MCQ SAMPLING AND SAMPLING DISTRIBUTIONS. MCQ 11.2 Any population constant is called a: (a) Statistic (b) Parameter (c) Estimate (d) Estimator
|
|
- Bertram Spencer
- 6 years ago
- Views:
Transcription
1 MCQ SAMPLING AND SAMPLING DISTRIBUTIONS MCQ 11.1 Sample is a sub-set of: (a) Population (b) Data (c) Set (d) Distribution MCQ 11.2 Any population constant is called a: (a) Statistic (b) Parameter (c) Estimate (d) Estimator MCQ 11.3 List of all the units of the population is called: (a) Random sampling (b) Bias (c) Sampling frame (d) Probability sampling MCQ 11.4 Any calculation on the sampling data is called: (a) Parameter (b) Static (c) (d) Error MCQ 11.5 Any measure of the population is called: (a) Finite (b) Parameter (c) Without replacement (d) Random MCQ 11.6 If all the units of a population are surveyed, it is called: (a) Random sample (b) Random sampling (c) Sampled population (d) Complete enumeration MCQ 11.7 Probability distribution of a statistics is called: (a) Sampling (b) Parameter (c) Data (d) Sampling distribution MCQ 11.8 The difference between a statistic and the parameter is called: (a) Probability (b) Sampling error (c) Random (d) Non-random MCQ 11.9 The sum of the frequencies of the frequency distribution of a statistic (a) Sample size (b) Population size (c) Possible samples (d) Sum of X values MCQ Standard deviation of sampling distribution of a statistic is called: (a) Serious error (b) Dispersion (c) Standard error (d) Difference MCQ If we obtain a point estimate for a population mean µ, the difference between and µ is: (a) Standard error (b) Bias (c) Error of estimation (d) Difficult to tell MCQ A distribution formed by all possible values of a statistics is called (a) Binomial distribution (b) Hypergeometric distribution (c) Normal distribution (d) Sampling distribution MCQ In probability sampling, probability of selecting an item from the population is known and is: (a) Equal to zero (b) Non zero (c) Equal to one (d) All of the above
2 MCQ A population about which we want to get some information is called: (a) Finite population (b) Infinite population (c) Sampling population (d) Target population MCQ The population consists of the results of repeated trials is named as: (a) Finite population (b) Infinite population (c) Real population (d) Hypothetical population MCQ A population consisting of the items which are all present physically is called: (a) Finite population (b) Infinite population (c) Real population (d) Hypothetical population MCQ Study of population is called: (a) Parameter (b) Statistic (c) Error (d) Census MCQ For making voters list in Pakistan we need: (a) Sampling error (b) Standard error (c) Census (d) Simple random sampling MCQ Sampling based upon equal probability is called: (a) Probability sampling (c) Simple random sampling (b) Systematic sampling (d) Stratified random sampling MCQ In sampling with replacement, an element can be chosen: (a) Less than once (b) More than once (c) Only once (d) Difficult to tell MCQ Standard deviation of sample mean without replacement standard deviation of sample mean with replacement: (a) Less than (b) More than (c) 2 times (d) Equal to MCQ In sampling without replacement, an element can be chosen: (a) Less than once (b) More than once (c) Only once (d) Difficult to tell MCQ In sampling with replacement, the following is always true: (a) n = N (b) n < N (c) n > N (d) All of the above MCQ Which of the following statement is true? (a) Standard error is always one (c) Standard error is always negative (b) Standard error is always zero (d) Standard error is always positive MCQ Random sampling is also called: (a) Probability sampling (b) Non-probability sampling (c) Sampling error (d) Random error MCQ Non-random sampling is also called: (a) Biased sampling (b) Non-probability sampling (c) Random sampling (d) Representative sample
3 MCQ Sampling error can be reducing by: (a) Non-probability sampling (c) Decreasing the sample size (b) Increasing the population (d) Increasing the sample size MCQ The selection of cricket team for the world cup is called: (a) Random sampling (b) Systematic sampling (c) Purposive sampling (d) Cluster sampling MCQ A complete list of all the sapling units is called: (a) Sampling design (b) Sampling frame (c) Population frame (d) Cluster MCQ A Plan for obtaining a sample from a population is called: (a) Population design (b) Sampling design (c) Sampling frame (d) Sampling distribution MCQ If a survey is conducted by a sampling design is called: (a) Sample survey (b) Population survey (c) Systematic survey (d) None MCQ The difference between the expected value of a statistic and the value of the parameter being estimated is called a: (a) Sampling error (b) Non-sampling error (c) Standard error (d) Bias MCQ The standard deviation of any sampling distribution is called: (a) Standard error (b) Non-sampling error (c) Type- I error (d) Type II-error MCQ Which of the following statement is not true? (a) S.E( ) 0 (b) S.E( ) 1 (c) S.E( ) = -2 (d) All of the above MCQ The standard error increases when sample size is: (a) Increase (b) Decrease (c) Fixed (d) More than 30 MCQ The mean of sampling distribution of mean (a) (b) µ (c) p (d) None of the above MCQ The mean of the sample means is exactly equal to the: (a) Sample mean (b) Population mean (c) Weighted mean (d) Combined mean MCQ (a) E( ) (b) µ (c) Both (a) and (b) (d) None of the above MCQ A sample which is free from bias is called: (a) Biased (b) Unbiased (c) Positively biased (d) Negatively biased MCQ If E( ) = µ then bias is: (a) Positive (b) Negative (c) Zero (d) 100%
