Analysis of Enzyme Kinetic Data
|
|
- Shanna Murphy
- 6 years ago
- Views:
Transcription
1 Analysis of Enzyme Kinetic Data
2 To Marilú
3 Analysis of Enzyme Kinetic Data ATHEL CORNISH-BOWDEN Directeur de Recherche Émérite, Centre National de la Recherche Scientifique, Marseilles OXFORD UNIVERSITY PRESS 1995 This file was prepared for distribution after the printed edition of the book went out of print in It was prepared from the original files sent by the author to the publisher, not from the files after editing the the publisher s office and actually used for printing the book. There may therefore be minor variations in wording from the published version, and, more important, there may be some errors that were in the original files but which were corrected during printing of the book. In any case I should be grateful to have errors brought to my attention. At present my address is acornish@imm.cnrs.fr. However, this changes from time to time so you may need to consult my web site at to determine how to contact me. The pagination in this file follows that in the book, so p. 100, for example, contains the same material as appears on p. 100 of the book. That is why most pages end in the middle of a line. This file is Athel Cornish-Bowden However, permission is hereby granted to make use of it for personal study, research, teaching, etc., without restriction. It will be appreciated, however, if any use of it in published work is apopropriately acknowledged therein by referring to the original book. As the computer program Leonora included with the book was written for an operating system that is now obsolete, the program itself has become difficult to use, and unappealing to people who are more familiar with more modern operating systems. Chapters 8 11 of the book are therefore omitted from this file. At the moment the figures are scanned from the book (as the original files used to create them proved to be unusable). In due course, and if there is sufficient interest, I shall replace them with better versions, and I shall also try to root out and correct errors in the text. Whether this happens or not will depend on whether I get any feedback. If no one writes to tell me that they have found this file useful (or otherwise) then it will remain for ever in the form you see now. Version 1.2, 16 November 2004 (minor corrections to this page, 19 October 2009, 28 August 2014)
4 Preface This is a book that I have wanted to write (the first six chapters, at least) for many years, and, indeed, I made a start on an ancestral version during a sabbatical in However, it soon became clear that a short book on the theory of data analysis in enzymology would have very limited appeal, and for this and various other reasons the original project did not advance very far. The arrival of the personal computer has completely transformed the world of scientific computing, however, to the point where virtually every working scientist is now also a computer user. As a result, it has become quite feasible to incorporate all of the methods of analysis developed in the 1960s and 1970s into a single program and to present both the methods and the program in a single book. The two principal parts of the book are largely independent of one another, with only a short link section (Chapter 7) between them: the first six chapters provide a theoretical account of statistical analysis of kinetic data for enzyme-catalysed reactions in the steady state; the last four describe Leonora, a program for analysing enzyme kinetic data on the IBM PC and compatible computers. Each of these parts can be read almost independently of the other, and each makes very little reference to the other. One may reasonably ask, therefore, why they have been bound between the same pair of covers and offered as a single book. The answer is that although they can be read in isolation from one another, that is far from being the best way to proceed. Something that will strike anyone who pays more than passing attention to statistics journals is that the number of statistical methods that have been proposed for scientists and engineers to use is much larger than the number of such methods that are actually in use by scientists or engineers. This is less true of methods proposed in journals that are normally read by their potential users, but it is still true to some degree of methods of kinetic data analysis that one can find in the biochemical literature. It is one thing to be reasonably convinced by a research article that a new method is better than existing ones, but it is quite another to go out and use it in the laboratory if one has to develop it from nothing. The reason for adding the practical part of this book (and the accompanying software) to the theoretical part, therefore, is to provide the tools necessary for the reader to test and apply all of the theory. This leaves unanswered, however, the complementary question of why the user of the software would want to be bothered with the theory. The
