Economists present their arguments in three different types of arguments: verbal, graphical, and mathematical. If you flip over introductory economic textbooks, you will notice that analysis is usually done based on verbal argument and diagrams. Even for intermediate and advanced textbooks, you will notice that the difference is the mathematical argument -- diagrams are still useful. This is also true for academic research. However, drawing a nice diagram is not easy. Standard software is not good for drawing economic diagrams. Either it is too simple or it is too professional. One nice drawing software is the TikZ package in LaTeX . However, it is a drawing programming so that there is a steep learning curve. This is the reason that I write this book.
This book is the first companion book for "TikZ Cookbook for Diagram in Economics: step-by-step illustration". It illustrates how to draw economic diagrams found in the popular principle of economics textbook using TikZ. If you want to download the code tex file, you may buy here: https://gumroad.com/l/ljVrJ
This new book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software. Those new to R Markdown will appreciate the short, practical examples that address the most common issues users encounter. Frequent users will also benefit from the wide ranging tips and tricks that expose ‘hidden’ features, support customization and demonstrate the many new and varied applications of the software. After reading this book users will learn how to: Enhance your R Markdown content with diagrams, citations, and dynamically generated text Streamline your workflow with child documents, code chunk references, and caching Control the formatting and layout with Pandoc markdown syntax or by writing custom HTML and LaTeX templates Utilize chunk options and hooks to fine-tune how your code is processed Switch between different language engineers to seamlessly incorporate python, D3, and more into your analysis
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and 'rusty' calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and BUGS software - Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). - Coverage of experiment planning - R and BUGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment
This book is the second companion book for "TikZ Cookbook for Diagram in Economics: step-by-step illustration". It illustrates how to draw economic diagrams found in the intermediate microeconomics textbook using TikZ.
With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market.Comparing and contrasting the dif
Scientific pluralism is an issue at the forefront of philosophy of science. This landmark work addresses the question, Can pluralism be advanced as a general, philosophical interpretation of science? Scientific Pluralism demonstrates the viability of the view that some phenomena require multiple accounts. Pluralists observe that scientists present various—sometimes even incompatible—models of the world and argue that this is due to the complexity of the world and representational limitations. Including investigations in biology, physics, economics, psychology, and mathematics, this work provides an empirical basis for a consistent stance on pluralism and makes the case that it should change the ways that philosophers, historians, and social scientists analyze scientific knowledge. Contributors: John Bell, U of Western Ontario; Michael Dickson, U of South Carolina; Carla Fehr, Iowa State U; Ronald N. Giere, U of Minnesota; Geoffrey Hellman, U of Minnesota; Alan Richardson, U of British Columbia; C. Wade Savage, U of Minnesota; Esther-Mirjam Sent, U of Nijmegen. Stephen H. Kellert is professor of philosophy at Hamline University and a fellow of the Minnesota Center for Philosophy of Science. Helen E. Longino is professor of philosophy at Stanford University. C. Kenneth Waters is associate professor of philosophy and director of the Minnesota Center for Philosophy of Science.
LaTeX is a free, automated state-of-the-art typesetting system. This book teaches all the ins and outs of LaTeX which are needed to write an article, report, thesis, or book. The book teaches by example, giving many worked out examples showing input and output side by side. The book presents the most recent techniques for presenting data plots, complex graphics, and computer presentations, but does not require previous knowledge. However, it is also a reference for the more seasoned user, with pointers to modern techniques and packages. Recurring themes in the book are consistent and effective presentation, planning and development, controlling style and content, and maintenance.
This book constitutes the proceedings of the 7th International Conference on Mathematical Software, ICMS 2020, held in Braunschweig, Germany, in July 2020. The 48 papers included in this volume were carefully reviewed and selected from 58 submissions. The program of the 2020 meeting consisted of 20 topical sessions, each of which providing an overview of the challenges, achievements and progress in a environment of mathematical software research, development and use.
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes' rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and JAGS software - Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis) - Coverage of experiment planning - R and JAGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment - Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs
