Download or read book Correlation and Regression Analysis written by Thomas J. Archdeacon and published by Univ of Wisconsin Press. This book was released on 1994 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: A blueprint for historians to understand and evaluate the variables and discusses the fundamentals of regression analysis. 2 looks at procedures for assessing the level of association among diagnostic methods for identifying and correcting shortcomings Finally, part 3 presents more advanced topics, including in regression models. quantitative analyses they're likely to encounter in journal literature and monographs on research in the social sciences. ignore the fact that most historians have little background in mathematics would be folly, to decipher equations and follow their logic. Concepts are introduced carefully, and the operation of equations is explained step by step. Annotation copyright by Book News, Inc., Portland, OR

Download or read book Text Book of Correlations and Regression written by A.K. Sharma and published by Discovery Publishing House. This book was released on 2005 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book Correlation and Regression is an outcome of authors long teaching experience of the subject. This book present a thorough treatment of what is required for the students of B.A/B.Sc., of all Indian Universities. It includes fundamental concepts, illustrated examples and application to various problems. These illustrative examples have been selected carefully on such topic and sufficient number of unsolved questions are provided which aims at sharpening the skill of students. Contents: Correlation Analysis, Regression Analysis, Partial and Multiple Correlation.

Download or read book Correlation and Regression written by Philip Bobko and published by SAGE Publications. This book was released on 2001-04-10 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: ". . . the writing makes this book interesting to all levels of students. Bobko tackles tough issues in an easy way but provides references for more complex and complete treatment of the subject. . . . there is a familiarity and love of the material that radiates through the words." --Malcolm James Ree, ORGANIZATIONAL RESEARCH METHODS, April 2002 "This book provides one of the clearest treatments of correlations and regression of any statistics book I have seen. . . . Bobko has achieved his objective of making the topics of correlation and regression accessible to students. . . . For someone looking for a very clearly written treatment of applied correlation and regression, this book would be an excellent choice." --Paul E. Spector, University of South Florida "As a quantitative methods instructor, I have reviewed and used many statistical textbooks. This textbook and approach is one of the very best when it comes to user-friendliness, approachability, clarity, and practical utility." --Steven G. Rogelberg, Bowling Green State University Building on the classical examples in the first edition, this updated edition provides students with an accessible textbook on statistical theories in correlation and regression. Taking an applied approach, the author uses concrete examples to help the student thoroughly understand how statistical techniques work and how to creatively apply them based on specific circumstances they face in the "real world." The author uses a layered approach in each chapter, first offering the student an intuitive understanding of the problems or examples and progressing through to the underlying statistics. This layered approach and the applied examples provide students with the foundation and reasoning behind each technique, so they will be able to use their own judgement to effectively choose from the alternative data analytic options.

Download or read book Applying Regression and Correlation written by Jeremy Miles and published by SAGE. This book was released on 2001-03-08 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Takes a look at applying regression analysis in the behavioural sciences by introducing the reader to regression analysis through a simple model-building approach.

Download or read book Machine Learning and Big Data written by Uma N. Dulhare and published by John Wiley & Sons. This book was released on 2020-09-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.

Download or read book Applied Multiple Regression Correlation Analysis for the Behavioral Sciences written by Patricia Cohen and published by Psychology Press. This book was released on 2014-04-04 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT. Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

Download or read book Interview Questions in Business Analytics written by Bhasker Gupta and published by Apress. This book was released on 2016-08-04 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover relevant questions—and detailed answers—to help you prepare for job interviews and break into the field of analytics. This book contains more than 200 questions based on consultations with hiring managers and technical professionals already working in analytics. Interview Questions in Business Analytics: How to Ace Interviews and Get the Job You Want fills a gap in information on business analytics for job seekers. Bhasker Gupta, the founder and editor of Analytics India Magazine, has come up with more than 200 questions job applicants are likely to face in an interview. Covering data preparation, statistics, analytics implementation, as well as other crucial topics favored by interviewers, this book: Provides 200+ interview questions often asked by recruiters and hiring managers in global corporations Offers short and to-the-point answers to the depth required, while looking at the problem from all angles Provides a full range of interview questions for jobs ranging from junior analytics to senior data scientists and managers Offers analytics professionals a quick reference on topics in analytics Using a question-and-answer format from start to finish, Interview Questions in Business Analytics: How to Ace Interviews and Get the Job You Want will help you grasp concepts sooner and with deep clarity. The book therefore also serves as a primer on analytics and covers issues relating to business implementation. You will learn about not just the how and what of analytics, but also the why and when. This book will thus ensure that you are well prepared for interviews—putting your dream job well within reach. Business analytics is currently one of the hottest and trendiest areas for technical professionals. With the rise of the profession, there is significant job growth. Even so, it’s not easy to get a job in the field, because you need knowledge of subjects such as statistics, databases, and IT services. Candidates must also possess keen business acumen. What's more, employers cast a cold critical eye on all applicants, making the task of getting a job even more difficult. What You'll Learn The 200 questions in this book cover such topics as: • The different types of data used in analytics • How analytics are put to use in different industries • The process of hypothesis testing • Predictive vs. descriptive analytics • Correlation, regression, segmentation and advanced statistics • Predictive modeling Who This Book Is For Those aspiring to jobs in business analytics, including recent graduates and technical professionals looking for a new or better job. Job interviewers will also find the book helpful in preparing interview questions.

Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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

Download or read book Time Series and Statistics written by John Eatwell and published by Palgrave Macmillan. This book was released on 1990-07-23 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistics from A to Z written by Andrew A. Jawlik and published by John Wiley & Sons. This book was released on 2016-09-16 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics is confusing, even for smart, technically competent people. And many students and professionals find that existing books and web resources don’t give them an intuitive understanding of confusing statistical concepts. That is why this book is needed. Some of the unique qualities of this book are: • Easy to Understand: Uses unique “graphics that teach” such as concept flow diagrams, compare-and-contrast tables, and even cartoons to enhance “rememberability.” • Easy to Use: Alphabetically arranged, like a mini-encyclopedia, for easy lookup on the job, while studying, or during an open-book exam. • Wider Scope: Covers Statistics I and Statistics II and Six Sigma Black Belt, adding such topics as control charts and statistical process control, process capability analysis, and design of experiments. As a result, this book will be useful for business professionals and industrial engineers in addition to students and professionals in the social and physical sciences. In addition, each of the 60+ concepts is covered in one or more articles. The 75 articles in the book are usually 5–7 pages long, ensuring that things are presented in “bite-sized chunks.” The first page of each article typically lists five “Keys to Understanding” which tell the reader everything they need to know on one page. This book also contains an article on “Which Statistical Tool to Use to Solve Some Common Problems”, additional “Which to Use When” articles on Control Charts, Distributions, and Charts/Graphs/Plots, as well as articles explaining how different concepts work together (e.g., how Alpha, p, Critical Value, and Test Statistic interrelate). ANDREW A. JAWLIK received his B.S. in Mathematics and his M.S. in Mathematics and Computer Science from the University of Michigan. He held jobs with IBM in marketing, sales, finance, and information technology, as well as a position as Process Executive. In these jobs, he learned how to communicate difficult technical concepts in easy - to - understand terms. He completed Lean Six Sigma Black Belt coursework at the IASSC - accredited Pyzdek Institute. In order to understand the confusing statistics involved, he wrote explanations in his own words and graphics. Using this material, he passed the certification exam with a perfect score. Those statistical explanations then became the starting point for this book.

Download or read book Regression Analysis with R written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2018-01-31 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build effective regression models in R to extract valuable insights from real data Key Features Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values From Simple Linear Regression to Logistic Regression - this book covers all regression techniques and their implementation in R A complete guide to building effective regression models in R and interpreting results from them to make valuable predictions Book Description Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples. By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects. What you will learn Get started with the journey of data science using Simple linear regression Deal with interaction, collinearity and other problems using multiple linear regression Understand diagnostics and what to do if the assumptions fail with proper analysis Load your dataset, treat missing values, and plot relationships with exploratory data analysis Develop a perfect model keeping overfitting, under-fitting, and cross-validation into consideration Deal with classification problems by applying Logistic regression Explore other regression techniques – Decision trees, Bagging, and Boosting techniques Learn by getting it all in action with the help of a real world case study. Who this book is for This book is intended for budding data scientists and data analysts who want to implement regression analysis techniques using R. If you are interested in statistics, data science, machine learning and wants to get an easy introduction to the topic, then this book is what you need! Basic understanding of statistics and math will help you to get the most out of the book. Some programming experience with R will also be helpful

Download or read book Statistical Inference via Data Science A ModernDive into R and the Tidyverse written by Chester Ismay and published by CRC Press. This book was released on 2019-12-23 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.

Download or read book Essential Medical Statistics written by Betty R. Kirkwood and published by John Wiley & Sons. This book was released on 2010-09-16 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: Blackwell Publishing is delighted to announce that this book hasbeen Highly Commended in the 2004 BMA Medical Book Competition.Here is the judges' summary of this book: "This is a technical book on a technical subject but presentedin a delightful way. There are many books on statistics for doctorsbut there are few that are excellent and this is certainly one ofthem. Statistics is not an easy subject to teach or write about.The authors have succeeded in producing a book that is as good asit can get. For the keen student who does not want a book formathematicians, this is an excellent first book on medicalstatistics." Essential Medical Statistics is a classic amongst medicalstatisticians. An introductory textbook, it presents statisticswith a clarity and logic that demystifies the subject, whileproviding a comprehensive coverage of advanced as well as basicmethods. The second edition of Essential Medical Statistics hasbeen comprehensively revised and updated to include modernstatistical methods and modern approaches to statistical analysis,while retaining the approachable and non-mathematical style of thefirst edition. The book now includes full coverage of the mostcommonly used regression models, multiple linear regression,logistic regression, Poisson regression and Cox regression, as wellas a chapter on general issues in regression modelling. Inaddition, new chapters introduce more advanced topics such asmeta-analysis, likelihood, bootstrapping and robust standarderrors, and analysis of clustered data. Aimed at students of medical statistics, medical researchers,public health practitioners and practising clinicians usingstatistics in their daily work, the book is designed as both ateaching and a reference text. The format of the book is clear withhighlighted formulae and worked examples, so that all concepts arepresented in a simple, practical and easy-to-understand way. Thesecond edition enhances the emphasis on choice of appropriatemethods with new chapters on strategies for analysis and measuresof association and impact. Essential Medical Statistics is supported by a web siteat www.blackwellpublishing.com/essentialmedstats. Thisuseful online resource provides statistical datasets to download,as well as sample chapters and future updates.

Download or read book Introductory Business Statistics hardcover Full Color written by Alexander Holmes and published by . This book was released on 2023-06-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Printed in color. Introductory Business Statistics is designed to meet the scope and sequence requirements of the one-semester statistics course for business, economics, and related majors. Core statistical concepts and skills have been augmented with practical business examples, scenarios, and exercises. The result is a meaningful understanding of the discipline, which will serve students in their business careers and real-world experiences.

Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Introductory Statistics written by Douglas S. Shafer and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: