool for thinking critically, wading through large quantities of information, and answering practical, everyday questions Written in an engaging and inviting manner, Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know presents the more subjective side of statistics—the art of data analytics. Each chapter explores a different question using fun, common sense examples that illustrate the concepts, methods, and applications of statistical techniques. Without going into the specifics of theorems, propositions, or formulas, the book effectively demonstrates statistics as a useful problem-solving tool. In addition, the author demonstrates how statistics is a tool for thinking critically, wading through large volumes of information, and answering life’s important questions. Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know also features: Plentiful examples throughout aimed to strengthen readers’ understanding of the statistical concepts and methods A step-by-step approach to elementary statistical topics such as sampling, hypothesis tests, outlier detection, normality tests, robust statistics, and multiple regression A case study in each chapter that illustrates the use of the presented techniques Highlights of well-known shortcomings that can lead to false conclusions An introduction to advanced techniques such as validation and bootstrapping Featuring examples that are engaging and non-application specific, the book appeals to a broad audience of students and professionals alike, specifically students of undergraduate statistics, managers, medical professionals, and anyone who has to make decisions based on raw data or compiled results. |
| basic statistics for data analysis: Beginning Statistics with Data Analysis Frederick Mosteller, Stephen E. Fienberg, Robert E.K. Rourke, 2013-11-20 This introduction to the world of statistics covers exploratory data analysis, methods for collecting data, formal statistical inference, and techniques of regression and analysis of variance. 1983 edition. |
| basic statistics for data analysis: Introduction to Statistics and Data Analysis Roxy Peck, Chris Olsen, Jay L. Devore, 2015-03-27 INTRODUCTION TO STATISTICS AND DATA ANALYSIS introduces you to the study of statistics and data analysis by using real data and attention-grabbing examples. The authors guide you through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including frequent substitution of words for symbols--helps you grasp concepts and cement your comprehension. You'll also find coverage of most major technologies as a problem-solving tool, plus hands-on activities in each chapter that allow you to practice statistics firsthand. |
| basic statistics for data analysis: Basic Statistics and Data Analysis Larry J. Kitchens, 2002 With an emphasis on exploratory data analysis, this title teaches students to identify trends in their data that may help them ask the right questions. It develops students' statistical intuition and nurtures the development of a statistical way of thinking. |
| basic statistics for data analysis: Statistical Data Analysis Glen Cowan, 1998 This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are takenfrom particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques,statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding). |
| basic statistics for data analysis: Basic Statistics with R Stephen C. Loftus, 2021-02-20 Basic Statistics with R: Reaching Decisions with Data provides an understanding of the processes at work in using data for results. Sections cover data collection and discuss exploratory analyses, including visual graphs, numerical summaries, and relationships between variables - basic probability, and statistical inference - including hypothesis testing and confidence intervals. All topics are taught using real-data drawn from various fields, including economics, biology, political science and sports. Using this wide variety of motivating examples allows students to directly connect and make statistics essential to their field of interest, rather than seeing it as a separate and ancillary knowledge area. In addition to introducing students to statistical topics using real data, the book provides a gentle introduction to coding, having the students use the statistical language and software R. Students learn to load data, calculate summary statistics, create graphs and do statistical inference using R with either Windows or Macintosh machines. - Features real-data to give students an engaging practice to connect with their areas of interest - Evolves from basic problems that can be worked by hand to the elementary use of opensource R software - Offers a direct, clear approach highlighted by useful visuals and examples |
| basic statistics for data analysis: Introduction to Statistics and Data Analysis Christian Heumann, Michael Schomaker, Shalabh, 2023-01-26 Now in its second edition, 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. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference. The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. 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 the methods to their own applications. |
| basic statistics for data analysis: Basic Statistics for Social Research Robert A. Hanneman, Augustine J. Kposowa, Mark D. Riddle, 2012-12-04 A core statistics text that emphasizes logical inquiry, not math Basic Statistics for Social Research teaches core general statistical concepts and methods that all social science majors must master to understand (and do) social research. Its use of mathematics and theory are deliberately limited, as the authors focus on the use of concepts and tools of statistics in the analysis of social science data, rather than on the mathematical and computational aspects. Research questions and applications are taken from a wide variety of subfields in sociology, and each chapter is organized around one or more general ideas that are explained at its beginning and then applied in increasing detail in the body of the text. Each chapter contains instructive features to aid students in understanding and mastering the various statistical approaches presented in the book, including: Learning objectives Check quizzes after many sections and an answer key at the end of the chapter Summary Key terms End-of-chapter exercises SPSS exercises (in select chapters) Ancillary materials for both the student and the instructor are available and include a test bank for instructors and downloadable video tutorials for students. |
