Run vs. Gap for each Session cross=musician, square=nonmusician


 Kathryn Perry
 1 years ago
 Views:
Transcription
1 training are plotted as a plus sign and the subjects without formal musical training are plotted as squares. The musically untrained subjects are clustered in the right portion of the graph whereas the musically trained subjects are spread across the left and middle of the graph. The clustering of the parameter estimates for the musically untrained subjects farthest from the origin of the graph suggests that their data is more predictable by the run{gap rules than are the data from the musically trained subjects. Gap Parameter Value Run vs. Gap for each Session cross=musician, square=nonmusician Run Parameter Value Figure 7: Run vs Gap parameter values for musically trained (plus signs) and musically untrained (squares) subjects. References [Boker, 1995] Steven M. Boker. Predicting the grouping of rhythmic sequences using local estimators of information content. In Proceedings of the Fourteenth International Joint Conference on Articial Intelligence, San Mateo, CA, MorganKauman. [Garner and Gottwald, 1968] W. R. Garner and R. L. Gottwald. The perception and learning of temporal patterns. Quarterly Journal of Experimental Psychology, 20:97{109, [Garner, 1974] Wendell R. Garner. The Processing of Information and Structure. Lawrence Erlbaum Associates, Hillsdale, NJ, [Handel, 1989] Stephen Handel. Listening: An Introduction to the Perception of Auditory Events. MIT Press, Cambridge, MA, [Lashley, 1952] K. S. Lashley. The problem of serial order in behavior. In L. Jeress, editor, Cerebral Mechanisms in Behavior, pages 112{136. Wiley, New York, [Royer and Garner, 1966] F. L. Royer and W. R. Garner. Response uncertainty and perceptual diculty of auditory temporal patterns. Perception & Psychophysics, 1:41{47, [Shannon and Weaver, 1949] C. E. Shannon and W. Weaver. The Mathematical Theory of Communication. The University of Illinois Press, Urbana, Discussion These analyses suggest that there exist large, stable individual dierences in the perception of rhythmic structure in isochronous auditory sequences. This result indicates that rule{based deterministic algorithms such as the run{gap rules are unlikely to be able to predict the perception of musical rhythmic structure. The apparent negative covariance between musical training and adherence to the run{gap rule system suggests that an articial musical generation program which adheres to such a rule{based system may produce music which is perceived as being similar to that produced by musically untrained individuals. We argue that a parameterized, probability{based system which constructs structure from local information theoretic estimates may provide a better approximation to the variety of individual dierences in music produced by trained individuals.
2 consistency in the predictions from the run{gap rules. The histogram of responses shown in Figure 4{A is predicted by the run{gap principles, whereas the pattern in Figure 4{B is almost identical and yet the responses have now shifted to a point which is not predicted by the run{gap rules. Any theory of organization of rhythmic perception must take into account this type of shift; a number of these dichotomies appear in the response data from this experiment Distribution of Beats for Subject 2 Length = 6 Histogram of number of taps on each beat Distribution of Beats for Subject 6 Length = 6 Histogram of number of taps on each beat Figure 5: Histograms showing response distributions from two subjects for all stimulus patterns of length 6. Note the dierence between the subject labeled A whose responses are well predicted by the run{gap rules and the subject labeled B who's responses that are highly patterned but are not structured in the way that the run{gap rules would predict. 3.2 Individual Dierences in Response Distributions. Figure 5 shows two histograms which represent the responses to all patterns of length two for two individual subjects. The responses of the subject shown in Figure 5{A are highly predictable from the run{gap rules. Subjects showing this type of pattern indicated that they had little or no formal musical training and didn't presently play a musical instrument. The responses of the subject shown in Figure 5{B are quite dierent from the previous subject. A large proportion of the responses from this subjects were not predicted by the run{gap rules. The responses from subjects indicating that they had musical training tended to show patterns of response unpredicted by the run{gap rules. Gap Parameter Value Run vs. Gap for each Session number=subjectid Run Parameter Value Figure 6: Run vs Gap parameter estimates for each individual session. The number in the scatterplot represents the subject ID number. 3.3 Model Fitting Results The histograms from Figure 5 indicate that there may be large, stable individual dierences in these data. In order to examine both interindividual dierences and intraindividual variability a structural equation model was t to the data from each experimental session. In this way, the parameter estimates are not aggregated either across time or across subjects. The Run{Gap Model shown in Figure 1 was separately t to each session for each individual using the SAS Proc Calis structural model tting procedure. The parameter estimates for P run, the eect of the Run predictor variable on S the latent perceived structure variable, and for P gap, the eect of the Gap variable on S are summarized in Figures 6 and 7. Figure 6 plots the parameter estimates for P run against P gap. The numbers in the scatterplot represent the subject ID number from the experimental data. There is evident clustering of the subject ID numbers, which suggests that the intraindividual variation is smaller than the intraindividual variability. Figure 7 again plots the parameter estimates for P run against P gap, but now the subjects with formal musical
