Mastering Metrics

Author: Joshua D. Angrist
Publisher: Princeton University Press
ISBN: 1400852382
Size: 75.73 MB
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An accessible and fun guide to the essential tools of econometric research Applied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu–themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstrates why econometrics is exciting and useful. The five most valuable econometric methods, or what the authors call the Furious Five--random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences--are illustrated through well-crafted real-world examples (vetted for awesomeness by Kung Fu Panda's Jade Palace). Does health insurance make you healthier? Randomized experiments provide answers. Are expensive private colleges and selective public high schools better than more pedestrian institutions? Regression analysis and a regression discontinuity design reveal the surprising truth. When private banks teeter, and depositors take their money and run, should central banks step in to save them? Differences-in-differences analysis of a Depression-era banking crisis offers a response. Could arresting O. J. Simpson have saved his ex-wife's life? Instrumental variables methods instruct law enforcement authorities in how best to respond to domestic abuse. Wielding econometric tools with skill and confidence, Mastering 'Metrics uses data and statistics to illuminate the path from cause to effect. Shows why econometrics is important Explains econometric research through humorous and accessible discussion Outlines empirical methods central to modern econometric practice Works through interesting and relevant real-world examples

Methods In Social Epidemiology

Author: J. Michael Oakes
Publisher: John Wiley & Sons
ISBN: 111850559X
Size: 32.50 MB
Format: PDF, ePub, Docs
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A thorough, practical reference on the social patterns behind health outcomes Methods in Social Epidemiology provides students and professionals with a comprehensive reference for studying the social distribution and social determinants of health. Covering the theory, models, and methods used to measure and analyze these phenomena, this book serves as both an introduction to the field and a practical manual for data collection and analysis. This new second edition has been updated to reflect the field's tremendous growth in recent years, including advancements in statistical modeling and study designs. New chapters delve into genetic methods, structural cofounding, selection bias, network methods, and more, including new discussion on qualitative data collection with disadvantaged populations. Social epidemiology studies the way society's innumerable social interactions, both past and present, yields different exposures and health outcomes between individuals within populations. This book provides a thorough, detailed overview of the field, with expert guidance toward the real-world methods that fuel the latest advances. Identify, measure, and track health patterns in the population Discover how poverty, race, and socioeconomic factors become risk factors for disease Learn qualitative data collection techniques and methods of statistical analysis Examine up-to-date models, theory, and frameworks in the social epidemiology sphere As the field continues to evolve, researchers continue to identify new disease-specific risk factors and learn more about how the social system promotes and maintains well-known exposure disparities. New technology in data science and genomics allows for more rigorous investigation and analysis, while the general thinking in the field has become more targeted and attentive to causal inference and core assumptions behind effect identification. It's an exciting time to be a part of the field, and Methods in Social Epidemiology provides a solid reference for any student, researcher, or faculty in public health.

The Case Against Education

Author: Bryan Caplan
Publisher: Princeton University Press
ISBN: 0691174652
Size: 74.51 MB
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Why we need to stop wasting public funds on education Despite being immensely popular--and immensely lucrative—education is grossly overrated. In this explosive book, Bryan Caplan argues that the primary function of education is not to enhance students' skill but to certify their intelligence, work ethic, and conformity—in other words, to signal the qualities of a good employee. Learn why students hunt for easy As and casually forget most of what they learn after the final exam, why decades of growing access to education have not resulted in better jobs for the average worker but instead in runaway credential inflation, how employers reward workers for costly schooling they rarely if ever use, and why cutting education spending is the best remedy. Caplan draws on the latest social science to show how the labor market values grades over knowledge, and why the more education your rivals have, the more you need to impress employers. He explains why graduation is our society's top conformity signal, and why even the most useless degrees can certify employability. He advocates two major policy responses. The first is educational austerity. Government needs to sharply cut education funding to curb this wasteful rat race. The second is more vocational education, because practical skills are more socially valuable than teaching students how to outshine their peers. Romantic notions about education being "good for the soul" must yield to careful research and common sense—The Case against Education points the way.

Quasi Experimentation

Author: Charles S. Reichardt
Publisher: Guilford Publications
ISBN: 1462540252
Size: 22.86 MB
Format: PDF, Docs
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Featuring engaging examples from diverse disciplines, this book explains how to use modern approaches to quasi-experimentation to derive credible estimates of treatment effects under the demanding constraints of field settings. Foremost expert Charles S. Reichardt provides an in-depth examination of the design and statistical analysis of pretest–posttest, nonequivalent groups, regression discontinuity, and interrupted time-series designs. He details their relative strengths and weaknesses and offers practical advice about their use. Comparing quasi-experiments to randomized experiments, Reichardt discusses when and why the former might be a better choice than the latter in the face of the contingencies that are likely to arise in practice. Modern methods for elaborating a research design to remove bias from estimates of treatment effects are described, as are tactics for dealing with missing data and noncompliance with treatment assignment. Throughout, mathematical equations are translated into words to enhance accessibility. Adding to its discussion of prototypical quasi-experiments, the book also provides a complete typology of quasi-experimental design options to help the reader craft the best research design to fit the circumstances of a given study.

Python End To End Data Analysis

Author: Phuong Vothihong
Publisher: Packt Publishing Ltd
ISBN: 1788396545
Size: 61.32 MB
Format: PDF, Mobi
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Leverage the power of Python to clean, scrape, analyze, and visualize your data About This Book Clean, format, and explore your data using the popular Python libraries and get valuable insights from it Analyze big data sets; create attractive visualizations; manipulate and process various data types using NumPy, SciPy, and matplotlib; and more Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data Who This Book Is For This course is for developers, analysts, and data scientists who want to learn data analysis from scratch. This course will provide you with a solid foundation from which to analyze data with varying complexity. A working knowledge of Python (and a strong interest in playing with your data) is recommended. What You Will Learn Understand the importance of data analysis and master its processing steps Get comfortable using Python and its associated data analysis libraries such as Pandas, NumPy, and SciPy Clean and transform your data and apply advanced statistical analysis to create attractive visualizations Analyze images and time series data Mine text and analyze social networks Perform web scraping and work with different databases, Hadoop, and Spark Use statistical models to discover patterns in data Detect similarities and differences in data with clustering Work with Jupyter Notebook to produce publication-ready figures to be included in reports In Detail Data analysis is the process of applying logical and analytical reasoning to study each component of data present in the system. Python is a multi-domain, high-level, programming language that offers a range of tools and libraries suitable for all purposes, it has slowly evolved as one of the primary languages for data science. Have you ever imagined becoming an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? If yes, look no further, this is the course you need! In this course, we will get you started with Python data analysis by introducing the basics of data analysis and supported Python libraries such as matplotlib, NumPy, and pandas. Create visualizations by choosing color maps, different shapes, sizes, and palettes then delve into statistical data analysis using distribution algorithms and correlations. You'll then find your way around different data and numerical problems, get to grips with Spark and HDFS, and set up migration scripts for web mining. You'll be able to quickly and accurately perform hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. Finally, you will delve into advanced techniques such as performing regression, quantifying cause and effect using Bayesian methods, and discovering how to use Python's tools for supervised machine learning. The course provides you with highly practical content explaining data analysis with Python, from the following Packt books: Getting Started with Python Data Analysis. Python Data Analysis Cookbook. Mastering Python Data Analysis. By the end of this course, you will have all the knowledge you need to analyze your data with varying complexity levels, and turn it into actionable insights. Style and approach Learn Python data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. It offers you a useful way of analyzing the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of data analysis.