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Statistics: 1001 Practice Problems For Dummies (+ Free Online Practice)
by The Experts at DummiesBecome more likely to succeed—gain stats mastery with Dummies Statistics: 1001 Practice Problems For Dummies gives you 1,001 opportunities to practice solving problems from all the major topics covered in Statistics classes—in the book and online! Get extra help with tricky subjects, solidify what you’ve already learned, and get in-depth walk-throughs for every problem with this useful book. These practice problems and detailed answer explanations will help you gain a valuable working knowledge of statistics, no matter what your skill level. Thanks to Dummies, you have a resource to help you put key stats concepts into practice. Work through practice problems on all Statistics topics covered in school classes Read through detailed explanations of the answers to build your understanding Access practice questions online to study anywhere, any time Improve your grade and up your study game with practice, practice, practiceThe material presented in Statistics: 1001 Practice Problems For Dummies is an excellent resource for students, as well as parents and tutors looking to help supplement Statistics instruction. Statistics: 1001 Practice Problems For Dummies (9781119883593) was previously published as 1,001 Statistics Practice Problems For Dummies (9781118776049). While this version features a new Dummies cover and design, the content is the same as the prior release and should not be considered a new or updated product.
Statistics 101: From Data Analysis and Predictive Modeling to Measuring Distribution and Determining Probability, Your Essential Guide to Statistics (Adams 101)
by David BormanA 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.
Statistics, 3E (Idiot's Guides)
by Robert A. Donnelly Fatma Abdel-RaoufStatistics is a class that is required in many college majors, and it&’s an increasingly popular Advanced Placement (AP) high school course. In addition to math and technical students, many business and liberal arts students are required to take it as a fundamental component of their majors. A knowledge of statistical interpretation is vital for many careers. Idiot&’s Guides®: Statistics explains the fundamental tenets in language anyone can understand. Content includes: - Calculating descriptive statistics. - Measures of central tendency: mean, median, and mode. - Probability. - Variance analysis. - Inferential statistics. - Hypothesis testing. - Organizing data into statistical charts and tables.
Statistics and Analysis of Scientific Data
by Massimiliano BonamenteStatistics and Analysis of Scientific Data covers the foundations of probability theory and statistics, and a number of numerical and analytical methods that are essential for the present-day analyst of scientific data. Topics covered include probability theory, distribution functions of statistics, fits to two-dimensional datasheets and parameter estimation, Monte Carlo methods and Markov chains. Equal attention is paid to the theory and its practical application, and results from classic experiments in various fields are used to illustrate the importance of statistics in the analysis of scientific data. The main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and proactive use of the material for practical applications. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data, as well as exercises and examples to aid the students' understanding of the topic.
Statistics and Analysis of Scientific Data
by Massimiliano BonamenteThe revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains. Features new to this edition include: * a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets. * a new chapter on the various measures of the mean including logarithmic averages. * new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors. * a new case study and additional worked examples. * mathematical derivations and theoretical background material have been appropriately marked, to improve the readability of the text. * end-of-chapter summary boxes, for easy reference. As in the first edition, the main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the material. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data, as well as exercises and examples to aid the readers' understanding of the topic.
The Statistics and Calculus with Python Workshop: A comprehensive introduction to mathematics in Python for artificial intelligence applications
by Peter Farrell Alvaro Fuentes Quan Nguyen Ajinkya Sudhir Kolhe Alexander Joseph Sarver Marios TsatsosWith examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easyKey FeaturesDiscover how most programmers use the main Python libraries when performing statistics with PythonUse descriptive statistics and visualizations to answer business and scientific questionsSolve complicated calculus problems, such as arc length and solids of revolution using derivatives and integralsBook DescriptionAre you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python.The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions.By the end of this book, you'll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.What you will learnGet to grips with the fundamental mathematical functions in PythonPerform calculations on tabular datasets using pandasUnderstand the differences between polynomials, rational functions, exponential functions, and trigonometric functionsUse algebra techniques for solving systems of equationsSolve real-world problems with probabilitySolve optimization problems with derivatives and integralsWho this book is forIf you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Python.
Statistics and Causality: Methods for Applied Empirical Research
by Wolfgang Wiedermann Alexander Von EyeA one-of-a-kind guide to identifying and dealing with modern statistical developments in causality Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses. The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes: * New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories * End-of-chapter bibliographies that provide references for further discussions and additional research topics * Discussions on the use and applicability of software when appropriate Statistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic. Wolfgang Wiedermann, PhD, is Assistant Professor in the Department of Educational, School, and Counseling Psychology at the University of Missouri, Columbia. His research interests include the development of methods for direction dependence analysis and causal inference, the development and evaluation of methods for person-oriented research, and methods for intensive longitudinal data. Alexander von Eye, PhD, is Professor Emeritus of Psychology at Michigan State University. His research interests include statistical methods, categorical data analysis, and human development. Dr. von Eye is Section Editor for the Encyclopedia of Statistics in Behavioral Science and is the coauthor of Log-Linear Modeling: Concepts, Interpretation, and Application, both published by Wiley.
Statistics and Data Analysis for Engineers and Scientists (Transactions on Computer Systems and Networks)
by Tanvir Mustafy Md. Tauhid RahmanThis textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level. It aims to combine the data analysis methods using three different programs—Microsoft Excel, SPSS, and MATLAB. The book combining the different data analysis tools is a unique approach. The book presents a variety of real-life problems in data analysis and machine learning, delivering the best solution. Analysis methods presented in this book include but are not limited to, performing various algebraic and trigonometric operations, regression modeling, and correlation, as well as plotting graphs and charts to represent the results. Fundamental concepts of applied statistics are also explained here, with illustrative examples. Thus, this book presents a pioneering solution to help a wide range of students, researchers, and professionals learn data processing, interpret different findings derived from the analyses, and apply them to their research or professional fields. The book also includes worked examples of practical problems. The primary focus behind designing these examples is understanding the concepts of data analysis and how it can solve problems. The chapters include practice exercises to assist users in enhancing their skills to execute statistical analysis calculations using software instead of relying on tables for probabilities and percentiles in the present world.
Statistics and Data Analysis for Social Science
by Eric Jon KriegStatistics and Data Analysis for Social Science helps students to build a strong foundational understanding of statistics by providing clarity around when and why statistics useful. Rather than focusing on the “how to” of statistics, author Eric J. Krieg simplifies the complexity of statistical calculations by introducing only what is necessary to understanding each concept. Every chapter is written around and applied to a different social problem or issues—enabling students to broaden their imagination about the statistical “tools” that can be used to make sense of our world and, maybe, to make the world a better place.
Statistics and Data Analysis for Social Science
by Eric Jon KriegStatistics and Data Analysis for Social Science helps students to build a strong foundational understanding of statistics by providing clarity around when and why statistics useful. Rather than focusing on the “how to” of statistics, author Eric J. Krieg simplifies the complexity of statistical calculations by introducing only what is necessary to understanding each concept. Every chapter is written around and applied to a different social problem or issues—enabling students to broaden their imagination about the statistical “tools” that can be used to make sense of our world and, maybe, to make the world a better place.
Statistics and Data Science: Research School on Statistics and Data Science, RSSDS 2019, Melbourne, VIC, Australia, July 24–26, 2019, Proceedings (Communications in Computer and Information Science #1150)
by Hien NguyenThis book constitutes the proceedings of the Research School on Statistics and Data Science, RSSDS 2019, held in Melbourne, VIC, Australia, in July 2019. The 11 papers presented in this book were carefully reviewed and selected from 23 submissions. The volume also contains 7 invited talks. The workshop brought together academics, researchers, and industry practitioners of statistics and data science, to discuss numerous advances in the disciplines and their impact on the sciences and society. The topics covered are data analysis, data science, data mining, data visualization, bioinformatics, machine learning, neural networks, statistics, and probability.
Statistics and Data Visualisation with Python (Chapman & Hall/CRC The Python Series)
by Jesus Rogel-SalazarThis book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.
Statistics and Data Visualization Using R: The Art and Practice of Data Analysis
by David S. BrownDesigned to introduce students to quantitative methods in a way that can be applied to all kinds of data in all kinds of situations, Statistics and Data Visualization Using R: The Art and Practice of Data Analysis by David S. Brown teaches students statistics through charts, graphs, and displays of data that help students develop intuition around statistics as well as data visualization skills. By focusing on the visual nature of statistics instead of mathematical proofs and derivations, students can see the relationships between variables that are the foundation of quantitative analysis. Using the latest tools in R and R RStudio® for calculations and data visualization, students learn valuable skills they can take with them into a variety of future careers in the public sector, the private sector, or academia. Starting at the most basic introduction to data and going through most crucial statistical methods, this introductory textbook quickly gets students new to statistics up to speed running analyses and interpreting data from social science research.
Statistics and Data Visualization Using R: The Art and Practice of Data Analysis
by David S. BrownDesigned to introduce students to quantitative methods in a way that can be applied to all kinds of data in all kinds of situations, Statistics and Data Visualization Using R: The Art and Practice of Data Analysis by David S. Brown teaches students statistics through charts, graphs, and displays of data that help students develop intuition around statistics as well as data visualization skills. By focusing on the visual nature of statistics instead of mathematical proofs and derivations, students can see the relationships between variables that are the foundation of quantitative analysis. Using the latest tools in R and R RStudio® for calculations and data visualization, students learn valuable skills they can take with them into a variety of future careers in the public sector, the private sector, or academia. Starting at the most basic introduction to data and going through most crucial statistical methods, this introductory textbook quickly gets students new to statistics up to speed running analyses and interpreting data from social science research.
Statistics and Decisions: An Introduction to Foundations
by S. H. KimThis book provides the necessary prerequisites in probability and statistics as well as the key ideas in decision theory. It is helpful to students and practitioners who desire to apply decision-theoretic thinking to their own work.
Statistics and Health Care Fraud: How to Save Billions (ASA-CRC Series on Statistical Reasoning in Science and Society)
by Tahir EkinStatistics and Health Care Fraud: How to Save Billions helps the public to become more informed citizens through discussions of real world health care examples and fraud assessment applications. The author presents statistical and analytical methods used in health care fraud audits without requiring any mathematical background. The public suffers from health care overpayments either directly as patients or indirectly as taxpayers, and fraud analytics provides ways to handle the large size and complexity of these claims. The book starts with a brief overview of global healthcare systems such as U.S. Medicare. This is followed by a discussion of medical overpayments and assessment initiatives using a variety of real world examples. The book covers subjects as: • Description and visualization of medical claims data • Prediction of fraudulent transactions • Detection of excessive billings • Revealing new fraud patterns • Challenges and opportunities with health care fraud analytics Dr. Tahir Ekin is the Brandon Dee Roberts Associate Professor of Quantitative Methods in McCoy College of Business, Texas State University. His previous work experience includes a working as a statistician on health care fraud detection. His scholarly work on health care fraud has been published in a variety of academic journals including International Statistical Review, The American Statistician, and Applied Stochastic Models in Business and Industry. He is a recipient of the Texas State University 2018 Presidential Distinction Award in Scholar Activities and the ASA/NISS y-Bis 2016 Best Paper Awards. He has developed and taught courses in the areas of business statistics, optimization, data mining and analytics. Dr. Ekin also serves as Vice President of the International Society for Business and Industrial Statistics.
Statistics and its Applications: Platinum Jubilee Conference, Kolkata, India, December 2016 (Springer Proceedings in Mathematics & Statistics #244)
by Asis Kumar Chattopadhyay Gaurangadeb ChattopadhyayThis book discusses recent developments and the latest research in statistics and its applications, primarily in agriculture and industry, survey sampling and biostatistics, gathering articles on a wide variety of topics. Written by leading academics, scientists, researchers and scholars from around the globe to mark the platinum jubilee of the Department of Statistics, University of Calcutta in 2016, the book is a valuable resource for statisticians, aspiring researchers and professionals across educational levels and disciplines.
Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics (Chapman & Hall/CRC Healthcare Informatics Series)
by Hulin Wu Jose-Miguel Yamal Ashraf Yaseen Vahed MaroufyThe use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.
Statistics and Measurement Concepts with OpenStat
by William MillerThis reference manual for the OpenStat software, an open-source software developed by William Miller, covers a broad spectrum of statistical methods and techniques. A unique feature is its compatibility with many other statistical programs. OpenStat users are researchers and students in the social sciences, education, or psychology, who benefit from the hands on approach to Statistics. During and upon completion of courses in Statistics or measurement, students and future researchers need a low cost computer program available to them, and OpenStat fills this void. The software is used in Statistics courses around the world with over 50,000 downloads per year. The manual covers all functions of the OpenStat software, including measurement, ANOVAS, regression analyses, simulations, product-moment and partial correlations, and logistic regression. The manual is an important learning tool that explains the Statistics behind the many analyses possible with the program and demonstrates these analyses.
Statistics and Probability Theory
by Michael Havbro FaberThis book provides the reader with the basic skills and tools of statistics and probability in the context of engineering modeling and analysis. The emphasis is on the application and the reasoning behind the application of these skills and tools for the purpose of enhancing decision making in engineering. The purpose of the book is to ensure that the reader will acquire the required theoretical basis and technical skills such as to feel comfortable with the theory of basic statistics and probability. Moreover, in this book, as opposed to many standard books on the same subject, the perspective is to focus on the use of the theory for the purpose of engineering model building and decision making. This work is suitable for readers with little or no prior knowledge on the subject of statistics and probability.
Statistics and Probability with Applications for Engineers and Scientists
by Bhisham C. Gupta Irwin GuttmanIntroducing the tools of statistics and probability from the ground upAn understanding of statistical tools is essential for engineers and scientists who often need to deal with data analysis over the course of their work. Statistics and Probability with Applications for Engineers and Scientists walks readers through a wide range of popular statistical techniques, explaining step-by-step how to generate, analyze, and interpret data for diverse applications in engineering and the natural sciences.Unique among books of this kind, Statistics and Probability with Applications for Engineers and Scientists covers descriptive statistics first, then goes on to discuss the fundamentals of probability theory. Along with case studies, examples, and real-world data sets, the book incorporates clear instructions on how to use the statistical packages Minitab® and Microsoft® Office Excel® to analyze various data sets. The book also features:* Detailed discussions on sampling distributions, statistical estimation of population parameters, hypothesis testing, reliability theory, statistical quality control including Phase I and Phase II control charts, and process capability indices* A clear presentation of nonparametric methods and simple and multiple linear regression methods, as well as a brief discussion on logistic regression method* Comprehensive guidance on the design of experiments, including randomized block designs, one- and two-way layout designs, Latin square designs, random effects and mixed effects models, factorial and fractional factorial designs, and response surface methodology* A companion website containing data sets for Minitab and Microsoft Office Excel, as well as JMP ® routines and resultsAssuming no background in probability and statistics, Statistics and Probability with Applications for Engineers and Scientists features a unique, yet tried-and-true, approach that is ideal for all undergraduate students as well as statistical practitioners who analyze and illustrate real-world data in engineering and the natural sciences.
Statistics and Probability with Applications for Engineers and Scientists Using MINITAB, R and JMP
by Bhisham C. Gupta Irwin Guttman Kalanka P. JayalathIntroduces basic concepts in probability and statistics to data science students, as well as engineers and scientists Aimed at undergraduate/graduate-level engineering and natural science students, this timely, fully updated edition of a popular book on statistics and probability shows how real-world problems can be solved using statistical concepts. It removes Excel exhibits and replaces them with R software throughout, and updates both MINITAB and JMP software instructions and content. A new chapter discussing data mining—including big data, classification, machine learning, and visualization—is featured. Another new chapter covers cluster analysis methodologies in hierarchical, nonhierarchical, and model based clustering. The book also offers a chapter on Response Surfaces that previously appeared on the book’s companion website. Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP, Second Edition is broken into two parts. Part I covers topics such as: describing data graphically and numerically, elements of probability, discrete and continuous random variables and their probability distributions, distribution functions of random variables, sampling distributions, estimation of population parameters and hypothesis testing. Part II covers: elements of reliability theory, data mining, cluster analysis, analysis of categorical data, nonparametric tests, simple and multiple linear regression analysis, analysis of variance, factorial designs, response surfaces, and statistical quality control (SQC) including phase I and phase II control charts. The appendices contain statistical tables and charts and answers to selected problems. Features two new chapters—one on Data Mining and another on Cluster Analysis Now contains R exhibits including code, graphical display, and some results MINITAB and JMP have been updated to their latest versions Emphasizes the p-value approach and includes related practical interpretations Offers a more applied statistical focus, and features modified examples to better exhibit statistical concepts Supplemented with an Instructor's-only solutions manual on a book’s companion website Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP is an excellent text for graduate level data science students, and engineers and scientists. It is also an ideal introduction to applied statistics and probability for undergraduate students in engineering and the natural sciences.
Statistics and Probability with Applications (High School)
by Daren S. Starnes Josh Tabor Luke WilcoxThis engaging and modern introduction to statistics helps prepare students for success in this course and in life. Structured into bite-sized lessons with many integrated activities to get students �doing statistics� from the start, this program helps students understand the �why� and �how� of statistics.
Statistics and Probability with Applications (High School)
by Josh Tabor Daren S. StarnesStatistics and Probability with Applications, Third Edition is the only introductory statistics text written by high school teachers for high school teachers and students. Daren Starnes, Josh Tabor, and the extended team of contributors bring their in-depth understanding of statistics and the challenges faced by high school students and teachers to development of the text and its accompanying suite of print and interactive resources for learning and instruction. A complete re-envisioning of the authors’ Statistics Through Applications, this new text covers the core content for the course in a series of brief, manageable lessons, making it easy for students and teachers to stay on pace. Throughout, new pedagogical tools and lively real-life examples help captivate students and prepare them to use statistics in college courses and in any career.