3 edge of the histogram. Since the stimulus pattern is always repeated, responses which occur at the end of the last beat can also be thought of as occurring just before the rst beat in the pattern. Therefore, Figure 2{A can be read as showing a distribution of responses with a mode centered 25{50 ms before the predicted starting point of the measure. Similarly, each of the other histograms in Figure 2B show distributions which closely follow the pattern predicted by the run{gap principles. Note that when the gap is long, the distribution of responses around the predicted starting point spreads out. But when every beat is lled except one, as in Figure 2{B, the distribution of responses is more precise. This is not at all surprising when one considers the task as being the prediction of the rst sounded beat; the longer the gap before the sounded beat, the more dicult the task of synchronizing one's response with the occurrence of that beat Distribution of Beats for [ ] space690(1) Run=0 Gap=0 [ ] Distribution of Beats for [ ] space690(2) Run=0 Gap=0 responses that the run{gap principles did not predict particularly well. Figure 3{A shows an example of one of the stimuli for which a sizable percentage of responses occur on the beat following the predicted starting point. The distributions of quite a few of the stimuli show this characteristic distribution of responses onto the beat following the predicted starting point even when this second beat is in the middle of a run of 1's Distribution of Beats for [ ] space890(4) Run=0 Gap=0 7 8 [ ] Distribution of Beats for [ ] space890(6) Run=0 Gap=0 7 8 [ ] Figure 4: Histogram showing response distributions for one stimulus pattern which elicited responses predicted by the run{gap rules. Histogram showing response distributions for a nearly identical stimulus pattern which elicited responses which were not predicted by the run{gap rules. [ ] Figure 3: Histograms showing response distributions from all subjects for two selected stimulus patterns which elicited responses showing lower degrees of agreement on the starting point for the stimulus pattern and substantial variation from the starting point predicted by the run{gap rules. Figure 3 shows two histograms of the distributions of responses to two more selected rhythmic stimuli. These histograms represent some of the stimuli which elicited Figure 3{B shows an example of another phenomenon which was observed to occur in response to a number of stimuli. The distribution of responses is split almost equally between the predicted starting point and a solitary 1 following a small gap following the longest run of 1's. There is nothing in the run{gap rules which accounts for this large, reliable shift in responses. A theory of the perception of rhythmic organization must be able to account for this type of shift which has been observed to occur in pairs of nearly identical stimuli. Figure 4 shows two histograms which highlight the in
4 2.2 Experimental Procedure Each subject was asked to complete the experimental procedure once on each of ve occasions, where the occasions were separated by as little as 24 hours and by as much as three weeks. In each trial, subjects were presented with one repeating rhythmic auditory pattern and were asked to respond by striking a key on a synthesizer keyboard synchronous with the perceived starting point of the pattern. Subjects were asked to continue to strike the key at the beginning of each repetition until condent that they had perceived the starting point. Once subjects were condent of their response, they were asked to press a mouse button ending the trial. A single rhythmic stimulus was composed of a xed number of beats: equal intervals of time which could either be empty or be lled with a percussive sound at the beginning of the interval. The set of stimuli for Experiment 1 consisted of the 69 unique rhythmic patterns of length 8 or less with oversampling applied to the most ambiguous patterns, thus creating 115 trials per session. A more complete description of the experimental procedure and the structural modeling analysis is given elsewhere in this volume [Boker, 1995]. Run Prun Pgap Gap 3 Results 3.1 Response Histograms Figure 2 presents two histograms of the distributions of responses to two selected rhythmic stimuli from all subjects on all occasions of measurement. Figure 2{A shows the distribution of responses to a simple stimulus, a measure of four beats in which one beat is sounded and the others are silent. The label for each histogram contains a binary representation of the stimulus pattern which elicited the distribution of responses. For instance, Figure 2{A is labeled with [ ]. Elapsed time is always increasing from left to right, and each number