| basic statistics for data analysis: An Introduction to Statistics and Data Analysis Using Stata® Lisa Daniels, Nicholas Minot, 2019-01-11 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. |
| basic statistics for data analysis: Essential Statistics for Non-STEM Data Analysts Rongpeng Li, 2020-11-12 Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programming Key FeaturesWork your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisionsUnderstand how various data science algorithms functionBuild a solid foundation in statistics for data science and machine learning using Python-based examplesBook Description Statistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines. This book is a detailed guide covering the math and various statistical methods required for undertaking data science tasks. The book starts by showing you how to preprocess data and inspect distributions and correlations from a statistical perspective. You’ll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. As you advance, you’ll find out how statistical concepts emerge from different stages of data science pipelines, understand the summary of datasets in the language of statistics, and use it to build a solid foundation for robust data products such as explanatory models and predictive models. Once you’ve uncovered the working mechanism of data science algorithms, you’ll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. Finally, you’ll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning. By the end of this Essential Statistics for Non-STEM Data Analysts book, you’ll have learned how to build and present a self-contained, statistics-backed data product to meet your business goals. What you will learnFind out how to grab and load data into an analysis environmentPerform descriptive analysis to extract meaningful summaries from dataDiscover probability, parameter estimation, hypothesis tests, and experiment design best practicesGet to grips with resampling and bootstrapping in PythonDelve into statistical tests with variance analysis, time series analysis, and A/B test examplesUnderstand the statistics behind popular machine learning algorithmsAnswer questions on statistics for data scientist interviewsWho this book is for This book is an entry-level guide for data science enthusiasts, data analysts, and anyone starting out in the field of data science and looking to learn the essential statistical concepts with the help of simple explanations and examples. If you’re a developer or student with a non-mathematical background, you’ll find this book useful. Working knowledge of the Python programming language is required. |
| basic statistics for data analysis: Introduction to Statistics Howard M. Reid, 2013-08-13 Using a truly accessible and reader-friendly approach, Introduction to Statistics: Fundamental Concepts and Procedures of Data Analysis, by Howard M. Reid, redefines the way statistics can be taught and learned. Unlike other books that merely focus on procedures, Reid’s approach balances development of critical thinking skills with application of those skills to contemporary statistical analysis. He goes beyond simply presenting techniques by focusing on the key concepts readers need to master in order to ensure their long-term success. Indeed, this exciting new book offers the perfect foundation upon which readers can build as their studies and careers progress to more advanced forms of statistics. Keeping computational challenges to a minimum, Reid shows readers not only how to conduct a variety of commonly used statistical procedures, but also when each procedure should be utilized and how they are related. Following a review of descriptive statistics, he begins his discussion of inferential statistics with a two-chapter examination of the Chi Square test to introduce students to hypothesis testing, the importance of determining effect size, and the need for post hoc tests. When more complex procedures related to interval/ratio data are covered, students already have a solid understanding of the foundational concepts involved. Exploring challenging topics in an engaging and easy-to-follow manner, Reid builds concepts logically and supports learning through robust pedagogical tools, the use of SPSS, numerous examples, historical quotations, insightful questions, and helpful progress checks. |
| basic statistics for data analysis: SPSS Statistics for Data Analysis and Visualization Keith McCormick, Jesus Salcedo, 2017-05-01 Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis Display complex relationships and create better visualizations Model complex interactions and master predictive analytics Integrate R and Python with SPSS Statistics for more efficient, more powerful code These hidden tools can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need. |
