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Projections of Education Statistics to 2022 Forty-First Edition
Projections of Education Statistics to 2022 Forty-first Edition 20192019 20212021 20182018 20202020 20222022 NCES 2014-051 U.S. DEPARTMENT OF EDUCATION Projections of Education Statistics to 2022 Forty-first Edition FEBRUARY 2014 William J. Hussar National Center for Education Statistics Tabitha M. Bailey IHS Global Insight NCES 2014-051 U.S. DEPARTMENT OF EDUCATION U.S. Department of Education Arne Duncan Secretary Institute of Education Sciences John Q. Easton Director National Center for Education Statistics John Q. Easton Acting Commissioner The National Center for Education Statistics (NCES) is the primary federal entity for collecting, analyzing, and reporting data related to education in the United States and other nations. It fulfills a congressional mandate to collect, collate, analyze, and report full and complete statistics on the condition of education in the United States; conduct and publish reports and specialized analyses of the meaning and significance of such statistics; assist state and local education agencies in improving their statistical systems; and review and report on education activities in foreign countries. NCES activities are designed to address high-priority education data needs; provide consistent, reliable, complete, and accurate indicators of education status and trends; and report timely, useful, and high-quality data to the U.S. Department of Education, the Congress, the states, other education policymakers, practitioners, data users, and the general public. Unless specifically noted, all information contained herein is in the public domain. We strive to make our products available in a variety of formats and in language that is appropriate to a variety of audiences. You, as our customer, are the best judge of our success in communicating information effectively. -
811D Ecollomic Statistics Adrllillistra!Tioll
811d Ecollomic Statistics Adrllillistra!tioll BUREAU THE CENSUS • I n i • I Charles G. Langham Issued 1973 U.S. D OF COM ERCE Frederick B. Dent. Secretary Social Economic Statistics Edward D. Administrator BU OF THE CENSUS Vincent P. Barabba, Acting Director Vincent Director Associate Director for Economic Associate Director for Statistical Standards and 11/1",1"\"/1,, DATA USER SERVICES OFFICE Robert B. Chief ACKNOWLEDGMENTS This report was in the Data User Services Office Charles G. direction of Chief, Review and many persons the Bureau. Library of Congress Card No.: 13-600143 SUGGESTED CiTATION U.S. Bureau of the Census. The Economic Censuses of the United by Charles G. longham. Working Paper D.C., U.S. Government Printing Office, 1B13 For sale by Publication Oistribution Section. Social and Economic Statistics Administration, Washington, D.C. 20233. Price 50 cents. N Page Economic Censuses in the 19th Century . 1 The First "Economic Censuses" . 1 Economic Censuses Discontinued, Resumed, and Augmented . 1 Improvements in the 1850 Census . 2 The "Kennedy Report" and the Civil War . • . 3 Economic Censuses and the Industrial Revolution. 4 Economic Censuses Adjust to the Times: The Censuses of 1880, 1890, and 1900 .........................•.. , . 4 Economic Censuses in the 20th Century . 8 Enumerations on Specialized Economic Topics, 1902 to 1937 . 8 Censuses of Manufacturing and Mineral Industries, 1905 to 1920. 8 Wartime Data Needs and Biennial Censuses of Manufactures. 9 Economic Censuses and the Great Depression. 10 The War and Postwar Developments: Economic Censuses Discontinued, Resumed, and Rescheduled. 13 The 1954 Budget Crisis. 15 Postwar Developments in Economic Census Taking: The Computer, and" Administrative Records" . -
Use of Statistical Tables
TUTORIAL | SCOPE USE OF STATISTICAL TABLES Lucy Radford, Jenny V Freeman and Stephen J Walters introduce three important statistical distributions: the standard Normal, t and Chi-squared distributions PREVIOUS TUTORIALS HAVE LOOKED at hypothesis testing1 and basic statistical tests.2–4 As part of the process of statistical hypothesis testing, a test statistic is calculated and compared to a hypothesised critical value and this is used to obtain a P- value. This P-value is then used to decide whether the study results are statistically significant or not. It will explain how statistical tables are used to link test statistics to P-values. This tutorial introduces tables for three important statistical distributions (the TABLE 1. Extract from two-tailed standard Normal, t and Chi-squared standard Normal table. Values distributions) and explains how to use tabulated are P-values corresponding them with the help of some simple to particular cut-offs and are for z examples. values calculated to two decimal places. STANDARD NORMAL DISTRIBUTION TABLE 1 The Normal distribution is widely used in statistics and has been discussed in z 0.00 0.01 0.02 0.03 0.050.04 0.05 0.06 0.07 0.08 0.09 detail previously.5 As the mean of a Normally distributed variable can take 0.00 1.0000 0.9920 0.9840 0.9761 0.9681 0.9601 0.9522 0.9442 0.9362 0.9283 any value (−∞ to ∞) and the standard 0.10 0.9203 0.9124 0.9045 0.8966 0.8887 0.8808 0.8729 0.8650 0.8572 0.8493 deviation any positive value (0 to ∞), 0.20 0.8415 0.8337 0.8259 0.8181 0.8103 0.8206 0.7949 0.7872 0.7795 0.7718 there are an infinite number of possible 0.30 0.7642 0.7566 0.7490 0.7414 0.7339 0.7263 0.7188 0.7114 0.7039 0.6965 Normal distributions. -