5 reason is in the sort of program that Leonora is. It does not follow the philosophy of assuming that there is One True Way of analysing data that must be applied in all circumstances. On the contrary, it offers a great deal of choice to its users, though to avoid making use too difficult it makes its own (i.e. my) choices when others are not made. To make appropriate choices the user needs a theoretical point of reference. Moreover, when I started writing Leonora I intended it to permit use of virtually any method the user might wish to try, but in practice the number of possibilities is almost infinite, because the more choices allowed, the more sub-choices these imply, etc. Consequently Leonora does make some restrictions, but to know why these restrictions apply rather than others one again needs a point of reference. Not all of the methods Leonora offers are in my opinion good methods, and even if they were it would be reasonable to ask what is the point of offering so many. This comes back to the question of user choice: far too many programs of all kinds are written in the spirit of the One True Way, and when they tolerate different preferences from those of the programmer they may force them to be specified every time the program is used. In the case of enzyme kinetics some of the most widely used methods come into the category of bad methods, but the solution is not to forbid their use if potential users don t find the methods they find most natural they won t continue to be users but to try to persuade users that better methods exist that are just as convenient. The ideal, in my view, is not only to offer a choice, but also to offer users who don t want to avail themselves of the choice a default method that will work well in most circumstances. Anyone using Leonora to analyse results of research experiments will, in all likelihood, settle quite soon on one method of analysis and ignore the others. Not everyone is a resarcher, however, and for teaching the principles of data analysis there is a more obvious need for a program that will allow the use and comparison of many different methods. Leonora is intended to address this need. I am grateful to Faculty of Sciences of the University of Chile for appointing me on two occasions to the visiting Chair set up in memory of the late Professor Hermann Niemeyer Fernández, and to the members of the Laboratory of Biochemistry in Santiago for providing me with the opportunity to do much of the work on this book there. Work on some of the methods described in the book benefitted greatly from collaborations with Robert Eisenthal and Laszlo Endrenyi, and I thank both of them for this. Finally, I thank Véronique Raphel for allowing me to use data from her doctoral thesis as the practical example around which Chapter 7 is written.
6 Contents I. THEORY 1. Least-Squares Analysis: Basic Principles 1.1 The Statistical Approach to Data Analysis 1.2 The Continuing Importance of Graphs 1.3 True Values, Population Values, Observed Values and Estimates 1.4 Variance 1.5 Weighting 1.6 Fitting the Straight Line 1.7 Degrees of Freedom 1.8 Choice of Dependent Variable 2. Fitting the Michaelis Menten Equation by Least Squares 2.1 Linearization of the Michaelis Menten Equation 2.2 Corresponding Results for the Double-Reciprocal Plot 2.3 Choosing the Proper Weights for the Rate 2.4 Standard Errors of Michaelis Menten Parameters 3. More than Two Parameters 3.1 The General Linear Model 3.2 Standard Errors in the General Linear Model 3.3 Application to Enzyme Inhibition and Other Kinetic Examples 3.4 Comparing Models 3.5 Additional Remarks about Residual Plots 3.6 Use of Replicate Observations 4. Maximum Likelihood and Efficiency 4.1 The Theoretical Basis of Least Squares 4.2 Minimum Variance 4.3 The Normal Distribution 4.4 How Normal is the Normal Distribution? 4.5 Efficiency 4.6 The Central Limit Theorem 4.7 Review of Assumptions Implicit in Least Squares