on the X{ axis represents a duration of 250 ms. The run{gap rules predict a starting point for the rhythmic stimulus aligned with the far left side of each histogram and consequently also with the rst binary digit in the label Distribution of Beats for [ ] space4100(0) Run=0 Gap=0 RB e R S V e V Figure 1: Path diagram showing a structural model of Garner's basic run{gap theory. A e A [ ] Distribution of Beats for [ ] space4100(2) Run=0 Gap= [ ] 2.3 Latent Path Model A latent variable structural equation model was constructed which t Garner's \run{gap" heuristic predictions to the data gathered from this experiment. Figure 1 shows a path model of Garner's run{gap heuristics. The predictor variables are Run, the run principle, and Gap, the gap principle. The latent variable is S, the perceived structure of the rhythmic pattern. The measured outcome variables are RB, the response within the beat; A, the accuracy of the response; and V, the velocity of the response. Figure 2: Histograms showing response distributions from all subjects for two selected stimulus patterns which elicited responses showing high degrees of agreement on the starting point for the stimulus pattern and agreement with the run{gap rules. Responses which occurred just after the predicted starting point appear at the far left edge of the histogram, and the responses which occurred at the end of the duration of the last beat appear at the far right
5 Individual Dierences in the Perceptual Segmentation of Auditory Rhythmic Sequences. Steven M. Boker y and Michael Kubovy Department of Psychology The University of Virginia Charlottesville, Virginia boker/ Abstract Musical rhythm is inherently structured in such a way that it is perceived to be partitioned into segments. A repeating rhythmic stream has many possible segmentations. We describe an experiment which explores the individual variability in the perception of the segmentation of repeating auditory rhythms. Histograms of responses to this experiment show large individual dierences which are not predicted by Garner's run{gap rules [Garner, 1974]. Analysis of individual dierences in parameters of a latent variable model of the run{gap predictors indicates that predictability by the run{gap rules inversely covaries with musical training. 1 Introduction Music presents the auditory system with a continuous stream of sensory data which is perceived to have a regular, self{referential and sometimes repeating structure. The perceptual segmentation of this auditory stream into a sequence of events which have further relationships with each other seems so automatic that it forms an often unstated assumption at the basis of musical theory. But upon closer inspection this task is not as automatic nor as rule{based as it rst appears. The problem of segmentation of continuous streams of sensory data into temporally ordered sequences has posed a longstanding problem for cognitive psychologists [Lashley, 1952]. There is more ambiguity in the temporal structure of musical sequences than it might appear. Musical structure is disambiguated through a variety of devices such as stress and temporal anticipation of critical segmentation points [Handel, 1989]. By studying the nature of the ambiguity in unstressed isochronous rhythmic sequences we can understand and predict the organization Presented to the International Joint Conference on Articial Intelligence Workshop on Music and Articial Intelligence, August 21, 1995 y Supported by the Institute for Developmental and Health Research Methodology which will be perceived to be inherent in the temporal structure of the sequence. A simple repeating rhythmic pattern which contains no stressed elements may be perceived as having a variety of starting points. Figure 1 shows a repeating sequence which could be perceived as having one of three potential starting points. Some starting points have a higher probability of being perceived than others, but each of these probabilities is greater than zero : : : : : : : : : Figure 1. A repeating sequence of length three has potential starting points in three dierent positions. Garner and his colleagues [Royer and Garner, 1966; Garner and Gottwald, 1968; Garner, 1974] studied these types of rhythmic patterns and devised rules which they named the run principle and gap principle by which predictions could be made regarding the organization that would be perceived by individual subjects. Our models replace Garner's rule{based system with an information theoretic [Shannon and Weaver, 1949] estimation of the probability of perceiving any starting point as a segmentation boundary. The data which we present suggests that not only do dierent individuals perceive the same rhythmic stream dierently, but that the same individual will perceive the same rhythmic stream dierently on dierent occasions. This result creates a problem for rule{based systems, but is consistent with a model which predicts behavior based on a distribution of probabilities generated from characteristics of the input. 2 Methods 2.1 Subjects Eleven subjects participated in the experiment, 8 males and 3 females. Age of the subjects ranged from 18 to 41. Six subjects reported having received 4 years of training in playing a musical instrument, while the remaining ve subjects reported no formal training in playing a musical instrument.