| basic statistics for data analysis: Statistics 101 David Borman, 2018-12-18 A comprehensive guide to statistics—with information on collecting, measuring, analyzing, and presenting statistical data—continuing the popular 101 series. Data is everywhere. In the age of the internet and social media, we’re responsible for consuming, evaluating, and analyzing data on a daily basis. From understanding the percentage probability that it will rain later today, to evaluating your risk of a health problem, or the fluctuations in the stock market, statistics impact our lives in a variety of ways, and are vital to a variety of careers and fields of practice. Unfortunately, most statistics text books just make us want to take a snooze, but with Statistics 101, you’ll learn the basics of statistics in a way that is both easy-to-understand and apply. From learning the theory of probability and different kinds of distribution concepts, to identifying data patterns and graphing and presenting precise findings, this essential guide can help turn statistical math from scary and complicated, to easy and fun. Whether you are a student looking to supplement your learning, a worker hoping to better understand how statistics works for your job, or a lifelong learner looking to improve your grasp of the world, Statistics 101 has you covered. |
| basic statistics for data analysis: Statistical Methods for Data Analysis in Particle Physics Luca Lista, 2017-10-13 This concise set of course-based notes provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP). First, the book provides an introduction to probability theory and basic statistics, mainly intended as a refresher from readers’ advanced undergraduate studies, but also to help them clearly distinguish between the Frequentist and Bayesian approaches and interpretations in subsequent applications. More advanced concepts and applications are gradually introduced, culminating in the chapter on both discoveries and upper limits, as many applications in HEP concern hypothesis testing, where the main goal is often to provide better and better limits so as to eventually be able to distinguish between competing hypotheses, or to rule out some of them altogether. Many worked-out examples will help newcomers to the field and graduate students alike understand the pitfalls involved in applying theoretical concepts to actual data. This new second edition significantly expands on the original material, with more background content (e.g. the Markov Chain Monte Carlo method, best linear unbiased estimator), applications (unfolding and regularization procedures, control regions and simultaneous fits, machine learning concepts) and examples (e.g. look-elsewhere effect calculation). |
| basic statistics for data analysis: Statistical Data Analysis and Entropy Nobuoki Eshima, 2020-01-21 This book reconsiders statistical methods from the point of view of entropy, and introduces entropy-based approaches for data analysis. Further, it interprets basic statistical methods, such as the chi-square statistic, t-statistic, F-statistic and the maximum likelihood estimation in the context of entropy. In terms of categorical data analysis, the book discusses the entropy correlation coefficient (ECC) and the entropy coefficient of determination (ECD) for measuring association and/or predictive powers in association models, and generalized linear models (GLMs). Through association and GLM frameworks, it also describes ECC and ECD in correlation and regression analyses for continuous random variables. In multivariate statistical analysis, canonical correlation analysis, T2-statistic, and discriminant analysis are discussed in terms of entropy. Moreover, the book explores the efficiency of test procedures in statistical tests of hypotheses using entropy. Lastly, it presents an entropy-based path analysis for structural GLMs, which is applied in factor analysis and latent structure models. Entropy is an important concept for dealing with the uncertainty of systems of random variables and can be applied in statistical methodologies. This book motivates readers, especially young researchers, to address the challenge of new approaches to statistical data analysis and behavior-metric studies. |
| basic statistics for data analysis: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| basic statistics for data analysis: All of Statistics Larry Wasserman, 2013-12-11 Taken literally, the title All of Statistics is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data. |
| basic statistics for data analysis: How to Use Pasw Statistics Brian. C. Cronk, Brian C. Cronk, 2020-07-24 • Designed for use by novice computer users, this text begins with the basics, such as starting SPSS, defining variables, and entering and saving data. • All major statistical techniques covered in beginning statistics classes are included: · descriptive statistics · graphing data · prediction and association · parametric inferential statistics · nonparametric inferential statistics · statistics for test construction • Each section starts with a brief description of the statistic that is covered and important underlying assumptions, which help students select appropriate statistics. • Each section describes how to interpret results and express them in a research report after the data are analyzed. For example, students are shown how to phrase the results of a significant and an insignificant t test. • More than 200 screenshots (including sample output) throughout the book show students exactly what to expect as they follow along using SPSS. • A glossary of statistical terms is included, which makes a handy reference for students who need to review the meanings of basic statistical terms. • Practice exercises throughout the book give students stimulus material to use as they practice to achieve mastery of the program. • Thoroughly field-tested; your students are certain to appreciate this book. |
| basic statistics for data analysis: Head First Data Analysis Michael Milton, 2009-07-24 A guide for data managers and analyzers. It shares guidelines for identifying patterns, predicting future outcomes, and presenting findings to others. |