7 5. Generalized Medians: Looking beyond Least Squares 5.1 Doing without Information on Distributions and Weights 5.2 Median Estimate of the Slope of a Straight Line 5.3 Confidence Limits for Median Slope Estimates 5.4 Relationship between Least-squares and Median Estimates 5.5 Median Estimates of Michaelis Menten Parameters 5.6. Least Absolutes Fitting 6. Robust Regression 6.1 Recognizing and Dealing with Outliers 6.2 Biweight Regression 6.3 Assessing Heteroscedasticity 6.4 Worked Example of Robust Regression 6.5 The Jackknife and Bootstrap 6.6 Minimax Fitting 6.7 Reading the Statistics Literature 7. Analysis of an Example II. INTERLUDE 7.1 Introduction: Acylaminoacyl-peptidase 7.2 Preliminary Examination of the Data 7.3 Inhibition by Acetyl-L-alanine 7.4 Inhibition by Acetyl-D-alanine 7.5 Planning Future Experiments III. PRACTICE [not included in this file] 8. Leonora: a Program for Robust Regression of Enzyme Data 8.1 Introduction 8.2 Typographical Conventions 8.3 Installation on a Hard Disk 8.4 Example 1: the Michaelis Menten Equation 8.5 Example 2: Competitive Inhibition 8.6 Two-substrate Kinetics 8.7 Example 3: ph-dependence Data 8.8 Fitting Other Equations 8.9 Screen Layout 8.10 Miscellaneous Points
8 9. Leonora Menus 9.1 Main Menu 9.2 Data Menu Editing Menu Weighting Function Window 9.3 Equation Menu Common Entries in the Equation Menu Specific Entries in the Equations Menu Adding a New Entry to the Equations Menu 9.4 Output Requirements Menu 9.5 Calculations Menu Methods Menu Weighting System Menu 9.6 Plotting Menu 9.7 Graphical Menu 9.8 Setting Defaults 10. Customizing Leonora 10.1 Introduction and Warning 10.2 Editing a Menu 10.3 Editing a Warning 10.4 Editing Other Messages 10.5 Editing Help Files 10.6 Editing the Equation File 11. Use of Simulated Data 11.1 Introduction: Generation of Pseudo-Random Numbers 11.2 Changing the Distribution of Pseudo-Random Numbers 11.3 Simulating Leonora 11.4 Entering Data 11.5 Selecting Equations 11.6 True Parameter Values, Error Parameters, Output 11.7 Methods and Weights 11.8 Results Screen One Equation, one Method Two Equations More than two Equations More than one Method Replicate Observations 11.9 Randomizer References Index
Algebra 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 informationProbability 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 informationTIMSS ADVANCED 2015 USER GUIDE FOR THE INTERNATIONAL DATABASE. Pierre Foy
TIMSS ADVANCED 2015 USER GUIDE FOR THE INTERNATIONAL DATABASE Pierre Foy TIMSS Advanced 2015 orks User Guide for the International Database Pierre Foy Contributors: Victoria A.S. Centurino, Kerry E. Cotter,
More informationOPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
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 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 informationShockwheat. Statistics 1, Activity 1
Statistics 1, Activity 1 Shockwheat Students require real experiences with situations involving data and with situations involving chance. They will best learn about these concepts on an intuitive or informal
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 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 informationA Model to Predict 24-Hour Urinary Creatinine Level Using Repeated Measurements
Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2006 A Model to Predict 24-Hour Urinary Creatinine Level Using Repeated Measurements Donna S. Kroos Virginia
More informationAGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS
AGS THE GREAT REVIEW GAME FOR PRE-ALGEBRA (CD) CORRELATED TO CALIFORNIA CONTENT STANDARDS 1 CALIFORNIA CONTENT STANDARDS: Chapter 1 ALGEBRA AND WHOLE NUMBERS Algebra and Functions 1.4 Students use algebraic
More informationCreating a Test in Eduphoria! Aware
in Eduphoria! Aware Login to Eduphoria using CHROME!!! 1. LCS Intranet > Portals > Eduphoria From home: LakeCounty.SchoolObjects.com 2. Login with your full email address. First time login password default
More informationApplication of Virtual Instruments (VIs) for an enhanced learning environment
Application of Virtual Instruments (VIs) for an enhanced learning environment Philip Smyth, Dermot Brabazon, Eilish McLoughlin Schools of Mechanical and Physical Sciences Dublin City University Ireland
More informationSchool of Innovative Technologies and Engineering
School of Innovative Technologies and Engineering Department of Applied Mathematical Sciences Proficiency Course in MATLAB COURSE DOCUMENT VERSION 1.0 PCMv1.0 July 2012 University of Technology, Mauritius
More informationTHEORY OF PLANNED BEHAVIOR MODEL IN ELECTRONIC LEARNING: A PILOT STUDY