4 MCQ (a) Unbiased sample variance (b) Population variance (c) Biased sample variance (d) All of the above MCQ (a) Unbiased sample variance (b) True variance (c) Biased sample variance (d) Variance of means MCQ The sampling procedure in which the population is first divided into homogenous groups and then a sample is drawn from each group is called: (a) Probability sampling (b) Simple random sampling (c) Stratified random sampling (d) Sampling with replacement MCQ When a random sample is drawn from each stratum, it is known as: (a) Simple random sampling (b) Stratified random sampling (c) Probability sampling (d) Purposive sampling MCQ When the procedure of selecting the elements from the population is not based on probability is known as: (a) Purposive sampling (b) Judgment sampling (c) Subjective sampling (d) All of the above MCQ Suppose a finite population has 6 items and 2 items are selected at random without replacement, then all possible samples will be: (a) 6 (b) 12 (c) 15 (d) 36 MCQ Suppose a finite population contains 7 items and 3 items are selected at random without replacement, then all possible samples will be: (a) 21 (b) 35 (c) 14 (d) 7 MCQ A population contain N item and all possible sample of size n are selected without replacement. The possible number of sample will be: (a) N (b) n N (c) N C n (d) N n MCQ Suppose a finite population contains 4 items and 2 items are selected at random with replacement, then all possible samples will be: (a) 6 (b) 16 (c) 8 (d) 4 MCQ A population contains 2 items and 4 items are selected at random with replacement, then all possible samples will be: (a) 16 (b) 8 (c) 4 C 2 (d) 4 MCQ Suppose a population has N items and n items are selected with replacement. Number of all possible samples will be: (a) N n (b) N C n (c) N (d) n MCQ In random sampling, the probability of selecting an item from the population is: (a) Unknown (b) Known (c) Un-decided (d) One MCQ If N is the size of the population and n is size of the sample, then sampling fraction is: (a) n N (b) N n (c) n/n (d) N C n
5 MCQ The finite population correction factor is: MCQ In sampling with replacement, the standard error of MCQ MCQ In sampling with replacement, the standard error of sample proportion MCQ MCQ If E( ) = 10 and µ = 10 then bias (a) 0 (b) 10 (c) 20 (d) Difficult to tell MCQ If = 10 and µ = 12 then sampling error (a) 22 (b) 10 (c) 12 (d) 2 MCQ The standard deviation of the distribution of sample means MCQ If n = 25, = 25 and = 25, then standard error of will be: (a) 25 (b) 5 (c) 1 (d) 0 MCQ If E(s 2 ) = 3 and = 2 then bias will be: (a) 5 (b) 3 (c) 2 (d) 1 MCQ In sampling without replacement, the standard error of sampling distribution of sample proportion to: is equal MCQ When sampling is done without replacement
6 MCQ In case of sampling with replacement MCQ The distribution of the mean of sample of size 4, taken from a population with a standard deviation, has a standard deviation of: MCQ In sampling with replacement MCQ When sampling is done with or without replacement, E( MCQ In case of sampling with replacement, Ε (S²) MCQ In sampling without replacement, the expected value of is S² MCQ When the sampling is done with replacement, then µ S2 MCQ In sampling without replacement, µ s² MCQ When sampling is done with or without replacement, MCQ If X represent the number of units having the specified characteristic and n is the size of the sample, then population proportion p MCQ If X represents the number of units having the specified characteristic and N is the size of the population, then population proportion p
Probability and Statistics Curriculum Pacing Guide
Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Ch 2 Test Remediation Work Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide an appropriate response. 1) High temperatures in a certain
More informationCorpus Linguistics (L615)
(L615) Basics of Markus Dickinson Department of, Indiana University Spring 2013 1 / 23 : the extent to which a sample includes the full range of variability in a population distinguishes corpora from archives
More informationSTA 225: Introductory Statistics (CT)
Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic
More informationAlgebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview
Algebra 1, Quarter 3, Unit 3.1 Line of Best Fit Overview Number of instructional days 6 (1 day assessment) (1 day = 45 minutes) Content to be learned Analyze scatter plots and construct the line of best
More informationSimple Random Sample (SRS) & Voluntary Response Sample: Examples: A Voluntary Response Sample: Examples: Systematic Sample Best Used When
Simple Random Sample (SRS) & Voluntary Response Sample: In statistics, a simple random sample is a group of people who have been chosen at random from the general population. A simple random sample is
More informationSTAT 220 Midterm Exam, Friday, Feb. 24
STAT 220 Midterm Exam, Friday, Feb. 24 Name Please show all of your work on the exam itself. If you need more space, use the back of the page. Remember that partial credit will be awarded when appropriate.
More informationNCEO Technical Report 27
Home About Publications Special Topics Presentations State Policies Accommodations Bibliography Teleconferences Tools Related Sites Interpreting Trends in the Performance of Special Education Students
More informationEdexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE
Edexcel GCSE Statistics 1389 Paper 1H June 2007 Mark Scheme Edexcel GCSE Statistics 1389 NOTES ON MARKING PRINCIPLES 1 Types of mark M marks: method marks A marks: accuracy marks B marks: unconditional
More informationCHAPTER III RESEARCH METHOD
CHAPTER III RESEARCH METHOD A. Research Method 1. Research Design In this study, the researcher uses an experimental with the form of quasi experimental design, the researcher used because in fact difficult
More informationLecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
More informationSchool Competition and Efficiency with Publicly Funded Catholic Schools David Card, Martin D. Dooley, and A. Abigail Payne
School Competition and Efficiency with Publicly Funded Catholic Schools David Card, Martin D. Dooley, and A. Abigail Payne Web Appendix See paper for references to Appendix Appendix 1: Multiple Schools
More informationMathematics (JUN14MS0401) General Certificate of Education Advanced Level Examination June Unit Statistics TOTAL.
Centre Number Candidate Number For Examiner s Use Surname Other Names Candidate Signature Examiner s Initials Mathematics Unit Statistics 4 Tuesday 24 June 2014 General Certificate of Education Advanced
More informationPre-Algebra A. Syllabus. Course Overview. Course Goals. General Skills. Credit Value
Syllabus Pre-Algebra A Course Overview Pre-Algebra is a course designed to prepare you for future work in algebra. In Pre-Algebra, you will strengthen your knowledge of numbers as you look to transition
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationUniversityy. The content of
WORKING PAPER #31 An Evaluation of Empirical Bayes Estimation of Value Added Teacher Performance Measuress Cassandra M. Guarino, Indianaa Universityy Michelle Maxfield, Michigan State Universityy Mark
More informationThe Good Judgment Project: A large scale test of different methods of combining expert predictions
The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania
More informationTheory of Probability
Theory of Probability Class code MATH-UA 9233-001 Instructor Details Prof. David Larman Room 806,25 Gordon Street (UCL Mathematics Department). Class Details Fall 2013 Thursdays 1:30-4-30 Location to be
More informationAlgebra 2- Semester 2 Review
Name Block Date Algebra 2- Semester 2 Review Non-Calculator 5.4 1. Consider the function f x 1 x 2. a) Describe the transformation of the graph of y 1 x. b) Identify the asymptotes. c) What is the domain
More informationA Program Evaluation of Connecticut Project Learning Tree Educator Workshops
A Program Evaluation of Connecticut Project Learning Tree Educator Workshops Jennifer Sayers Dr. Lori S. Bennear, Advisor May 2012 Masters project submitted in partial fulfillment of the requirements for
More informationTitle:A Flexible Simulation Platform to Quantify and Manage Emergency Department Crowding
Author's response to reviews Title:A Flexible Simulation Platform to Quantify and Manage Emergency Department Crowding Authors: Joshua E Hurwitz (jehurwitz@ufl.edu) Jo Ann Lee (joann5@ufl.edu) Kenneth
More informationChapters 1-5 Cumulative Assessment AP Statistics November 2008 Gillespie, Block 4
Chapters 1-5 Cumulative Assessment AP Statistics Name: November 2008 Gillespie, Block 4 Part I: Multiple Choice This portion of the test will determine 60% of your overall test grade. Each question is
More information15-year-olds enrolled full-time in educational institutions;
CHAPTER 4 SAMPLE DESIGN TARGET POPULATION AND OVERVIEW OF THE SAMPLING DESIGN The desired base PISA target population in each country consisted of 15-year-old students attending educational institutions
More informationWhy Did My Detector Do That?!
Why Did My Detector Do That?! Predicting Keystroke-Dynamics Error Rates Kevin Killourhy and Roy Maxion Dependable Systems Laboratory Computer Science Department Carnegie Mellon University 5000 Forbes Ave,
More informationS T A T 251 C o u r s e S y l l a b u s I n t r o d u c t i o n t o p r o b a b i l i t y
Department of Mathematics, Statistics and Science College of Arts and Sciences Qatar University S T A T 251 C o u r s e S y l l a b u s I n t r o d u c t i o n t o p r o b a b i l i t y A m e e n A l a
More informationThe Effects of Ability Tracking of Future Primary School Teachers on Student Performance
The Effects of Ability Tracking of Future Primary School Teachers on Student Performance Johan Coenen, Chris van Klaveren, Wim Groot and Henriëtte Maassen van den Brink TIER WORKING PAPER SERIES TIER WP
More informationIndividual Differences & Item Effects: How to test them, & how to test them well
Individual Differences & Item Effects: How to test them, & how to test them well Individual Differences & Item Effects Properties of subjects Cognitive abilities (WM task scores, inhibition) Gender Age
More informationMathacle PSet Stats, Concepts in Statistics and Probability Level Number Name: Date:
1 st Quarterly Exam ~ Sampling, Designs, Exploring Data and Regression Part 1 Review I. SAMPLING MC I-1.) [APSTATSMC2014-6M] Approximately 52 percent of all recent births were boys. In a simple random
More informationProbability Therefore (25) (1.33)
Probability We have intentionally included more material than can be covered in most Student Study Sessions to account for groups that are able to answer the questions at a faster rate. Use your own judgment,
More informationExploration. CS : Deep Reinforcement Learning Sergey Levine
Exploration CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?
More informationCertified Six Sigma Professionals International Certification Courses in Six Sigma Green Belt
Certification Singapore Institute Certified Six Sigma Professionals Certification Courses in Six Sigma Green Belt ly Licensed Course for Process Improvement/ Assurance Managers and Engineers Leading the
More informationAN ANALYSIS OF GRAMMTICAL ERRORS MADE BY THE SECOND YEAR STUDENTS OF SMAN 5 PADANG IN WRITING PAST EXPERIENCES
AN ANALYSIS OF GRAMMTICAL ERRORS MADE BY THE SECOND YEAR STUDENTS OF SMAN 5 PADANG IN WRITING PAST EXPERIENCES Yelna Oktavia 1, Lely Refnita 1,Ernati 1 1 English Department, the Faculty of Teacher Training
More informationStatistical Studies: Analyzing Data III.B Student Activity Sheet 7: Using Technology
Suppose data were collected on 25 bags of Spud Potato Chips. The weight (to the nearest gram) of the chips in each bag is listed below. 25 28 23 26 23 25 25 24 24 27 23 24 28 27 24 26 24 25 27 26 25 26
More informationIntroduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition
Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and
More informationThe lab is designed to remind you how to work with scientific data (including dealing with uncertainty) and to review experimental design.
Name: Partner(s): Lab #1 The Scientific Method Due 6/25 Objective The lab is designed to remind you how to work with scientific data (including dealing with uncertainty) and to review experimental design.
More informationLecture 2: Quantifiers and Approximation
Lecture 2: Quantifiers and Approximation Case study: Most vs More than half Jakub Szymanik Outline Number Sense Approximate Number Sense Approximating most Superlative Meaning of most What About Counting?
