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"This text demonstrates how to conduct latent variable modeling in R. Techniques that can be analyzed using the free program R are showcased including exploratory and confirmatory factor analysis, structural equation modeling (SEM), latent growth curve modeling, item response theory (IRT), and latent class analysis. Easy to follow demonstrations of how to conduct latent variable modeling in R are provided along with descriptions of the major features of the models,their specialized uses, and a full interpretation of the results. Every R command necessary for conducting the analyses is described so readers can directly apply the R functions to their own data. Each chapter features a complete analysis of one or more example datasets including a demonstration of the analysis of the data using R, along with a discussion of relevant theory that includes a full description of the models, the assumptions underlying each model, and statistical details of estimation, hypothesis testing, and more to help readers better understand the models and interpret the results. Some of the examples represent data that is not perfectly "behaved" so as to provide a more realistic view of situations readers will likely encounter with their own data. Detailed explanations of input statements help readers generalize what they learn to their own analyses. Each chapter features an introduction, summary, and exercises involving the application of the model(s), and a list of further readings with an emphasis on related texts that provide more detailed theoretical coverage. A full glossary of the key terms, a cheat sheet that reviews the key R commands, and answers to half of the exercises are provided at the end of the book"--

"This book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used. It is aimed at the general reader with the equivalent of a bachelor's degree in economics, statistics or some more technical field. It covers the standard tools of microeconometrics, OLS, instrumental variables, Heckman selection and difference in difference. In addition, it introduces bounds, factor models, mixture models and empirical Bayesian analysis."

Preface -- Introduction -- Data objects in R -- Data handling in R -- R programming & control flow -- Data exploration -- Graphics in R -- Regression analysis-1 -- Regression analysis-ii -- Time series analysis -- Extreme value theory modelling -- Introduction to multivariate analysis using copulas -- Bibliography -- Index

"This book is built on the premise that anyone can learn to use the R software. The authors emphasize using R to do useful things like writing papers and reports, creating graphs, and conducting simple data analysis. After a first chapter on installing the software and project setup, the second chapter shows how to write an essay using R Markdown, rewarding readers with an immediate tangible result, and taking the fear out of working with a new software. Student-friendly language and examples (e.g. binge-watched shows on Netflix, top 5 songs on Spotify), cumulative learning and repetition across chapters, and practice exercises make this a must-have guide for a variety of courses where data is used and reports need to be written (including, but not limited to intro statistics and research methods). Code and datasets used to carry out the examples in the book are available on an accompanying website"--

Business Statistics with Solutions in R covers a wide range of applications of statistics in solving business related problems. It will introduce readers to quantitative tools that are necessary for daily business needs and help them to make evidence-based decisions. The book provides an insight on how to summarize data, analyze it, and draw meaningful inferences that can be used to improve decisions. It will enable readers to develop computational skills and problem-solving competence using the open source language, R. Mustapha Abiodun Akinkunmi uses real life business data for illustrative examples while discussing the basic statistical measures, probability, regression analysis, significance testing, correlation, the Poisson distribution, process control for manufacturing, time series analysis, forecasting techniques, exponential smoothing, univariate and multivariate analysis including ANOVA and MANOVA and more in this valuable reference for policy makers, professionals, academics and individuals interested in the areas of business statistics, applied statistics, statistical computing, finance, management and econometrics

Introduction -- Making the transition -- Descriptive statistics -- Regression analysis in Excel and R -- Analysis of variance and covariance in Excel and R -- Logistic regression in Excel and R -- Principal components analysis