THEORY OF PLANNED BEHAVIOR MODEL IN ELECTRONIC LEARNING: A PILOT STUDY William Barnett, University of Louisiana Monroe, barnett@ulm.edu Adrien Presley, Truman State University, apresley@truman.edu ABSTRACT
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 informationLahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017
Instructor Syed Zahid Ali Room No. 247 Economics Wing First Floor Office Hours Email szahid@lums.edu.pk Telephone Ext. 8074 Secretary/TA TA Office Hours Course URL (if any) Suraj.lums.edu.pk FINN 321 Econometrics
More informationRadius STEM Readiness TM
Curriculum Guide Radius STEM Readiness TM While today s teens are surrounded by technology, we face a stark and imminent shortage of graduates pursuing careers in Science, Technology, Engineering, and
More informationEDCI 699 Statistics: Content, Process, Application COURSE SYLLABUS: SPRING 2016
EDCI 699 Statistics: Content, Process, Application COURSE SYLLABUS: SPRING 2016 Instructor: Dr. Katy Denson, Ph.D. Office Hours: Because I live in Albuquerque, New Mexico, I won t have office hours. But
More informationHard Drive 60 GB RAM 4 GB Graphics High powered graphics Input Power /1/50/60
TRAINING SOLUTION VRTEX 360 For more information, go to: www.vrtex360.com - Register for the First Pass email newsletter. - See the demonstration event calendar. - Find out who's using VR Welding Training
More informationFoothill College Summer 2016
Foothill College Summer 2016 Intermediate Algebra Math 105.04W CRN# 10135 5.0 units Instructor: Yvette Butterworth Text: None; Beoga.net material used Hours: Online Except Final Thurs, 8/4 3:30pm Phone:
More informationCode of Practice on Freedom of Speech
Code of Practice on Freedom of Speech Rev Date Purpose of Issue / Description of Change Equality Impact Assessment Completed 1. October 2011 Initial Issue 2. 8 th June 2015 Revision version 2 28 th July
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 informationStatewide Framework Document for:
Statewide Framework Document for: 270301 Standards may be added to this document prior to submission, but may not be removed from the framework to meet state credit equivalency requirements. Performance
More informationOffice Hours: Mon & Fri 10:00-12:00. Course Description
1 State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 4 credits (3 credits lecture, 1 credit lab) Fall 2016 M/W/F 1:00-1:50 O Brian 112 Lecture Dr. Michelle Benson mbenson2@buffalo.edu
More informationDetailed course syllabus
Detailed course syllabus 1. Linear regression model. Ordinary least squares method. This introductory class covers basic definitions of econometrics, econometric model, and economic data. Classification
More informationHierarchical Linear Modeling with Maximum Likelihood, Restricted Maximum Likelihood, and Fully Bayesian Estimation
A peer-reviewed electronic journal. Copyright is retained by the first or sole author, who grants right of first publication to Practical Assessment, Research & Evaluation. Permission is granted to distribute
More informationRyerson University Sociology SOC 483: Advanced Research and Statistics
Ryerson University Sociology SOC 483: Advanced Research and Statistics Prerequisites: SOC 481 Instructor: Paul S. Moore E-mail: psmoore@ryerson.ca Office: Sociology Department Jorgenson JOR 306 Phone:
More informationGeo Risk Scan Getting grips on geotechnical risks
Geo Risk Scan Getting grips on geotechnical risks T.J. Bles & M.Th. van Staveren Deltares, Delft, the Netherlands P.P.T. Litjens & P.M.C.B.M. Cools Rijkswaterstaat Competence Center for Infrastructure,
More informationClassroom Connections Examining the Intersection of the Standards for Mathematical Content and the Standards for Mathematical Practice
Classroom Connections Examining the Intersection of the Standards for Mathematical Content and the Standards for Mathematical Practice Title: Considering Coordinate Geometry Common Core State Standards
More informationCentre for Evaluation & Monitoring SOSCA. Feedback Information
Centre for Evaluation & Monitoring SOSCA Feedback Information Contents Contents About SOSCA... 3 SOSCA Feedback... 3 1. Assessment Feedback... 4 2. Predictions and Chances Graph Software... 7 3. Value
More informationPurdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study
Purdue Data Summit 2017 Communication of Big Data Analytics New SAT Predictive Validity Case Study Paul M. Johnson, Ed.D. Associate Vice President for Enrollment Management, Research & Enrollment Information
More informationAP Calculus AB. Nevada Academic Standards that are assessable at the local level only.