More informationArtificial Neural Networks written examination
1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14
More informationSector Differences in Student Learning: Differences in Achievement Gains Across School Years and During the Summer
Catholic Education: A Journal of Inquiry and Practice Volume 7 Issue 2 Article 6 July 213 Sector Differences in Student Learning: Differences in Achievement Gains Across School Years and During the Summer
More informationA Comparison of Charter Schools and Traditional Public Schools in Idaho
A Comparison of Charter Schools and Traditional Public Schools in Idaho Dale Ballou Bettie Teasley Tim Zeidner Vanderbilt University August, 2006 Abstract We investigate the effectiveness of Idaho charter
More information4-3 Basic Skills and Concepts
4-3 Basic Skills and Concepts Identifying Binomial Distributions. In Exercises 1 8, determine whether the given procedure results in a binomial distribution. For those that are not binomial, identify at
More informationClass-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification
Class-Discriminative Weighted Distortion Measure for VQ-Based Speaker Identification Tomi Kinnunen and Ismo Kärkkäinen University of Joensuu, Department of Computer Science, P.O. Box 111, 80101 JOENSUU,
More informationGrade 6: Correlated to AGS Basic Math Skills
Grade 6: Correlated to AGS Basic Math Skills Grade 6: Standard 1 Number Sense Students compare and order positive and negative integers, decimals, fractions, and mixed numbers. They find multiples and
More informationTask Types. Duration, Work and Units Prepared by
Task Types Duration, Work and Units Prepared by 1 Introduction Microsoft Project allows tasks with fixed work, fixed duration, or fixed units. Many people ask questions about changes in these values when
More informationMGT/MGP/MGB 261: Investment Analysis
UNIVERSITY OF CALIFORNIA, DAVIS GRADUATE SCHOOL OF MANAGEMENT SYLLABUS for Fall 2014 MGT/MGP/MGB 261: Investment Analysis Daytime MBA: Tu 12:00p.m. - 3:00 p.m. Location: 1302 Gallagher (CRN: 51489) Sacramento
More informationUnderstanding and Interpreting the NRC s Data-Based Assessment of Research-Doctorate Programs in the United States (2010)
Understanding and Interpreting the NRC s Data-Based Assessment of Research-Doctorate Programs in the United States (2010) Jaxk Reeves, SCC Director Kim Love-Myers, SCC Associate Director Presented at UGA
More informationA Bootstrapping Model of Frequency and Context Effects in Word Learning
Cognitive Science 41 (2017) 590 622 Copyright 2016 Cognitive Science Society, Inc. All rights reserved. ISSN: 0364-0213 print / 1551-6709 online DOI: 10.1111/cogs.12353 A Bootstrapping Model of Frequency
More informationRemainder Rules. 3. Ask students: How many carnations can you order and what size bunches do you make to take five carnations home?
Math Concepts whole numbers multiplication division subtraction addition Materials TI-10, TI-15 Explorer recording sheets cubes, sticks, etc. pencils Overview Students will use calculators, whole-number
More informationEntrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany
Entrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany Jana Kitzmann and Dirk Schiereck, Endowed Chair for Banking and Finance, EUROPEAN BUSINESS SCHOOL, International
More informationAn Introduction to Simio for Beginners
An Introduction to Simio for Beginners C. Dennis Pegden, Ph.D. This white paper is intended to introduce Simio to a user new to simulation. It is intended for the manufacturing engineer, hospital quality
More informationRunning head: DELAY AND PROSPECTIVE MEMORY 1
Running head: DELAY AND PROSPECTIVE MEMORY 1 In Press at Memory & Cognition Effects of Delay of Prospective Memory Cues in an Ongoing Task on Prospective Memory Task Performance Dawn M. McBride, Jaclyn
More informationLongitudinal Analysis of the Effectiveness of DCPS Teachers
F I N A L R E P O R T Longitudinal Analysis of the Effectiveness of DCPS Teachers July 8, 2014 Elias Walsh Dallas Dotter Submitted to: DC Education Consortium for Research and Evaluation School of Education
More informationA Version Space Approach to Learning Context-free Grammars
Machine Learning 2: 39~74, 1987 1987 Kluwer Academic Publishers, Boston - Manufactured in The Netherlands A Version Space Approach to Learning Context-free Grammars KURT VANLEHN (VANLEHN@A.PSY.CMU.EDU)