| basic statistics for data analysis: Statistical Methods Cheryl Ann Willard, 2020-02-27 Statistical Methods: An Introduction to Basic Statistical Concepts and Analysis, Second Edition is a textbook designed for students with no prior training in statistics. It provides a solid background of the core statistical concepts taught in most introductory statistics textbooks. Mathematical proofs are deemphasized in favor of careful explanations of statistical constructs. The text begins with coverage of descriptive statistics such as measures of central tendency and variability, then moves on to inferential statistics. Transitional chapters on z-scores, probability, and sampling distributions pave the way to understanding the logic of hypothesis testing and the inferential tests that follow. Hypothesis testing is taught through a four-step process. These same four steps are used throughout the text for the other statistical tests presented including t tests, one- and two-way ANOVAs, chi-square, and correlation. A chapter on nonparametric tests is also provided as an alternative when the requirements cannot be met for parametric tests. Because the same logical framework and sequential steps are used throughout the text, a consistency is provided that allows students to gradually master the concepts. Their learning is enhanced further with the inclusion of thought questions and practice problems integrated throughout the chapters. New to the second edition: Chapters on factorial analysis of variance and non-parametric techniques for all data Additional and updated chapter exercises for students to test and demonstrate their learning Full instructor resources: test bank questions, Powerpoint slides, and an Instructor Manual |
| basic statistics for data analysis: Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson, 2015-04-21 The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. |
| basic statistics for data analysis: The Art of Data Analysis Kristin H. Jarman, 2013-07-22 A friendly and accessible approach to applying statistics in the real world With an emphasis on critical thinking, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics presents fun and unique examples, guides readers through the entire data collection and analysis process, and introduces basic statistical concepts along the way. Leaving proofs and complicated mathematics behind, the author portrays the more engaging side of statistics and emphasizes its role as a problem-solving tool. In addition, light-hearted case studies illustrate the application of statistics to real data analyses, highlighting the strengths and weaknesses of commonly used techniques. Written for the growing academic and industrial population that uses statistics in everyday life, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics highlights important issues that often arise when collecting and sifting through data. Featured concepts include: • Descriptive statistics • Analysis of variance • Probability and sample distributions • Confidence intervals • Hypothesis tests • Regression • Statistical correlation • Data collection • Statistical analysis with graphs Fun and inviting from beginning to end, The Art of Data Analysis is an ideal book for students as well as managers and researchers in industry, medicine, or government who face statistical questions and are in need of an intuitive understanding of basic statistical reasoning. |
| basic statistics for data analysis: Statistics for Data Scientists Maurits Kaptein, Edwin van den Heuvel, 2022-02-02 This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science. |
| basic statistics for data analysis: The Basic Practice of Statistics David S. Moore, 2010 This is a clear and innovative overview of statistics which emphasises major ideas, essential skills and real-life data. The organisation and design has been improved for the fifth edition, coverage of engaging, real-world topics has been increased and content has been updated to appeal to today's trends and research. |
| basic statistics for data analysis: Illustrating Statistical Procedures: Finding Meaning in Quantitative Data Ray W. Cooksey, 2020-05-14 This book occupies a unique position in the field of statistical analysis in the behavioural and social sciences in that it targets learners who would benefit from learning more conceptually and less computationally about statistical procedures and the software packages that can be used to implement them. This book provides a comprehensive overview of this important research skill domain with an emphasis on visual support for learning and better understanding. The primary focus is on fundamental concepts, procedures and interpretations of statistical analyses within a single broad illustrative research context. The book covers a wide range of descriptive, correlational and inferential statistical procedures as well as more advanced procedures not typically covered in introductory and intermediate statistical texts. It is an ideal reference for postgraduate students as well as for researchers seeking to broaden their conceptual exposure to what is possible in statistical analysis. |
| basic statistics for data analysis: Basic Statistics and Epidemiology Antony Stewart, 2007 A guide in basic statistics emphasises its practical use in epidemiology and public health, providing understanding of topics such as study design, data analysis and statistical methods used in the execution of medical research. This title includes sections on Correlation and Linear Regression, as well as exercises reflecting working life. |
| basic statistics for data analysis: Basic Statistics Tenko Raykov, George A. Marcoulides, 2013 Basic Statistics provides an accessible and comprehensive introduction to statistics using the free, state-of-the-art, powerful software program R. This book is designed to both introduce students to key concepts in statistics and to provide simple instructions for using R. This concise book: Teaches essential concepts in statistics, assuming little background knowledge on the part of the reader Introduces students to R with as few sub-commands as possible for ease of use Provides practical examples from the educational, behavioral, and social sciences With clear explanations of statistical processes and step-by-step commands in R, Basic Statistics will appeal to students and professionals across the social and behavioral sciences. |
| basic statistics for data analysis: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| basic statistics for data analysis: Statistics for Beginners in Data Science Ai Publishing, 2020-04-18 Statistics for Beginners in Data Science Statistical methods are an integral part of data science. Hence, a formal training in statistics is indispensable for data scientists. If you are keen on getting your foot into the lucrative data science and analysis universe, you need to have a fundamental understanding of statistical analysis. Besides, Python is a versatile programming language you need to master to become a career data scientist. As a data scientist, you will identify, clean, explore, analyze, and interpret trends or possible patterns in complex data sets. The explosive growth of Big Data means you have to manage enormous amounts of data, clean it, manipulate it, and process it. Only then the most relevant data can be used. Python is a natural data science tool as it has an assortment of useful libraries, such as Pandas, NumPy, SciPy, Matplotlib, Seaborn, StatsModels, IPython, and several more. And Python's focus on simplicity makes it relatively easy for you to learn. Importantly, the ease of performing repetitive tasks saves you precious time. Long story short--Python is simply a high-priority data science tool. How Is This Book Different? The book focuses equally on the theoretical as well as practical aspects of data science. You will learn how to implement elementary data science tools and algorithms from scratch. The book contains an in-depth theoretical and analytical explanation of all data science concepts and also includes dozens of hands-on, real-life projects that will help you understand the concepts better. The ready-to-access Python codes at various places right through the book are aimed at shortening your learning curve. The main goal is to present you with the concepts, the insights, the inspiration, and the right tools needed to dive into coding and analyzing data in Python. The main benefit of purchasing this book is you get quick access to all the extra content provided with this book--Python codes, exercises, references, and PDFs--on the publisher's website, at no extra price. You get to experiment with the practical aspects of Data Science right from page 1. Beginners in Python and statistics will find this book extremely informative, practical, and helpful. Even if you aren't new to Python and data science, you'll find the hands-on projects in this book immensely helpful. The topics covered include: Introduction to Statistics Getting Familiar with Python Data Exploration and Data Analysis Pandas, Matplotlib, and Seaborn for Statistical Visualization Exploring Two or More Variables and Categorical Data Statistical Tests and ANOVA Confidence Interval Regression Analysis Classification Analysis Click the BUY button and download the book now to start learning and coding Python for Data Science. |
| basic statistics for data analysis: Learning Statistics with R Daniel Navarro, 2013-01-13 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 |
| basic statistics for data analysis: Basic and Advanced Statistical Tests Amanda Ross, Victor L. Willson, 2018-01-03 This book focuses on extraction of pertinent information from statistical test outputs, in order to write result sections and/or accompanying tables and/or figures. The book is divided into two encompassing sections: Part I – Basic Statistical Tests and Part II – Advanced Statistical Tests. Part I includes 9 basic statistical tests, and Part II includes 7 advanced statistical tests. Each chapter provides the name of a basic or advanced statistical test, a brief description, examples of when to use each, a sample scenario, and a sample results section write-up. Depending on the test and need, most chapters provide a table and/or figure to accompany the write-up. The purpose of the book is to provide researchers with a reference manual for writing results sections and tables/figures in scholarly works. The authors fill a gap in research support manuals by focusing on sample write-ups and tables/figures for given statistical tests. The book assists researchers by eliminating the need to comb through numerous publications to determine necessary information to report, as well as correct APA format to use, at the close of analyses. |
| basic statistics for data analysis: The Statistical Analysis of Discrete Data Thomas J. Santner, Diane E. Duffy, 2012-12-06 The Statistical Analysis of Discrete Data provides an introduction to cur rent statistical methods for analyzing discrete response data. The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data. The book's mathematical prereq uisites are linear algebra and elementary advanced calculus. It assumes a basic statistics course which includes some decision theory, and knowledge of classical linear model theory for continuous response data. Problems are provided at the end of each chapter to give the reader an opportunity to ap ply the methods in the text, to explore extensions of the material covered, and to analyze data with discrete responses. In the text examples, and in the problems, we have sought to include interesting data sets from a wide variety of fields including political science, medicine, nuclear engineering, sociology, ecology, cancer research, library science, and biology. Although there are several texts available on discrete data analysis, we felt there was a need for a book which incorporated some of the myriad recent research advances. Our motivation was to introduce the subject by emphasizing its ties to the well-known theories of linear models, experi mental design, and regression diagnostics, as well as to describe alterna tive methodologies (Bayesian, smoothing, etc. ); the latter are based on the premise that external information is available. These overriding goals, to gether with our own experiences and biases, have governed our choice of topics. |
| basic statistics for data analysis: Essential Statistics for Bioscientists Mohammed Meah, 2022-07-25 Dive into the most common statistical tests and software packages used for scientific data analysis and interpretation In Essential Statistics For Bioscientists, experienced university and bioscientist Dr Mohammed Meah delivers easy access to statistical analysis and data presentation. It is a great resource for students in the field of life and health sciences to conceptualize, analyze, and present data. This book uses three popular and commonly used statistics softwares—Microsoft Excel, Graphpad Prism, and SPSS—and offers clear, step-by-step instructions for essential data analysis and graphical/tabular display of data. Beginning with fundamental statistics terminology and concepts, including data types, descriptive statistics (central and spread of data), exploratory statistics (graphical display) and inferential statistics (hypothesis testing and correlation), the content gradually builds in complexity, explaining which statistical test is best suited and how to perform it. A thorough introduction to basic statistical terms and building up to an advanced level of statistical application- ideal for those new to study of statistics Extensive application of three popular software packages- Microsoft Excel, Graphpad Prism and SPSS Numerous hands-on examples of performing data analysis using Microsoft Excel, Graphpad Prism, and SPSS Considers the limitations and errors of statistical analysis Essential reading for those designing and planning a research project in Biosciences Perfect for undergraduate students in the life and health sciences, Essential Statistics For Bioscientists will also earn a place in the libraries of anyone studying medicine, nursing, physiotherapy, pharmacy, and dentistry requiring a refresher or primer on statistical fundamentals. |
| basic statistics for data analysis: Statistical Analysis with Missing Data Roderick J. A. Little, Donald B. Rubin, 2019-03-21 An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work has been no less than defining and transforming. (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry. |
| basic statistics for data analysis: Statistical Methods in Water Resources D.R. Helsel, R.M. Hirsch, 1993-03-03 Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences. |
| basic statistics for data analysis: Hypothesis Testing Jim Frost, 2020-08-29 Build a solid foundation for understanding how hypothesis tests work and become confident that you know when to use each type of test, how to use them properly to obtain reliable results, and interpret the results correctly. Chances are high that you'll need a working knowledge of hypothesis testing to produce new findings yourself and to understand the work of others. I present a wide variety of tests that assess characteristics of different data types. I focus on helping you grasp key concepts, methodologies, and procedures while deemphasizing equations. Learn how to use these tests painlessly in this ebook!In today's data-driven world, we hear about making decisions based on the data all the time. Hypothesis testing plays a crucial role in that process, whether you're in academia, making business decisions, or in quality improvement. Without hypothesis tests, you risk drawing the wrong conclusions and making bad decisions. The world today produces more data and more analyses designed to influence you than ever before. Are you ready for it? In this 367-page ebook, build the skills and knowledge you'll need for effective hypothesis testing, including the following: Why you need hypothesis tests and how they work. Using significance levels, p-values, confidence intervals. Select the correct type of hypothesis test to answer your question. Learn how to test means, medians, variances, proportions, distributions, counts, correlations for continuous and categorical data, and outliers. Use One-Way ANOVA, Two-Way ANOVA and interaction effects. Interpreting the results. Checking assumptions and obtaining reliable results. Manage the error rates for false positives and false negatives. Understand sampling distributions, central limit theorem, and statistical power. Know how t-tests, F-tests, chi-squared, and post hoc tests work. Learn about the differences between parametric, nonparametric, and bootstrapping methods. Examples of different types of hypothesis tests. Downloadable datasets so you can try it yourself. For each hypothesis test I cover, you will learn what it tells you, understand its assumptions, know how to interpret the results, and work through examples with downloadable datasets. |
| basic statistics for data analysis: Basic Statistics for Business and Economics Douglas A. Lind, William G. Marchal, Samuel Adam Wathen, 2013 |
Basic Statistics, Page 1 Basic Statistics - Pennsylvania State …
The purpose of this learning module is to introduce you to some of the fundamental definitions and techniques related to analyzing measurements with statistics. In all the definitions and …
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First, we consider the summary of data associated with categorical (either nominal or ordinal) and sometimes discrete numerical variables.