Calculus AB Priority Keys Aligned with Nevada Standards MA I MI L S MA represents a Major content area. Any concept labeled MA is something of central importance to the entire class/curriculum; it is a
More informationOn-the-Fly Customization of Automated Essay Scoring
Research Report On-the-Fly Customization of Automated Essay Scoring Yigal Attali Research & Development December 2007 RR-07-42 On-the-Fly Customization of Automated Essay Scoring Yigal Attali ETS, Princeton,
More informationCourse Content Concepts
CS 1371 SYLLABUS, Fall, 2017 Revised 8/6/17 Computing for Engineers Course Content Concepts The students will be expected to be familiar with the following concepts, either by writing code to solve problems,
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 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 informationSpring 2015 IET4451 Systems Simulation Course Syllabus for Traditional, Hybrid, and Online Classes
Spring 2015 IET4451 Systems Simulation Course Syllabus for Traditional, Hybrid, and Online Classes Instructor: Dr. Gregory L. Wiles Email Address: Use D2L e-mail, or secondly gwiles@spsu.edu Office: M
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 informationCOMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS
COMPUTER-ASSISTED INDEPENDENT STUDY IN MULTIVARIATE CALCULUS L. Descalço 1, Paula Carvalho 1, J.P. Cruz 1, Paula Oliveira 1, Dina Seabra 2 1 Departamento de Matemática, Universidade de Aveiro (PORTUGAL)
More informationStorytelling Made Simple
Storytelling Made Simple Storybird is a Web tool that allows adults and children to create stories online (independently or collaboratively) then share them with the world or select individuals. Teacher
More information5 Star Writing Persuasive Essay
5 Star Writing Persuasive Essay Grades 5-6 Intro paragraph states position and plan Multiparagraphs Organized At least 3 reasons Explanations, Examples, Elaborations to support reasons Arguments/Counter
More informationDoctor in Engineering (EngD) Additional Regulations
UCL Academic Manual 2016-17 Chapter 8: Derogations and Variations Doctor in Engineering (EngD) Additional Regulations Contact: Lizzie Vinton, Assessment Regulations and Governance Manager, Academic Services,
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 informationWe will use the text, Lehninger: Principles of Biochemistry, as the primary supplement to topics presented in lecture.