An Introduction to Psychometric Theory with Applications in R
What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD An introduction to Psychometric Theory with applications in R William Revelle Department of Psychology Northwestern University Evanston, Illinois USA February, 2013 1 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Overview 1 Overview Psychometrics and R What is Psychometrics What is R 2 Part I: an introduction to R What is R A brief example Basic steps and graphics 3 Day 1: Theory of Data, Issues in Scaling 4 Day 2: More than you ever wanted to know about correlation 5 Day 3: Dimension reduction through factor analysis, principal components analyze and cluster analysis 6 Day 4: Classical Test Theory and Item Response Theory 7 Day 5: Structural Equation Modeling and applied scale construction 2 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Outline of Day 1/part 1 1 What is psychometrics? Conceptual overview Theory: the organization of Observed and Latent variables A latent variable approach to measurement Data and scaling Structural Equation Models 2 What is R? Where did it come from, why use it? Installing R on your computer and adding packages Installing and using packages Implementations of R Basic R capabilities: Calculation, Statistical tables, Graphics Data sets 3 Basic statistics and graphics 4 steps: read, explore, test, graph Basic descriptive and inferential statistics 4 TOD 3 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD What is psychometrics? In physical science a first essential step in the direction of learning any subject is to find principles of numerical reckoning and methods for practicably measuring some quality connected with it. -
2019 TIGER/Line Shapefiles Technical Documentation
TIGER/Line® Shapefiles 2019 Technical Documentation ™ Issued September 2019220192018 SUGGESTED CITATION FILES: 2019 TIGER/Line Shapefiles (machine- readable data files) / prepared by the U.S. Census Bureau, 2019 U.S. Department of Commerce Economic and Statistics Administration Wilbur Ross, Secretary TECHNICAL DOCUMENTATION: Karen Dunn Kelley, 2019 TIGER/Line Shapefiles Technical Under Secretary for Economic Affairs Documentation / prepared by the U.S. Census Bureau, 2019 U.S. Census Bureau Dr. Steven Dillingham, Albert Fontenot, Director Associate Director for Decennial Census Programs Dr. Ron Jarmin, Deputy Director and Chief Operating Officer GEOGRAPHY DIVISION Deirdre Dalpiaz Bishop, Chief Andrea G. Johnson, Michael R. Ratcliffe, Assistant Division Chief for Assistant Division Chief for Address and Spatial Data Updates Geographic Standards, Criteria, Research, and Quality Monique Eleby, Assistant Division Chief for Gregory F. Hanks, Jr., Geographic Program Management Deputy Division Chief and External Engagement Laura Waggoner, Assistant Division Chief for Geographic Data Collection and Products 1-0 Table of Contents 1. Introduction ...................................................................................................................... 1-1 1. Introduction 1.1 What is a Shapefile? A shapefile is a geospatial data format for use in geographic information system (GIS) software. Shapefiles spatially describe vector data such as points, lines, and polygons, representing, for instance, landmarks, roads, and lakes. The Environmental Systems Research Institute (Esri) created the format for use in their software, but the shapefile format works in additional Geographic Information System (GIS) software as well. 1.2 What are TIGER/Line Shapefiles? The TIGER/Line Shapefiles are the fully supported, core geographic product from the U.S. Census Bureau. They are extracts of selected geographic and cartographic information from the U.S. -
Publications Using the HMD in Years 1997 – 2013
