Author: Fred L. Ramsey
Publisher: Duxbury Resource Center
ISBN: 9780534389505
Category : Mathematical statistics
Languages : en
Pages : 0
Book Description
Prepare for exams and succeed in your statistics course with this comprehensive solutions manual! Featuring worked out-solutions to the problems in THE STATISTICAL SLEUTH: A COURSE IN METHODS OF DATA ANALYSIS, 2nd Edition, this manual shows you how to approach and solve problems using the same step-by-step explanations found in your textbook examples.
The Statistical Sleuth
The Statistical Sleuth: A Course in Methods of Data Analysis
Author: Fred Ramsey
Publisher: Cengage Learning
ISBN: 9781133490678
Category : Mathematics
Languages : en
Pages : 784
Book Description
THE STATISTICAL SLEUTH: A COURSE IN METHODS OF DATA ANALYSIS, Third Edition offers an appealing treatment of general statistical methods that takes full advantage of the computer, both as a computational and an analytical tool. The material is independent of any specific software package, and prominently treats modeling and interpretation in a way that goes beyond routine patterns. The book focuses on a serious analysis of real case studies, strategies and tools of modern statistical data analysis, the interplay of statistics and scientific learning, and the communication of results. With interesting examples, real data, and a variety of exercise types (conceptual, computational, and data problems), the authors get students excited about statistics. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Publisher: Cengage Learning
ISBN: 9781133490678
Category : Mathematics
Languages : en
Pages : 784
Book Description
THE STATISTICAL SLEUTH: A COURSE IN METHODS OF DATA ANALYSIS, Third Edition offers an appealing treatment of general statistical methods that takes full advantage of the computer, both as a computational and an analytical tool. The material is independent of any specific software package, and prominently treats modeling and interpretation in a way that goes beyond routine patterns. The book focuses on a serious analysis of real case studies, strategies and tools of modern statistical data analysis, the interplay of statistics and scientific learning, and the communication of results. With interesting examples, real data, and a variety of exercise types (conceptual, computational, and data problems), the authors get students excited about statistics. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Information Sampling and Adaptive Cognition
Author: Klaus Fiedler
Publisher: Cambridge University Press
ISBN: 9780521831598
Category : Psychology
Languages : en
Pages : 504
Book Description
This book proposes that environmental information samples are biased and cognitive processes are not.
Publisher: Cambridge University Press
ISBN: 9780521831598
Category : Psychology
Languages : en
Pages : 504
Book Description
This book proposes that environmental information samples are biased and cognitive processes are not.
Probability, Statistics, and Data
Author: Darrin Speegle
Publisher: CRC Press
ISBN: 1000504514
Category : Business & Economics
Languages : en
Pages : 644
Book Description
This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included, using calculus when appropriate, but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized, quantified using simulations, and demonstrated with real data. The book compares parametric and non-parametric methods through simulation, allowing for a thorough investigation of testing error and power. The text builds R skills from the outset, allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers, so that you are working with current, real data, which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr, ggplot2, tidyr, stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University, and the majority of the more than 400 exercises have been classroom tested.
Publisher: CRC Press
ISBN: 1000504514
Category : Business & Economics
Languages : en
Pages : 644
Book Description
This book is a fresh approach to a calculus based, first course in probability and statistics, using R throughout to give a central role to data and simulation. The book introduces probability with Monte Carlo simulation as an essential tool. Simulation makes challenging probability questions quickly accessible and easily understandable. Mathematical approaches are included, using calculus when appropriate, but are always connected to experimental computations. Using R and simulation gives a nuanced understanding of statistical inference. The impact of departure from assumptions in statistical tests is emphasized, quantified using simulations, and demonstrated with real data. The book compares parametric and non-parametric methods through simulation, allowing for a thorough investigation of testing error and power. The text builds R skills from the outset, allowing modern methods of resampling and cross validation to be introduced along with traditional statistical techniques. Fifty-two data sets are included in the complementary R package fosdata. Most of these data sets are from recently published papers, so that you are working with current, real data, which is often large and messy. Two central chapters use powerful tidyverse tools (dplyr, ggplot2, tidyr, stringr) to wrangle data and produce meaningful visualizations. Preliminary versions of the book have been used for five semesters at Saint Louis University, and the majority of the more than 400 exercises have been classroom tested.
An Introduction to Categorical Data Analysis
Author: Alan Agresti
Publisher: John Wiley & Sons
ISBN: 1119405270
Category : Mathematics
Languages : en
Pages : 393
Book Description
A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
Publisher: John Wiley & Sons
ISBN: 1119405270
Category : Mathematics
Languages : en
Pages : 393
Book Description
A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
Filing Patents Online
Author: Sarfaraz K. Niazi
Publisher: CRC Press
ISBN: 0203485122
Category : Business & Economics
Languages : en
Pages : 464
Book Description
The average cost of an uncomplicated patent application filing is about $10,000. This high cost can leave thousands of inventors out in the cold. Filing Patents Online: A Professional Guide is a complete manual that walks inventors through each step of filing and prosecuting the patent online at a fraction of the cost. The online filing system reco
Publisher: CRC Press
ISBN: 0203485122
Category : Business & Economics
Languages : en
Pages : 464
Book Description
The average cost of an uncomplicated patent application filing is about $10,000. This high cost can leave thousands of inventors out in the cold. Filing Patents Online: A Professional Guide is a complete manual that walks inventors through each step of filing and prosecuting the patent online at a fraction of the cost. The online filing system reco
Treebanks
Author: A. Abeillé
Publisher: Springer Science & Business Media
ISBN: 9781402013348
Category : Psychology
Languages : en
Pages : 442
Book Description
This book provides a state of the art on work being done with parsed corpora. It gathers 21 papers on building and using parsed corpora raising many relevant questions, and deals with a variety of languages and a variety of corpora. It is for those working in linguistics, computational linguistics, natural language, syntax, and grammar.
Publisher: Springer Science & Business Media
ISBN: 9781402013348
Category : Psychology
Languages : en
Pages : 442
Book Description
This book provides a state of the art on work being done with parsed corpora. It gathers 21 papers on building and using parsed corpora raising many relevant questions, and deals with a variety of languages and a variety of corpora. It is for those working in linguistics, computational linguistics, natural language, syntax, and grammar.
Regression Modelling wih Spatial and Spatial-Temporal Data
Author: Robert P. Haining
Publisher: CRC Press
ISBN: 0429529104
Category : Mathematics
Languages : en
Pages : 556
Book Description
Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.
Publisher: CRC Press
ISBN: 0429529104
Category : Mathematics
Languages : en
Pages : 556
Book Description
Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.
Using R for Introductory Statistics
Author: John Verzani
Publisher: CRC Press
ISBN: 1315360306
Category : Computers
Languages : en
Pages : 522
Book Description
The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.
Publisher: CRC Press
ISBN: 1315360306
Category : Computers
Languages : en
Pages : 522
Book Description
The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.
Statistics for Management and Economics
Author: Gerald Keller
Publisher:
ISBN: 9780534491239
Category : Economics
Languages : en
Pages : 900
Book Description
Teaches students how to apply statistics to real business problems through the authors' unique three-step approach to problem solving. Students learn to identify, compute and interpret the results in the context of the problem.
Publisher:
ISBN: 9780534491239
Category : Economics
Languages : en
Pages : 900
Book Description
Teaches students how to apply statistics to real business problems through the authors' unique three-step approach to problem solving. Students learn to identify, compute and interpret the results in the context of the problem.