AN INTRODUCTION TO STATISTICS
The data presented in Table 1 were collected on the flrst day of classes during the Spring 1999 semester. These data provide information about the 67 students who
Basic Statistics - eoss.asu.edu
amount of water each day. There is no replication, so no data analysis can be done and this is not a valid experiment. Figure 2. Having only one plant per treatment is an example of no …
Basic Principles of Statistical Inference - Harvard University
Statistics for Social Scientists Quantitative social science research: 1 Find a substantive question 2 Construct theory and hypothesis 3 Design an empirical study and collect data 4 Use statistics …
Basic Statistical Analysis & SPSS - University of Lucknow
•The use of spreadsheets for data analysis and word processors or presentation software for reports improves upon your existing skills.
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There are two basic types of structured data: numeric and categorical. Numeric data comes in two forms: continuous , such as wind speed or time duration, and discrete ,
Fundamentals of Statistics - Pearson
Reading this chapter will help you understand the fundamentals of statistics and introduce you to concepts that are used throughout this book. The five words population, sample, parameter, …
An introduction to statistical data analysis (Summer 2014) …
By the end of the course you should know the following: • Basic usage of the R language for data analysis. • Basic understanding of the logic of significance testing and hypothesis testing. • …
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Title Basic Statistics and Data Analysis Version 1.2.2 Date 2023-09-14 LazyData yes Maintainer Alan T. Arnholt
Description Data sets for book ``Basic Statistics and …
Introduction to Statistical Analysis - Flinders University
Introduction to Statistical Analysis_2018_02_21.ppt [Compatibility Mode] Please note that the workshop is aimed to be a brief introduction to the topic and this PowerPoint is primarily …
CHAPTER 8 INTRODUCTION TO STATISTICAL ANALYSIS
Statistics refers to a variety of processes that deal with data 1 . In particular, statistics includes the collection, assembly, classification, summarization, presentation, and analysis of data. Analysis …
STATISTICS and DATA ANALYSIS - New York University
The course COR1-GB.1305 is designed to achieve an understanding of fundamental notions of data presentation and analysis and to use statistical thinking in the context of business problems.
Basics of Statistics
Statistics consists of a body of methods for col-lecting and analyzing data. (Agresti & Finlay, 1997) From above, it should be clear that statistics is much more than just the tabu-lation of numbers …
Use of Basic Statistics UNIT 4 OVERVIEW OF STATISTICAL
Statistics is a science that deals with the collection, organization, analysis, interpretation and presentation of information that can be presented numerically and/or graphically to help us …
A Really Simple Guide to Quantitative Data Analysis
The best kind of quantitative data in statistical analysis is numerical, followed by ordinal, and lastly nominal. It is important to know what kind of data you are planning to collect or analyse as this …
Basic Statistics Terms and Calculations - Metropolitan …
In the study of statistics, it is important to be familiar with a variety of terms. Note: There are certain terms used in statistical analysis that only apply to Quantitative Variables.
Introduction to Data Analysis - UNESCO
Organize and present data in a table . Calculate percentages, ratios and indicators . Create graph or chart to illustrate the data. STEP 1.
Introduction to Statistics and Data Analysis
Introduction to Statistics and Data Analysis GeoffreyM.Boynton DepartmentofPsychology UniversityofWashington Latestbuild: November25,2024
Basic Statistics, Page 1 Basic Statistics - Pennsylvania State …
The purpose of this learning module is to introduce you to some of the fundamental definitions and techniques related to analyzing measurements with statistics. In all the definitions and …
INTRODUCTION TO STATISTICS - KSU
Since statistics is used in almost every field of endeavor, the educated individual should be knowledgeable about the vocabulary, concepts, and procedures of statistics.
Statistics Cheat Sheet - Blast Analytics
Check out Probability & Correlation Cheat Sheet for more on this one!
An Introduction to Data Analysis using SPSS
First, we consider the summary of data associated with categorical (either nominal or ordinal) and sometimes discrete numerical variables.
AN INTRODUCTION TO STATISTICS
The data presented in Table 1 were collected on the flrst day of classes during the Spring 1999 semester. These data provide information about the 67 students who
Basic Statistics - eoss.asu.edu
amount of water each day. There is no replication, so no data analysis can be done and this is not a valid experiment. Figure 2. Having only one plant per treatment is an example of no …
Basic Principles of Statistical Inference - Harvard University
Statistics for Social Scientists Quantitative social science research: 1 Find a substantive question 2 Construct theory and hypothesis 3 Design an empirical study and collect data 4 Use statistics …
Basic Statistical Analysis & SPSS - University of Lucknow
•The use of spreadsheets for data analysis and word processors or presentation software for reports improves upon your existing skills.
Statistics for Data Scientists: 50 Essential Concepts
There are two basic types of structured data: numeric and categorical. Numeric data comes in two forms: continuous , such as wind speed or time duration, and discrete ,
Fundamentals of Statistics - Pearson
Reading this chapter will help you understand the fundamentals of statistics and introduce you to concepts that are used throughout this book. The five words population, sample, parameter, …
An introduction to statistical data analysis (Summer 2014) …
By the end of the course you should know the following: • Basic usage of the R language for data analysis. • Basic understanding of the logic of significance testing and hypothesis testing. • …
BSDA: Basic Statistics and Data Analysis - The …
Title Basic Statistics and Data Analysis Version 1.2.2 Date 2023-09-14 LazyData yes Maintainer Alan T. Arnholt Description Data sets for book ``Basic Statistics and …
Introduction to Statistical Analysis - Flinders University
Introduction to Statistical Analysis_2018_02_21.ppt [Compatibility Mode] Please note that the workshop is aimed to be a brief introduction to the topic and this PowerPoint is primarily …
CHAPTER 8 INTRODUCTION TO STATISTICAL ANALYSIS
Statistics refers to a variety of processes that deal with data 1 . In particular, statistics includes the collection, assembly, classification, summarization, presentation, and analysis of data. Analysis …
STATISTICS and DATA ANALYSIS - New York University
The course COR1-GB.1305 is designed to achieve an understanding of fundamental notions of data presentation and analysis and to use statistical thinking in the context of business problems.
Basics of Statistics
Statistics consists of a body of methods for col-lecting and analyzing data. (Agresti & Finlay, 1997) From above, it should be clear that statistics is much more than just the tabu-lation of numbers …
Use of Basic Statistics UNIT 4 OVERVIEW OF STATISTICAL
Statistics is a science that deals with the collection, organization, analysis, interpretation and presentation of information that can be presented numerically and/or graphically to help us …
A Really Simple Guide to Quantitative Data Analysis
The best kind of quantitative data in statistical analysis is numerical, followed by ordinal, and lastly nominal. It is important to know what kind of data you are planning to collect or analyse as this …
Basic Statistics Terms and Calculations - Metropolitan …
In the study of statistics, it is important to be familiar with a variety of terms. Note: There are certain terms used in statistical analysis that only apply to Quantitative Variables.
Introduction to Data Analysis - UNESCO
Organize and present data in a table . Calculate percentages, ratios and indicators . Create graph or chart to illustrate the data. STEP 1.
