Stat2 Modeling With Regression And Anova
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Stat2
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Author : Ann R. Cannon
language : en
Publisher: Macmillan Higher Education
Release Date : 2018-11-05
Stat2 written by Ann R. Cannon and has been published by Macmillan Higher Education this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-05 with Mathematics categories.
STAT2 introduces students to statistical modeling beyond what they have learned in a Stat 101 college course or an AP Statistics course. Building on basic concepts and methods learned in that course, STAT2 empowers students to analyze richer datasets that include more variables and address a broader range of research questions. Other than a working understanding of exponential and logarithmic functions, there are no prerequisites beyond successful completion of their first statistics course. To help all students make a smooth transition to this course, Chapter 0 reminds students of basic statistical terminology and also uses the familiar two-sample t-test as a way to illustrate the approach of specifying, estimating, and testing a statistical model. Using STAT2, students will: Go beyond their Stat 101 experience by learning to develop and apply models with both quantitative and categorical response variables, and with multiple explanatory variables. STAT2 Chapters are grouped into units that consider models based on the type of response and type of predictors. Discover that the practice of statistical modeling involves applying an interactive process. STAT2 employs a four-step process in all statistical modeling: Choose a form for the model, fit the model to the data, assess how well the model describes the data, and use the model to address the question of interest. Learn how to apply their developing judgment about statistical modeling. STAT2 introduces the idea of constructing statistical models at the very beginning, in a setting that students encountered in their Stat 101 course. This modeling focus continues throughout the course as students encounter new and increasingly more complicated scenarios. Analyze and draw conclusions from real data, which is crucial for preparing students to use statistical modeling in their professional lives. STAT2 incorporates real and rich data throughout the text. Using real data to address genuine research questions helps motivate students to study statistics. The richness stems not only from interesting contexts in a variety of disciplines, but also from the multivariable nature of most datasets.
Stat2 Modeling With Regression And Anova
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Author : Bradley A. Hartlaub
language : en
Publisher: WH Freeman
Release Date : 2018-01-29
Stat2 Modeling With Regression And Anova written by Bradley A. Hartlaub and has been published by WH Freeman this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-29 with Mathematics categories.
STAT2 introduces students to statistical modeling beyond what they have learned in a Stat 101 college course or an AP Statistics course. Building on basic concepts and methods learned in that course, STAT2 empowers students to analyze richer datasets that include more variables and address a broader range of research questions. Other than a working understanding of exponential and logarithmic functions, there are no prerequisites beyond successful completion of their first statistics course. To help all students make a smooth transition to this course, Chapter 0 reminds students of basic statistical terminology and also uses the familiar two-sample t-test as a way to illustrate the approach of specifying, estimating, and testing a statistical model. Using STAT2, students will: Go beyond their Stat 101 experience by learning to develop and apply models with both quantitative and categorical response variables, and with multiple explanatory variables. STAT2 Chapters are grouped into units that consider models based on the type of response and type of predictors. Discover that the practice of statistical modeling involves applying an interactive process. STAT2 employs a four-step process in all statistical modeling: Choose a form for the model, fit the model to the data, assess how well the model describes the data, and use the model to address the question of interest. Learn how to apply their developing judgment about statistical modeling. STAT2 introduces the idea of constructing statistical models at the very beginning, in a setting that students encountered in their Stat 101 course. This modeling focus continues throughout the course as students encounter new and increasingly more complicated scenarios. Analyze and draw conclusions from real data, which is crucial for preparing students to use statistical modeling in their professional lives. STAT2 incorporates real and rich data throughout the text. Using real data to address genuine research questions helps motivate students to study statistics. The richness stems not only from interesting contexts in a variety of disciplines, but also from the multivariable nature of most datasets.
Loose Leaf Version For Stat2
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Author : Ann R. Cannon
language : en
Publisher:
Release Date : 2018-12-21
Loose Leaf Version For Stat2 written by Ann R. Cannon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-21 with categories.
Beyond Multiple Linear Regression
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Author : Paul Roback
language : en
Publisher: CRC Press
Release Date : 2021-01-14
Beyond Multiple Linear Regression written by Paul Roback and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-14 with Mathematics categories.
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)
Stat2
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Author : Ann R. Cannon
language : en
Publisher: Worth Publishers
Release Date : 2013
Stat2 written by Ann R. Cannon and has been published by Worth Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Database management categories.
'STAT2' is designed to help students build on their statistical knowledge in order to analyse rich datasets using statistical models. The book develops a systematic approach to using different models and includes exercises to allow students to practice working with real data alongside output from statistical software.
Intermediate Statistical Investigations
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Author : Nathan Tintle
language : en
Publisher: John Wiley & Sons
Release Date : 2020-09-09
Intermediate Statistical Investigations written by Nathan Tintle and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-09 with Mathematics categories.