Biochemical Pathways Biology 361, Spring 2014 Instructor: Office: Office Time: Email: Lecture: Text: Lecture Notes: Course Website: Gregory Johnson, Ph.D. Thompson 257d W, 10:00-11:30 and 1:00-2:00 pm
More informationDirectorate Children & Young People Policy Directive Complaints Procedure for MOD Schools
Directorate Children & Young People Policy Directive 3.2.8 Complaints Procedure for MOD Schools Version 2.0 January 2017 Preface Authorisation 1. This DCYP Policy Directive has been authorised for use
More informationPhysics 270: Experimental Physics
2017 edition Lab Manual Physics 270 3 Physics 270: Experimental Physics Lecture: Lab: Instructor: Office: Email: Tuesdays, 2 3:50 PM Thursdays, 2 4:50 PM Dr. Uttam Manna 313C Moulton Hall umanna@ilstu.edu
More informationTU-E2090 Research Assignment in Operations Management and Services
Aalto University School of Science Operations and Service Management TU-E2090 Research Assignment in Operations Management and Services Version 2016-08-29 COURSE INSTRUCTOR: OFFICE HOURS: CONTACT: Saara
More informationAppendix L: Online Testing Highlights and Script
Online Testing Highlights and Script for Fall 2017 Ohio s State Tests Administrations Test administrators must use this document when administering Ohio s State Tests online. It includes step-by-step directions,
More informationGCSE Mathematics B (Linear) Mark Scheme for November Component J567/04: Mathematics Paper 4 (Higher) General Certificate of Secondary Education
GCSE Mathematics B (Linear) Component J567/04: Mathematics Paper 4 (Higher) General Certificate of Secondary Education Mark Scheme for November 2014 Oxford Cambridge and RSA Examinations OCR (Oxford Cambridge
More informationUsing Calculators for Students in Grades 9-12: Geometry. Re-published with permission from American Institutes for Research
Using Calculators for Students in Grades 9-12: Geometry Re-published with permission from American Institutes for Research Using Calculators for Students in Grades 9-12: Geometry By: Center for Implementing
More informationECON 6901 Research Methods for Economists I Spring 2017
1 ECON 6901 Research Methods for Economists I Spring 2017 Instructors: John Gandar Artie Zillante Office: 220 Friday 211B Friday Office Phone: 704 687 7675 704 687 7589 E mail: jmgandar@uncc.edu azillant@uncc.edu
More informationTest Administrator User Guide
Test Administrator User Guide Fall 2017 and Winter 2018 Published October 17, 2017 Prepared by the American Institutes for Research Descriptions of the operation of the Test Information Distribution Engine,
More informationProject summary. English version. November 2005
Belgian Science Policy «AGORA - VIRTUAL LIBRARY» Research December 2004 - Project summary English version Véronique DUMONT (FUNDP-CITA) Véronique LAURENT (FUNDP-CITA) Evelien DE PAUW (University of GHENT
More informationGetting Started with TI-Nspire High School Science
Getting Started with TI-Nspire High School Science 2012 Texas Instruments Incorporated Materials for Institute Participant * *This material is for the personal use of T3 instructors in delivering a T3
More informationVOL. 3, NO. 5, May 2012 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.
Exploratory Study on Factors that Impact / Influence Success and failure of Students in the Foundation Computer Studies Course at the National University of Samoa 1 2 Elisapeta Mauai, Edna Temese 1 Computing
More informationReinforcement Learning by Comparing Immediate Reward
Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate
More informationPrerequisite: General Biology 107 (UE) and 107L (UE) with a grade of C- or better. Chemistry 118 (UE) and 118L (UE) or permission of instructor.
Introduction to Molecular and Cell Biology BIOL 499-02 Fall 2017 Class time: Lectures: Tuesday, Thursday 8:30 am 9:45 am Location: Name of Faculty: Contact details: Laboratory: 2:00 pm-4:00 pm; Monday
More informationMinitab Tutorial (Version 17+)
Minitab Tutorial (Version 17+) Basic Commands and Data Entry Graphical Tools Descriptive Statistics Outline Minitab Basics Basic Commands, Data Entry, and Organization Minitab Project Files (*.MPJ) vs.
