*A Second Course in Statistics*

**Author**: Frederick Mosteller

**Publisher:**Pearson College Division

**ISBN:**N.A

**Category:**Mathematics

**Page:**588

**View:**7975

Skip to content
# Search Results for: data-analysis-and-regression

*A Second Course in Statistics*

**Author**: Frederick Mosteller

**Publisher:** Pearson College Division

**ISBN:** N.A

**Category:** Mathematics

**Page:** 588

**View:** 7975

Approaching data analysis; Indication and indicators; Displays and summaries for batches; Straightening curves and plots; The practice of re-expression; Need we re-express? Hunting out the real uncertainty; A method of direct assessment; Two-and more-way tables; Robust and resistant measures; Standardizing for comparison; Regression for fitting; Woes of regression coefficients; A class of mechanisms for fitting; Guided regression; Examining regression residuals.

**Author**: Andrew Gelman,Jennifer Hill

**Publisher:** Cambridge University Press

**ISBN:** 9780521686891

**Category:** Mathematics

**Page:** 625

**View:** 6380

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
*A Data-Oriented Approach*

**Author**: Richard F. Gunst

**Publisher:** Routledge

**ISBN:** 1351419293

**Category:** Mathematics

**Page:** 424

**View:** 7951

Regression Analysis and Its Application: A Data-Oriented Approach answers the need for researchers and students who would like a better understanding of classical regression analysis. Useful either as a textbook or as a reference source, this book bridges the gap between the purely theoretical coverage of regression analysis and its practical application. The book presents regression analysis in the general context of data analysis. Using a teach-by-example format, it contains ten major data sets along with several smaller ones to illustrate the common characteristics of regression data and properties of statistics that are employed in regression analysis. The book covers model misspecification, residual analysis, multicollinearity, and biased regression estimators. It also focuses on data collection, model assumptions, and the interpretation of parameter estimates.Complete with an extensive bibliography, Regression Analysis and Its Application is suitable for statisticians, graduate and upper-level undergraduate students, and research scientists in biometry, business, ecology, economics, education, engineering, mathematics, physical sciences, psychology, and sociology. In addition, data collection agencies in the government and private sector will benefit from the book.
*Design and develop statistical nodes to identify unique relationships within data at scale*

**Author**: Giuseppe Ciaburro

**Publisher:** Packt Publishing Ltd

**ISBN:** 1788622707

**Category:** Computers

**Page:** 422

**View:** 823

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 1. Get started with the journey of data science using Simple linear regression 2. Deal with interaction, collinearity and other problems using multiple linear regression 3. Understand diagnostics and what to do if the assumptions fail with proper analysis 4. Load your dataset, treat missing values, and plot relationships with exploratory data analysis 5. Develop a perfect model keeping overfitting, under-fitting, and cross-validation into consideration 6. Deal with classification problems by applying Logistic regression 7. Explore other regression techniques – Decision trees, Bagging, and Boosting techniques 8. 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
*A Historian's Guide*

**Author**: Thomas J. Archdeacon

**Publisher:** Univ of Wisconsin Press

**ISBN:** 9780299136543

**Category:** History

**Page:** 352

**View:** 1234

In Correlation and Regression Analysis: A Historian's Guide Thomas J. Archdeacon provides historians with a practical introduction to the use of correlation and regression analysis. The book concentrates on the kinds of analysis that form the broad range of statistical methods used in the social sciences. It enables historians to understand and to evaluate critically the quantitative analyses that they are likely to encounter in journal literature and monographs reporting research findings in the social sciences. Without attempting to be a text in basic statistics, the book provides enough background information to allow readers to grasp the essentials of correlation and regression. Correlation analysis refers to the measurement of association between or among variables, and regression analysis focuses primarily on the use of linear models to predict changes in the value taken by one variable in terms of changes in the values of a set of explanatory variables. The book also discusses diagnostic methods for identifying shortcomings in regression models, the use of regression to analyze causation, and the application of regression and related procedures to the study of problems containing categorical as well as numerical data. Archdeacon asserts that knowing how statistical procedures are computed can clarify the theoretical structures underlying them and is essential for recognizing the conditions under which their use is appropriate. The book does not shy away from the mathematics of statistical analysis; but Archdeacon presents concepts carefully and explains the operation of equations step by step. Unlike many works in the field, the book does not assume that readers have mathematical training beyond basic algebra and geometry. In the hope of promoting the role of quantitative analysis in his discipline, Archdeacon discusses the theory and methods behind the most important interpretive paradigm for quantitative research in the social sciences. Correlation and Regression Analysis introduces statistical techniques that are indispensable to historians and enhances the presentation of them with practical examples from scholarly works.
*Concepts, Applications, and Implementation*

**Author**: Richard B. Darlington,Andrew F. Hayes

**Publisher:** Guilford Publications

**ISBN:** 1462521134

**Category:** Social Science

**Page:** 661

**View:** 2706

Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable?s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

**Author**: Alan Anderson

**Publisher:** John Wiley & Sons

**ISBN:** 1118940024

**Category:** Computers

**Page:** 384

**View:** 5155

The fast and easy way to make sense of statistics for big data Does the subject of data analysis make you dizzy? You've come to the right place! Statistics For Big Data For Dummies breaks this often-overwhelming subject down into easily digestible parts, offering new and aspiring data analysts the foundation they need to be successful in the field. Inside, you'll find an easy-to-follow introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages, plain-English explanations of how to make sense of data in the real world, and much more. Data has never been easier to come by, and the tools students and professionals need to enter the world of big data are based on applied statistics. While the word "statistics" alone can evoke feelings of anxiety in even the most confident student or professional, it doesn't have to. Written in the familiar and friendly tone that has defined the For Dummies brand for more than twenty years, Statistics For Big Data For Dummies takes the intimidation out of the subject, offering clear explanations and tons of step-by-step instruction to help you make sense of data mining—without losing your cool. Helps you to identify valid, useful, and understandable patterns in data Provides guidance on extracting previously unknown information from large databases Shows you how to discover patterns available in big data Gives you access to the latest tools and techniques for working in big data If you're a student enrolled in a related Applied Statistics course or a professional looking to expand your skillset, Statistics For Big Data For Dummies gives you access to everything you need to succeed.

**Author**: Samprit Chatterjee,Ali S. Hadi

**Publisher:** John Wiley & Sons

**ISBN:** 0470055456

**Category:** Mathematics

**Page:** 416

**View:** 7791

The essentials of regression analysis through practical applications Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. This new edition features the following enhancements: Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis A new chapter entitled Further Topics discusses advanced areas of regression analysis Reorganized, expanded, and upgraded exercises appear at the end of each chapter A fully integrated Web page provides data sets Numerous graphical displays highlight the significance of visual appeal Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

**Author**: Rudolf J. Freund,William J. Wilson,Ping Sa

**Publisher:** Elsevier

**ISBN:** 0080522971

**Category:** Mathematics

**Page:** 480

**View:** 5134

Regression Analysis provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design. Examples and exercises contain real data and graphical illustration for ease of interpretation Outputs from SAS 7, SPSS 7, Excel, and Minitab are used for illustration, but any major statistical software package will work equally well

**Author**: Steven M. Sidik

**Publisher:** N.A

**ISBN:** N.A

**Category:** Least squares

**Page:** 94

**View:** 6379

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.
*Mixed-Effects Modeling Approaches*

**Author**: Hulin Wu,Jin-Ting Zhang

**Publisher:** John Wiley & Sons

**ISBN:** 0470009667

**Category:** Mathematics

**Page:** 384

**View:** 3489

Incorporates mixed-effects modeling techniques for more powerful and efficient methods This book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented. With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques. The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis. Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices. With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems.

**Author**: A. Colin Cameron,Pravin K. Trivedi

**Publisher:** Cambridge University Press

**ISBN:** 1107014166

**Category:** Business & Economics

**Page:** 596

**View:** 3682

This book provides the most comprehensive and up-to-date account of regression methods to explain the frequency of events.

**Author**: Chandan Mukherjee,Howard White,Marc Wuyts

**Publisher:** Routledge

**ISBN:** 1136144609

**Category:** Business & Economics

**Page:** 520

**View:** 6509

Getting accurate data on less developed countries has created great problems for studying these areas. Yet until recently students of development economics have relied on standard econometrics texts, which assume a Western context. Econometrics and Data Analysis for Developing Countries solves this problem. It will be essential reading for all advanced students of development economics.
*Regression and Experimental Design*

**Author**: J.D. Jobson

**Publisher:** Springer Science & Business Media

**ISBN:** 1461209552

**Category:** Mathematics

**Page:** 622

**View:** 6143

An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.
*Applied Statistical Methods*

**Author**: Ronald Christensen

**Publisher:** CRC Press

**ISBN:** 9780412062919

**Category:** Mathematics

**Page:** 608

**View:** 5871

This text presents a comprehensive treatment of basic statistical methods and their applications. It focuses on the analysis of variance and regression, but also addressing basic ideas in experimental design and count data. The book has four connecting themes: similarity of inferential procedures, balanced one-way analysis of variance, comparison of models, and checking assumptions. Most inferential procedures are based on identifying a scalar parameter of interest, estimating that parameter, obtaining the standard error of the estimate, and identifying the appropriate reference distribution. Given these items, the inferential procedures are identical for various parameters. Balanced one-way analysis of variance has a simple, intuitive interpretation in terms of comparing the sample variance of the group means with the mean of the sample variance for each group. All balanced analysis of variance problems are considered in terms of computing sample variances for various group means. Comparing different models provides a structure for examining both balanced and unbalanced analysis of variance problems and regression problems. Checking assumptions is presented as a crucial part of every statistical analysis. Examples using real data from a wide variety of fields are used to motivate theory. Christensen consistently examines residual plots and presents alternative analyses using different transformation and case deletions. Detailed examination of interactions, three factor analysis of variance, and a split-plot design with four factors are included. The numerous exercises emphasize analysis of real data. Senior undergraduate and graduate students in statistics and graduate students in other disciplines using analysis of variance, design of experiments, or regression analysis will find this book useful.

**Author**: Jian Qing Shi,Taeryon Choi

**Publisher:** CRC Press

**ISBN:** 1439837732

**Category:** Mathematics

**Page:** 216

**View:** 8822

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables. Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dimensional data and variable selection. The remainder of the text explores advanced topics of functional regression analysis, including novel nonparametric statistical methods for curve prediction, curve clustering, functional ANOVA, and functional regression analysis of batch data, repeated curves, and non-Gaussian data. Many flexible models based on Gaussian processes provide efficient ways of model learning, interpreting model structure, and carrying out inference, particularly when dealing with large dimensional functional data. This book shows how to use these Gaussian process regression models in the analysis of functional data. Some MATLAB® and C codes are available on the first author’s website.
*A Bag of Tricks*

**Author**: Andrew Gelman,Deborah Nolan

**Publisher:** Oxford University Press

**ISBN:** 0191088641

**Category:** Mathematics

**Page:** 384

**View:** 9835

Students in the sciences, economics, social sciences, and medicine take an introductory statistics course. And yet statistics can be notoriously difficult for instructors to teach and for students to learn. To help overcome these challenges, Gelman and Nolan have put together this fascinating and thought-provoking book. Based on years of teaching experience the book provides a wealth of demonstrations, activities, examples, and projects that involve active student participation. Part I of the book presents a large selection of activities for introductory statistics courses and has chapters such as 'First week of class'— with exercises to break the ice and get students talking; then descriptive statistics, graphics, linear regression, data collection (sampling and experimentation), probability, inference, and statistical communication. Part II gives tips on what works and what doesn't, how to set up effective demonstrations, how to encourage students to participate in class and to work effectively in group projects. Course plans for introductory statistics, statistics for social scientists, and communication and graphics are provided. Part III presents material for more advanced courses on topics such as decision theory, Bayesian statistics, sampling, and data science.

**Author**: John Fox

**Publisher:** SAGE Publications

**ISBN:** 1483321312

**Category:** Social Science

**Page:** 816

**View:** 8312

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.

**Author**: Henning Best,Christof Wolf

**Publisher:** SAGE

**ISBN:** 1473908353

**Category:** Social Science

**Page:** 424

**View:** 4271

'The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.' - John Fox, Professor, Department of Sociology, McMaster University 'The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.' - Ben Jann, Executive Director, Institute of Sociology, University of Bern 'Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.' -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.

Full PDF Download Free

Privacy Policy

Copyright © 2018 Download PDF Site — Primer WordPress theme by GoDaddy