Intermediate Statistical Investigations provides a unified framework for explaining variation across study designs and variable types, helping students increase their statistical literacy and appreciate the indispensable role of statistics in scientific research. Requiring only a single introductory statistics course as a prerequisite, the program uses the immersive, simulation-based inference approach for which the author team is known.Students engage with various aspects of data collection and analysis using real examples and clear explanations designed to strengthen multivariable understanding and reinforce first-course concepts. Each chapter contains in-depth exercises which follow a consistent six-step statistical exploration and investigation method (ask a research question, design a study, explore the data, draw inferences, formulate conclusions, and look back and ahead) enabling students to assess a variety of concepts in a single assignment. Challenging questions based on research articles strengthen critical reading skills, fully worked examples demonstrate essential concepts and methods, and engaging visualizations illustrate key themes of explained variation. End-of-chapter investigations use real data from popular culture and published research studies in a variety of disciplines, exposing students to various applications of statistics in the real world. Throughout the text, user-friendly Rossman Chance web applets allow students to conduct the simulations and analyses covered in the book.
Applied Linear Regression
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Author : Sanford Weisberg
language : en
Publisher: John Wiley & Sons
Release Date : 2013-06-07
Applied Linear Regression written by Sanford Weisberg and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-07 with Mathematics categories.
Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: "I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression." —Technometrics, February 1987 "Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis." —American Scientist, May–June 1987 Applied Linear Regression, Third Edition has been thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, the Third Edition stresses the use of graphical methods in an effort to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results. The Third Edition incorporates new material reflecting the latest advances, including: Use of smoothers to summarize a scatterplot Box-Cox and graphical methods for selecting transformations Use of the delta method for inference about complex combinations of parameters Computationally intensive methods and simulation, including the bootstrap method Expanded chapters on nonlinear and logistic regression Completely revised chapters on multiple regression, diagnostics, and generalizations of regression Readers will also find helpful pedagogical tools and learning aids, including: More than 100 exercises, most based on interesting real-world data Web primers demonstrating how to use standard statistical packages, including R, S-Plus®, SPSS®, SAS®, and JMP®, to work all the examples and exercises in the text A free online library for R and S-Plus that makes the methods discussed in the book easy to use With its focus on graphical methods and analysis, coupled with many practical examples and exercises, this is an excellent textbook for upper-level undergraduates and graduate students, who will quickly learn how to use linear regression analysis techniques to solve and gain insight into real-life problems.
Statistical Modeling Linear Regression And Anova A Practical Computational Perspective
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Author : Hamid Ismail
language : en
Publisher:
Release Date : 2018-01-21
Statistical Modeling Linear Regression And Anova A Practical Computational Perspective written by Hamid Ismail and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-21 with Reference categories.
Statistical modeling is a branch of advanced statistics and a critical component of many applications in science and business. This book is an attempt to satisfy the need of mathematical statisticians and computational students in linear modeling and ANOVA. This book addresses linear modeling from a computational perspective with an emphasis on the mathematical details and step-by-step calculations using SAS(R) PROC IML. This book covers correlation analysis, simple and multiple linear regression, polynomial regression, regression with correlated data, model selection, analysis of covariance (ANCOVA), and analysis of variance (ANOVA). The level is suitable for upper level undergraduate and graduate students with knowledge of linear algebra and some programming skills.
Modern Statistical Methods For Hci
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Author : Judy Robertson
language : en
Publisher: Springer
Release Date : 2016-03-22
Modern Statistical Methods For Hci written by Judy Robertson and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-22 with Computers categories.
This book critically reflects on current statistical methods used in Human-Computer Interaction (HCI) and introduces a number of novel methods to the reader. Covering many techniques and approaches for exploratory data analysis including effect and power calculations, experimental design, event history analysis, non-parametric testing and Bayesian inference; the research contained in this book discusses how to communicate statistical results fairly, as well as presenting a general set of recommendations for authors and reviewers to improve the quality of statistical analysis in HCI. Each chapter presents [R] code for running analyses on HCI examples and explains how the results can be interpreted. Modern Statistical Methods for HCI is aimed at researchers and graduate students who have some knowledge of “traditional” null hypothesis significance testing, but who wish to improve their practice by using techniques which have recently emerged from statistics and related fields. This book critically evaluates current practices within the field and supports a less rigid, procedural view of statistics in favour of fair statistical communication.
Bayesian Nonparametric Data Analysis
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Author : Peter Müller
language : en
Publisher: Springer
Release Date : 2015-06-17
Bayesian Nonparametric Data Analysis written by Peter Müller and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-17 with Mathematics categories.
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.