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationHow to Judge the Quality of an Objective Classroom Test
How to Judge the Quality of an Objective Classroom Test Technical Bulletin #6 Evaluation and Examination Service The University of Iowa (319) 335-0356 HOW TO JUDGE THE QUALITY OF AN OBJECTIVE CLASSROOM
More informationEssentials of Ability Testing. Joni Lakin Assistant Professor Educational Foundations, Leadership, and Technology
Essentials of Ability Testing Joni Lakin Assistant Professor Educational Foundations, Leadership, and Technology Basic Topics Why do we administer ability tests? What do ability tests measure? How are
More informationIntroduction to the Practice of Statistics
Chapter 1: Looking at Data Distributions Introduction to the Practice of Statistics Sixth Edition David S. Moore George P. McCabe Bruce A. Craig Statistics is the science of collecting, organizing and
More informationEvaluation of Teach For America:
EA15-536-2 Evaluation of Teach For America: 2014-2015 Department of Evaluation and Assessment Mike Miles Superintendent of Schools This page is intentionally left blank. ii Evaluation of Teach For America:
More informationProfessional Development and Incentives for Teacher Performance in Schools in Mexico. Gladys Lopez-Acevedo (LCSPP)*
Public Disclosure Authorized Professional Development and Incentives for Teacher Performance in Schools in Mexico Gladys Lopez-Acevedo (LCSPP)* Gacevedo@worldbank.org Public Disclosure Authorized Latin
More informationThe Efficacy of PCI s Reading Program - Level One: A Report of a Randomized Experiment in Brevard Public Schools and Miami-Dade County Public Schools
The Efficacy of PCI s Reading Program - Level One: A Report of a Randomized Experiment in Brevard Public Schools and Miami-Dade County Public Schools Megan Toby Boya Ma Andrew Jaciw Jessica Cabalo Empirical
More informationTeacher Quality and Value-added Measurement
Teacher Quality and Value-added Measurement Dan Goldhaber University of Washington and The Urban Institute dgoldhab@u.washington.edu April 28-29, 2009 Prepared for the TQ Center and REL Midwest Technical
More informationChallenges in Deep Reinforcement Learning. Sergey Levine UC Berkeley
Challenges in Deep Reinforcement Learning Sergey Levine UC Berkeley Discuss some recent work in deep reinforcement learning Present a few major challenges Show some of our recent work toward tackling
More information(Sub)Gradient Descent
(Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include
More informationw o r k i n g p a p e r s
w o r k i n g p a p e r s 2 0 0 9 Assessing the Potential of Using Value-Added Estimates of Teacher Job Performance for Making Tenure Decisions Dan Goldhaber Michael Hansen crpe working paper # 2009_2
More informationPeer Influence in Educational Reform A System Dynamics Approach
Peer Influence in Educational Reform A System Dynamics Approach Don R. Morris Miami-Dade County Public Schools 1500 Biscayne Blvd., Ste. 225, Miami, Florida 33132 Tel: 1 305 995 7531 / Fax: 1 305 995 7521
More informationListening and Speaking Skills of English Language of Adolescents of Government and Private Schools
Listening and Speaking Skills of English Language of Adolescents of Government and Private Schools Dr. Amardeep Kaur Professor, Babe Ke College of Education, Mudki, Ferozepur, Punjab Abstract The present
More informationSETTING STANDARDS FOR CRITERION- REFERENCED MEASUREMENT
SETTING STANDARDS FOR CRITERION- REFERENCED MEASUREMENT By: Dr. MAHMOUD M. GHANDOUR QATAR UNIVERSITY Improving human resources is the responsibility of the educational system in many societies. The outputs
More informationSouth Carolina College- and Career-Ready Standards for Mathematics. Standards Unpacking Documents Grade 5
South Carolina College- and Career-Ready Standards for Mathematics Standards Unpacking Documents Grade 5 South Carolina College- and Career-Ready Standards for Mathematics Standards Unpacking Documents
More informationKathryn C. Monahan & J. David Hawkins & Robert D. Abbott
DOI 10.1007/s11121-012-0298-x The Application of Meta-analysis within a Matched-pair Randomized Control Trial: An Illustration Testing the Effects of Communities That Care on Delinquent Behavior Kathryn
