The Explanatory Power Of Models
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The Explanatory Power Of Models
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Author : Robert Franck
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
The Explanatory Power Of Models written by Robert Franck and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Political Science categories.
This book progressively works out a method of constructing models which can bridge the gap between empirical and theoretical research in the social sciences. It aims to improve the explanatory power of models. The issue is quite novel, and has benefited from a thorough examination of statistical and mathematical models, conceptual models, diagrams and maps, machines, computer simulations, and artificial neural networks.
The Explanatory Power Of Models
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Author : Robert Franck
language : en
Publisher:
Release Date : 2014-01-15
The Explanatory Power Of Models written by Robert Franck and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Explanatory Model Analysis
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Author : Przemyslaw Biecek
language : en
Publisher: CRC Press
Release Date : 2021-02-15
Explanatory Model Analysis written by Przemyslaw Biecek and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-15 with Business & Economics categories.
Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.
Explanatory Models In Linguistics
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Author : Pere Julia
language : en
Publisher: Princeton University Press
Release Date : 2014-07-14
Explanatory Models In Linguistics written by Pere Julia and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-14 with Language Arts & Disciplines categories.
Pere Julia questions the recourse of contemporary linguists, psycholinguists, and philosophers to an idealized speaker-listener and maintains that there is no way to be sure of the organizing principles for linguistic data other than going to the sources of these data, i.e., speakers, listeners, and the circumstances under which they interact in actual situations. Originally published in 1983. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
On The Explanatory Power Of The Capm And Multifactor Models On The German Stock Market
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Author : Fabio Martin
language : en
Publisher:
Release Date : 2018-05
On The Explanatory Power Of The Capm And Multifactor Models On The German Stock Market written by Fabio Martin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05 with categories.
Bachelor Thesis from the year 2018 in the subject Business economics - General, grade: 1,0, Justus-Liebig-University Giessen, language: English, abstract: The aim of this thesis is to apply the CAPM and the Fama-French model on the German stock market and to see whether the models hold or not. The research methodology in this thesis is mostly an empirical analysis and adopts the approach of Pamane et. al (2014) and Fama and French (1993). However, I will use a different data set and run the test for the CAPM on single stocks rather than on portfolios in order to avoid covariance problems. Firstly, we will calculate the security market line in a two-step regression and then evaluate the influence of non-linear factors and non-systematic risk factors. In addition, the effects of the financial crisis have to be taken into consideration which is why, dummy variables will be used. However, before we interpret the regression results, we make sure that the data are reliable in the first place and correct them if necessary. For the purpose of assessing the Fama-French model, however, we use a quite different approach and follow the original procedure that was used by Fama and French (1993) themselves. This involves classifying the stocks according to size and value and then building a total of four portfolios. Afterwards, returns are computed and regressed against size and value factors. Even though it is quite common to use, for instance, the DAX or the NASDAQ as proxies, I see the chance of facing endogeneity issues when explaining returns of stocks that are listed in the DAX, which is why I will run all tests for a second time but this time using the MDAX instead of DAX as the market portfolio in order to avoid endogeneity problems.
Partial Least Squares Structural Equation Modeling Pls Sem Using R
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Author : Joseph F. Hair Jr.
language : en
Publisher: Springer Nature
Release Date : 2021-11-03
Partial Least Squares Structural Equation Modeling Pls Sem Using R written by Joseph F. Hair Jr. and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-03 with Business & Economics categories.
Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.
Statistical Inference Via Data Science A Moderndive Into R And The Tidyverse
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Author : Chester Ismay
language : en
Publisher: CRC Press
Release Date : 2019-12-23
Statistical Inference Via Data Science A Moderndive Into R And The Tidyverse written by Chester Ismay and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-23 with Mathematics categories.
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.
The Explanatory Autonomy Of The Biological Sciences
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Author : Wei Fang
language : en
Publisher: Taylor & Francis
Release Date : 2021-12-23
The Explanatory Autonomy Of The Biological Sciences written by Wei Fang and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-23 with Science categories.
This book argues for the explanatory autonomy of the biological sciences. It does so by showing that scientific explanations in the biological sciences cannot be reduced to explanations in the fundamental sciences such as physics and chemistry and by demonstrating that biological explanations are advanced by models rather than laws of nature. To maintain the explanatory autonomy of the biological sciences, the author argues against explanatory reductionism and shows that explanation in the biological sciences can be achieved without reduction. Then, he demonstrates that the biological sciences do not have laws of nature. Instead of laws, he suggests that biological models usually do the explanatory work. To understand how a biological model can explain phenomena in the world, the author proposes an inferential account of model explanation. The basic idea of this account is that, for a model to be explanatory, it must answer two kinds of questions: counterfactual-dependence questions that concern the model itself and hypothetical questions that concern the relationship between the model and its target system. The reason a biological model can answer these two kinds of questions is due to the fact that a model is a structure, and the holistic relationship between the model and its target warrants the hypothetical inference from the model to its target and thus helps to answer the second kind of question. The Explanatory Autonomy of the Biological Sciences will be of interest to researchers and advanced students working in philosophy of science, philosophy of biology and metaphysics.
Practical Statistics For Data Scientists
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Author : Peter Bruce
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-05-10
Practical Statistics For Data Scientists written by Peter Bruce and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-10 with Computers categories.
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
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)