Publications Using the HMD in Years 1997 – 2013 Table of Contents Official Reports ................................................................................................................................................... 1 Books and Book Chapters .............................................................................................................................. 4 Journal Articles ................................................................................................................................................ 12 Dissertations and Theses .............................................................................................................................. 63 Technical Reports, Working, Research and Discussion Papers ......................................................... 67 Introduction The following comprises a list of publications that rely on data from the Human Mortality Database. It resorts to the Google Scholar web search engine1 using “Human mortality database” and “Berkeley mortality database” as the search expressions. The expressions may appear anywhere in the publication (title, abstract, body). Works that used the BMD are identified by “[BMD]” at the end of the citation; all other publications used the HMD. This version of the HMD reference list concentrates on scholarly articles and books, dissertations, technical reports and working papers published from January 1997 up to the end of November 2013. The list also includes all publications by the HMD team members based on analyses of -
Statistics on Spotlight: World Statistics Day 2015
Statistics on Spotlight: World Statistics Day 2015 Shahjahan Khan Professor of Statistics School of Agricultural, Computational and Environmental Sciences University of Southern Queensland, Toowoomba, Queensland, AUSTRALIA Founding Chief Editor, Journal of Applied Probability and Statistics (JAPS), USA Email: [email protected] Abstract In the age of evidence based decision making and data science, statistics has become an integral part of almost all spheres of modern life. It is being increasingly applied for both private and public benefits including business and trade as well as various public sectors, not to mention its crucial role in research and innovative technologies. No modern government could conduct its normal functions and deliver its services and implement its development agenda without relying on good quality statistics. The key role of statistics is more visible and engraved in the planning and development of every successful nation state. In fact, the use of statistics is not only national but also regional, international and transnational for organisations and agencies that are driving social, economic, environmental, health, poverty elimination, education and other agendas for planned development. Starting from stocktaking of the state of the health of various sectors of the economy of any nation/region to setting development goals, assessment of progress, monitoring programs and undertaking follow-up initiatives depend heavily on relevant statistics. Only statistical methods are capable of determining indicators, comparing them, and help identify the ways to ensure balanced and equitable development. 1 Introduction The goals of the celebration of World Statistics Day 2015 is to highlight the fact that official statistics help decision makers develop informed policies that impact millions of people. -
Cluster Analysis for Gene Expression Data: a Survey
Cluster Analysis for Gene Expression Data: A Survey Daxin Jiang Chun Tang Aidong Zhang Department of Computer Science and Engineering State University of New York at Buffalo Email: djiang3, chuntang, azhang @cse.buffalo.edu Abstract DNA microarray technology has now made it possible to simultaneously monitor the expres- sion levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremen- dous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expres- sion data, and also new algorithms have recently been proposed specifically aiming at gene ex- pression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. -
Reliability Engineering: Today and Beyond
Reliability Engineering: Today and Beyond Keynote Talk at the 6th Annual Conference of the Institute for Quality and Reliability Tsinghua University People's Republic of China by Professor Mohammad Modarres Director, Center for Risk and Reliability Department of Mechanical Engineering Outline – A New Era in Reliability Engineering – Reliability Engineering Timeline and Research Frontiers – Prognostics and Health Management – Physics of Failure – Data-driven Approaches in PHM – Hybrid Methods – Conclusions New Era in Reliability Sciences and Engineering • Started as an afterthought analysis – In enduing years dismissed as a legitimate field of science and engineering – Worked with small data • Three advances transformed reliability into a legitimate science: – 1. Availability of inexpensive sensors and information systems – 2. Ability to better described physics of damage, degradation, and failure time using empirical and theoretical sciences – 3. Access to big data and PHM techniques for diagnosing faults and incipient failures • Today we can predict abnormalities, offer just-in-time remedies to avert failures, and making systems robust and resilient to failures Seventy Years of Reliability Engineering – Reliability Engineering Initiatives in 1950’s • Weakest link • Exponential life model • Reliability Block Diagrams (RBDs) – Beyond Exp. Dist. & Birth of System Reliability in 1960’s • Birth of Physics of Failure (POF) • Uses of more proper distributions (Weibull, etc.) • Reliability growth • Life testing • Failure Mode and Effect Analysis -
Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists
Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists Seong Ho Park, MD1 The receiver operating characteristic (ROC) curve, which is defined as a plot of Jin Mo Goo, MD1 test sensitivity as the y coordinate versus its 1-specificity or false positive rate Chan-Hee Jo, PhD2 (FPR) as the x coordinate, is an effective method of evaluating the performance of diagnostic tests. The purpose of this article is to provide a nonmathematical introduction to ROC analysis. Important concepts involved in the correct use and interpretation of this analysis, such as smooth and empirical ROC curves, para- metric and nonparametric methods, the area under the ROC curve and its 95% confidence interval, the sensitivity at a particular FPR, and the use of a partial area under the ROC curve are discussed. Various considerations concerning the collection of data in radiological ROC studies are briefly discussed. An introduc- tion to the software frequently used for performing ROC analyses is also present- ed. he receiver operating characteristic (ROC) curve, which is defined as a Index terms: plot of test sensitivity as the y coordinate versus its 1-specificity or false Diagnostic radiology positive rate (FPR) as the x coordinate, is an effective method of evaluat- Receiver operating characteristic T (ROC) curve ing the quality or performance of diagnostic tests, and is widely used in radiology to Software reviews evaluate the performance of many radiological tests. Although one does not necessar- Statistical analysis ily need to understand the complicated mathematical equations and theories of ROC analysis, understanding the key concepts of ROC analysis is a prerequisite for the correct use and interpretation of the results that it provides. -
The Statistical Significance of Randomized Controlled Trial Results Is Frequently Fragile: a Case for a Fragility Index
The statistical significance of randomized controlled trial results is frequently fragile: A case for a Fragility Index Walsh, M., Srinathan, S. K., McAuley, D. F., Mrkobrada, M., Levine, O., Ribic, C., Molnar, A. O., Dattani, N. D., Burke, A., Guyatt, G., Thabane, L., Walter, S. D., Pogue, J., & Devereaux, P. J. (2014). The statistical significance of randomized controlled trial results is frequently fragile: A case for a Fragility Index. Journal of Clinical Epidemiology, 67(6), 622-628. https://doi.org/10.1016/j.jclinepi.2013.10.019 Published in: Journal of Clinical Epidemiology Document Version: Publisher's PDF, also known as Version of record Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal Publisher rights 2014 The Authors. Published by Elsevier Inc. This is an open access article published under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/3.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited. General rights Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. -
2020 Census Barriers, Attitudes, and Motivators Study Survey Report
2020 Census Barriers, Attitudes, and Motivators Study Survey Report A New Design for the 21st Century January 24, 2019 Version 2.0 Prepared by Kyley McGeeney, Brian Kriz, Shawnna Mullenax, Laura Kail, Gina Walejko, Monica Vines, Nancy Bates, and Yazmín García Trejo 2020 Census Research | 2020 CBAMS Survey Report Page intentionally left blank. ii 2020 Census Research | 2020 CBAMS Survey Report Table of Contents List of Tables ................................................................................................................................... iv List of Figures .................................................................................................................................. iv Executive Summary ......................................................................................................................... 1 Introduction ............................................................................................................................. 3 Background .............................................................................................................................. 5 CBAMS I ......................................................................................................................................... 5 CBAMS II ........................................................................................................................................ 6 2020 CBAMS Survey Climate ........................................................................................................