More informationACADEMIC AFFAIRS GUIDELINES
ACADEMIC AFFAIRS GUIDELINES Section 8: General Education Title: General Education Assessment Guidelines Number (Current Format) Number (Prior Format) Date Last Revised 8.7 XIV 09/2017 Reference: BOR Policy
More informationPROMOTING QUALITY AND EQUITY IN EDUCATION: THE IMPACT OF SCHOOL LEARNING ENVIRONMENT
Fourth Meeting of the EARLI SIG Educational Effectiveness "Marrying rigour and relevance: Towards effective education for all University of Southampton, UK 27-29 August, 2014 PROMOTING QUALITY AND EQUITY
More informationSURVEILLANCE OF SCHOOL VIOLENCE, INJURY, AND DISCIPLINARY ACTIONS
Psychology in the Schools, Vol. 38(2), 2001 2001 John Wiley & Sons, Inc. SURVEILLANCE OF SCHOOL VIOLENCE, INJURY, AND DISCIPLINARY ACTIONS PAUL M. KINGERY MARK B. COGGESHALL Hamilton Fish Institute The
More informationUnraveling symbolic number processing and the implications for its association with mathematics. Delphine Sasanguie
Unraveling symbolic number processing and the implications for its association with mathematics Delphine Sasanguie 1. Introduction Mapping hypothesis Innate approximate representation of number (ANS) Symbols
More informationManagerial Decision Making
Course Business Managerial Decision Making Session 4 Conditional Probability & Bayesian Updating Surveys in the future... attempt to participate is the important thing Work-load goals Average 6-7 hours,
More informationGender and socioeconomic differences in science achievement in Australia: From SISS to TIMSS
Gender and socioeconomic differences in science achievement in Australia: From SISS to TIMSS, Australian Council for Educational Research, thomson@acer.edu.au Abstract Gender differences in science amongst
More informationKnowledge Transfer in Deep Convolutional Neural Nets
Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract
More informationGCE. Mathematics (MEI) Mark Scheme for June Advanced Subsidiary GCE Unit 4766: Statistics 1. Oxford Cambridge and RSA Examinations
GCE Mathematics (MEI) Advanced Subsidiary GCE Unit 4766: Statistics 1 Mark Scheme for June 2013 Oxford Cambridge and RSA Examinations OCR (Oxford Cambridge and RSA) is a leading UK awarding body, providing
More informationSoftprop: Softmax Neural Network Backpropagation Learning
Softprop: Softmax Neural Networ Bacpropagation Learning Michael Rimer Computer Science Department Brigham Young University Provo, UT 84602, USA E-mail: mrimer@axon.cs.byu.edu Tony Martinez Computer Science
More informationThe Challenges Associated with Relying on CAPI Interviewers to Implement Novel Field Procedures
The Challenges Associated with Relying on CAPI Interviewers to Implement Novel Field Procedures Gina Walejko, U.S. Census Bureau James Wagner, University of Michigan American Association for Public Opinion
More informationSaeed Rajaeepour Associate Professor, Department of Educational Sciences. Seyed Ali Siadat Professor, Department of Educational Sciences
Investigating and Comparing Primary, Secondary, and High School Principals and Teachers Attitudes in the City of Isfahan towards In-Service Training Courses Masoud Foroutan (Corresponding Author) PhD Student
More informationSchool Size and the Quality of Teaching and Learning
School Size and the Quality of Teaching and Learning An Analysis of Relationships between School Size and Assessments of Factors Related to the Quality of Teaching and Learning in Primary Schools Undertaken
More informationRedirected Inbound Call Sampling An Example of Fit for Purpose Non-probability Sample Design
Redirected Inbound Call Sampling An Example of Fit for Purpose Non-probability Sample Design Burton Levine Karol Krotki NISS/WSS Workshop on Inference from Nonprobability Samples September 25, 2017 RTI
More informationMathematics subject curriculum
Mathematics subject curriculum Dette er ei omsetjing av den fastsette læreplanteksten. Læreplanen er fastsett på Nynorsk Established as a Regulation by the Ministry of Education and Research on 24 June
More informationUniversiteit Leiden ICT in Business
Universiteit Leiden ICT in Business Ranking of Multi-Word Terms Name: Ricardo R.M. Blikman Student-no: s1184164 Internal report number: 2012-11 Date: 07/03/2013 1st supervisor: Prof. Dr. J.N. Kok 2nd supervisor:
More informationMonitoring and Evaluating Curriculum Implementation Final Evaluation Report on the Implementation of The New Zealand Curriculum Report to
Monitoring and Evaluating Curriculum Implementation Final Evaluation Report on the Implementation of The New Zealand Curriculum 2008-2009 Report to the Ministry of Education Dr Claire Sinnema The University
More informationMGF 1106 Final Exam Review / (sections )
MGF 1106 Final Exam Review / (sections ---------) Time of Common Final Exam: Place of Common Final Exam (Sections ----------- only): --------------- Those students with a final exam conflict (with another
More informationMandarin Lexical Tone Recognition: The Gating Paradigm
Kansas Working Papers in Linguistics, Vol. 0 (008), p. 8 Abstract Mandarin Lexical Tone Recognition: The Gating Paradigm Yuwen Lai and Jie Zhang University of Kansas Research on spoken word recognition
More informationSouth Carolina English Language Arts
South Carolina English Language Arts A S O F J U N E 2 0, 2 0 1 0, T H I S S TAT E H A D A D O P T E D T H E CO M M O N CO R E S TAT E S TA N DA R D S. DOCUMENTS REVIEWED South Carolina Academic Content
More informationCONSTRUCTION OF AN ACHIEVEMENT TEST Introduction One of the important duties of a teacher is to observe the student in the classroom, laboratory and
CONSTRUCTION OF AN ACHIEVEMENT TEST Introduction One of the important duties of a teacher is to observe the student in the classroom, laboratory and in other settings. He may also make use of tests in
More informationAn empirical study of learning speed in backpropagation
Carnegie Mellon University Research Showcase @ CMU Computer Science Department School of Computer Science 1988 An empirical study of learning speed in backpropagation networks Scott E. Fahlman Carnegie
More informationHindsight Bias From 3 to 95 Years of Age
THIS ARTICLE HAS BEEN CORRECTED. SEE LAST PAGE Journal of Experimental Psychology: Learning, Memory, and Cognition 2011, Vol. 37, No. 2, 378 391 2011 American Psychological Association 0278-7393/11/$12.00
More informationBENCHMARK TREND COMPARISON REPORT:
National Survey of Student Engagement (NSSE) BENCHMARK TREND COMPARISON REPORT: CARNEGIE PEER INSTITUTIONS, 2003-2011 PREPARED BY: ANGEL A. SANCHEZ, DIRECTOR KELLI PAYNE, ADMINISTRATIVE ANALYST/ SPECIALIST
More informationDiagnostic Test. Middle School Mathematics
Diagnostic Test Middle School Mathematics Copyright 2010 XAMonline, Inc. All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by
More informationJulia Smith. Effective Classroom Approaches to.
Julia Smith @tessmaths Effective Classroom Approaches to GCSE Maths resits julia.smith@writtle.ac.uk Agenda The context of GCSE resit in a post-16 setting An overview of the new GCSE Key features of a
More informationABILITY SORTING AND THE IMPORTANCE OF COLLEGE QUALITY TO STUDENT ACHIEVEMENT: EVIDENCE FROM COMMUNITY COLLEGES
ABILITY SORTING AND THE IMPORTANCE OF COLLEGE QUALITY TO STUDENT ACHIEVEMENT: EVIDENCE FROM COMMUNITY COLLEGES Kevin Stange Ford School of Public Policy University of Michigan Ann Arbor, MI 48109-3091
More informationISFA2008U_120 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM
Proceedings of 28 ISFA 28 International Symposium on Flexible Automation Atlanta, GA, USA June 23-26, 28 ISFA28U_12 A SCHEDULING REINFORCEMENT LEARNING ALGORITHM Amit Gil, Helman Stern, Yael Edan, and
More informationEarnings Functions and Rates of Return
DISCUSSION PAPER SERIES IZA DP No. 3310 Earnings Functions and Rates of Return James J. Heckman Lance J. Lochner Petra E. Todd January 2008 Forschungsinstitut zur Zukunft der Arbeit Institute for the Study
More informationLinking the Ohio State Assessments to NWEA MAP Growth Tests *
Linking the Ohio State Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. August 2016 Introduction Northwest Evaluation Association (NWEA
More informationSeminar - Organic Computing
Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts
More informationRole Models, the Formation of Beliefs, and Girls Math. Ability: Evidence from Random Assignment of Students. in Chinese Middle Schools
Role Models, the Formation of Beliefs, and Girls Math Ability: Evidence from Random Assignment of Students in Chinese Middle Schools Alex Eble and Feng Hu February 2017 Abstract This paper studies the
More information