More informationCase study Norway case 1
Case study Norway case 1 School : B (primary school) Theme: Science microorganisms Dates of lessons: March 26-27 th 2015 Age of students: 10-11 (grade 5) Data sources: Pre- and post-interview with 1 teacher
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 informationLife and career planning
Paper 30-1 PAPER 30 Life and career planning Bob Dick (1983) Life and career planning: a workbook exercise. Brisbane: Department of Psychology, University of Queensland. A workbook for class use. Introduction
More informationENME 605 Advanced Control Systems, Fall 2015 Department of Mechanical Engineering
ENME 605 Advanced Control Systems, Fall 2015 Department of Mechanical Engineering Lecture Details Instructor Course Objectives Tuesday and Thursday, 4:00 pm to 5:15 pm Information Technology and Engineering
More informationPython Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
More informationNotetaking Directions
Porter Notetaking Directions 1 Notetaking Directions Simplified Cornell-Bullet System Research indicates that hand writing notes is more beneficial to students learning than typing notes, unless there
More informationImplementing a tool to Support KAOS-Beta Process Model Using EPF
Implementing a tool to Support KAOS-Beta Process Model Using EPF Malihe Tabatabaie Malihe.Tabatabaie@cs.york.ac.uk Department of Computer Science The University of York United Kingdom Eclipse Process Framework
More informationLevel 1 Mathematics and Statistics, 2015
91037 910370 1SUPERVISOR S Level 1 Mathematics and Statistics, 2015 91037 Demonstrate understanding of chance and data 9.30 a.m. Monday 9 November 2015 Credits: Four Achievement Achievement with Merit
More informationEvidence-based Practice: A Workshop for Training Adult Basic Education, TANF and One Stop Practitioners and Program Administrators
Evidence-based Practice: A Workshop for Training Adult Basic Education, TANF and One Stop Practitioners and Program Administrators May 2007 Developed by Cristine Smith, Beth Bingman, Lennox McLendon and
More informationWriting Research Articles
Marek J. Druzdzel with minor additions from Peter Brusilovsky University of Pittsburgh School of Information Sciences and Intelligent Systems Program marek@sis.pitt.edu http://www.pitt.edu/~druzdzel Overview
More informationHoughton Mifflin Online Assessment System Walkthrough Guide
Houghton Mifflin Online Assessment System Walkthrough Guide Page 1 Copyright 2007 by Houghton Mifflin Company. All Rights Reserved. No part of this document may be reproduced or transmitted in any form
More informationMathematics. Mathematics
Mathematics Program Description Successful completion of this major will assure competence in mathematics through differential and integral calculus, providing an adequate background for employment in
More informationPeer Influence on Academic Achievement: Mean, Variance, and Network Effects under School Choice
Megan Andrew Cheng Wang Peer Influence on Academic Achievement: Mean, Variance, and Network Effects under School Choice Background Many states and municipalities now allow parents to choose their children
More informationCS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus
CS 1103 Computer Science I Honors Fall 2016 Instructor Muller Syllabus Welcome to CS1103. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts
More informationIntroduction to Simulation
Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /
More informationInstructor: Matthew Wickes Kilgore Office: ES 310
MATH 1314 College Algebra Syllabus Instructor: Matthew Wickes Kilgore Office: ES 310 Longview Office: LN 205C Email: mwickes@kilgore.edu Phone: 903 988-7455 Prerequistes: Placement test score on TSI or
More informationTOPICS LEARNING OUTCOMES ACTIVITES ASSESSMENT Numbers and the number system
Curriculum Overview Mathematics 1 st term 5º grade - 2010 TOPICS LEARNING OUTCOMES ACTIVITES ASSESSMENT Numbers and the number system Multiplies and divides decimals by 10 or 100. Multiplies and divide
More informationLearning Disability Functional Capacity Evaluation. Dear Doctor,
Dear Doctor, I have been asked to formulate a vocational opinion regarding NAME s employability in light of his/her learning disability. To assist me with this evaluation I would appreciate if you can
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 informationConceptual and Procedural Knowledge of a Mathematics Problem: Their Measurement and Their Causal Interrelations
Conceptual and Procedural Knowledge of a Mathematics Problem: Their Measurement and Their Causal Interrelations Michael Schneider (mschneider@mpib-berlin.mpg.de) Elsbeth Stern (stern@mpib-berlin.mpg.de)
More informationBluetooth mlearning Applications for the Classroom of the Future
Bluetooth mlearning Applications for the Classroom of the Future Tracey J. Mehigan Daniel C. Doolan Sabin Tabirca University College Cork, Ireland 2007 Overview Overview Introduction Mobile Learning Bluetooth
More informationACADEMIC TECHNOLOGY SUPPORT
ACADEMIC TECHNOLOGY SUPPORT D2L Respondus: Create tests and upload them to D2L ats@etsu.edu 439-8611 www.etsu.edu/ats Contents Overview... 1 What is Respondus?...1 Downloading Respondus to your Computer...1
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 informationImproving Conceptual Understanding of Physics with Technology
INTRODUCTION Improving Conceptual Understanding of Physics with Technology Heidi Jackman Research Experience for Undergraduates, 1999 Michigan State University Advisors: Edwin Kashy and Michael Thoennessen
More informationFull text of O L O W Science As Inquiry conference. Science as Inquiry
Page 1 of 5 Full text of O L O W Science As Inquiry conference Reception Meeting Room Resources Oceanside Unifying Concepts and Processes Science As Inquiry Physical Science Life Science Earth & Space
More informationGo fishing! Responsibility judgments when cooperation breaks down
Go fishing! Responsibility judgments when cooperation breaks down Kelsey Allen (krallen@mit.edu), Julian Jara-Ettinger (jjara@mit.edu), Tobias Gerstenberg (tger@mit.edu), Max Kleiman-Weiner (maxkw@mit.edu)
More informationA Case Study: News Classification Based on Term Frequency
A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center
More informationTabletClass Math Geometry Course Guidebook
TabletClass Math Geometry Course Guidebook Includes Final Exam/Key, Course Grade Calculation Worksheet and Course Certificate Student Name Parent Name School Name Date Started Course Date Completed Course
More informationState University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 1-1:50 NSC 210
1 State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 1-1:50 NSC 210 Dr. Michelle Benson mbenson2@buffalo.edu Office: 513 Park Hall Office Hours: Mon & Fri 10:30-12:30
More informationSTT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point.
STT 231 Test 1 Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. 1. A professor has kept records on grades that students have earned in his class. If he
More informationCIS 121 INTRODUCTION TO COMPUTER INFORMATION SYSTEMS - SYLLABUS
CIS 121 INTRODUCTION TO COMPUTER INFORMATION SYSTEMS - SYLLABUS Section: 7591, 7592 Instructor: Beth Roberts Class Time: Hybrid Classroom: CTR-270, AAH-234 Credits: 5 cr. Email: Canvas messaging (preferred)
More informationMath 96: Intermediate Algebra in Context
: Intermediate Algebra in Context Syllabus Spring Quarter 2016 Daily, 9:20 10:30am Instructor: Lauri Lindberg Office Hours@ tutoring: Tutoring Center (CAS-504) 8 9am & 1 2pm daily STEM (Math) Center (RAI-338)
More informationBluetooth mlearning Applications for the Classroom of the Future
Bluetooth mlearning Applications for the Classroom of the Future Tracey J. Mehigan, Daniel C. Doolan, Sabin Tabirca Department of Computer Science, University College Cork, College Road, Cork, Ireland
More informationSenior Project Information
BIOLOGY MAJOR PROGRAM Senior Project Information Contents: 1. Checklist for Senior Project.... p.2 2. Timeline for Senior Project. p.2 3. Description of Biology Senior Project p.3 4. Biology Senior Project
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 informationAssignment 1: Predicting Amazon Review Ratings
Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for
More informationTechnical Manual Supplement
VERSION 1.0 Technical Manual Supplement The ACT Contents Preface....................................................................... iii Introduction....................................................................
More informationAP Statistics Summer Assignment 17-18
AP Statistics Summer Assignment 17-18 Welcome to AP Statistics. This course will be unlike any other math class you have ever taken before! Before taking this course you will need to be competent in basic
More informationINPE São José dos Campos
INPE-5479 PRE/1778 MONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND COVER CLASSIFICATION IN A NEDRAL NETWORK ENVIRONNENT Maria Suelena S. Barros Valter Rodrigues INPE São José dos Campos 1993 SECRETARIA
More informationConcept Acquisition Without Representation William Dylan Sabo
Concept Acquisition Without Representation William Dylan Sabo Abstract: Contemporary debates in concept acquisition presuppose that cognizers can only acquire concepts on the basis of concepts they already
More information