Complexity measures of musical rhythms
COMPLEXITY MEASURES OF MUSICAL RHYTHMS 1 Complexity measures of musical rhythms Ilya Shmulevich and DirkJan Povel [Shmulevich, I., Povel, D.J. (2000) Complexity measures of musical rhythms. In P. Desain
More informationRhythmic Perception, Music and Language: A New Theoretical Framework for Understanding and Remediating Specific Language Impairment
1 Rhythmic Perception, Music and Language: A New Theoretical Framework for Understanding and Remediating Specific Language Impairment Usha Goswami, Ruth Cumming and Angela Wilson Centre for Neuroscience
More informationWHICH TYPE OF GRAPH SHOULD YOU CHOOSE?
PRESENTING GRAPHS WHICH TYPE OF GRAPH SHOULD YOU CHOOSE? CHOOSING THE RIGHT TYPE OF GRAPH You will usually choose one of four very common graph types: Line graph Bar graph Pie chart Histograms LINE GRAPHS
More informationSession 7 Bivariate Data and Analysis
Session 7 Bivariate Data and Analysis Key Terms for This Session Previously Introduced mean standard deviation New in This Session association bivariate analysis contingency table covariation least squares
More informationClustering and scheduling maintenance tasks over time
Clustering and scheduling maintenance tasks over time Per Kreuger 20080429 SICS Technical Report T2008:09 Abstract We report results on a maintenance scheduling problem. The problem consists of allocating
More informationHints for Success on the AP Statistics Exam. (Compiled by Zack Bigner)
Hints for Success on the AP Statistics Exam. (Compiled by Zack Bigner) The Exam The AP Stat exam has 2 sections that take 90 minutes each. The first section is 40 multiple choice questions, and the second
More informationFairfield Public Schools
Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity
More informationBrain Maps The Sensory Homunculus
Brain Maps The Sensory Homunculus Our brains are maps. This mapping results from the way connections in the brain are ordered and arranged. The ordering of neural pathways between different parts of the
More informationMeans, standard deviations and. and standard errors
CHAPTER 4 Means, standard deviations and standard errors 4.1 Introduction Change of units 4.2 Mean, median and mode Coefficient of variation 4.3 Measures of variation 4.4 Calculating the mean and standard
More informationThe right edge of the box is the third quartile, Q 3, which is the median of the data values above the median. Maximum Median
CONDENSED LESSON 2.1 Box Plots In this lesson you will create and interpret box plots for sets of data use the interquartile range (IQR) to identify potential outliers and graph them on a modified box
More informationAppendix A: Science Practices for AP Physics 1 and 2
Appendix A: Science Practices for AP Physics 1 and 2 Science Practice 1: The student can use representations and models to communicate scientific phenomena and solve scientific problems. The real world
More informationSTRUTS: Statistical Rules of Thumb. Seattle, WA
STRUTS: Statistical Rules of Thumb Gerald van Belle Departments of Environmental Health and Biostatistics University ofwashington Seattle, WA 981954691 Steven P. Millard Probability, Statistics and Information
More informationTYPES OF NUMBERS. Example 2. Example 1. Problems. Answers
TYPES OF NUMBERS When two or more integers are multiplied together, each number is a factor of the product. Nonnegative integers that have exactly two factors, namely, one and itself, are called prime
More informationORGANIZATIONAL DESIGN AND ADAPTATION IN RESPONSE TO CRISES: THEORY AND PRACTICE
ORGANIZATIONAL DESIGN AND ADAPTATION IN RESPONSE TO CRISES: THEORY AND PRACTICE ZHIANG ("JOHN") LIN School of Management University of Texas at Dallas Richardson, TX 75083 KATHLEEN M. CARLEY Carnegie Mellon
More informationDescriptive Statistics and Measurement Scales
Descriptive Statistics 1 Descriptive Statistics and Measurement Scales Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationConstructing a TpB Questionnaire: Conceptual and Methodological Considerations
Constructing a TpB Questionnaire: Conceptual and Methodological Considerations September, 2002 (Revised January, 2006) Icek Ajzen Brief Description of the Theory of Planned Behavior According to the theory
More information1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number
1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number A. 3(x  x) B. x 3 x C. 3x  x D. x  3x 2) Write the following as an algebraic expression
More informationTAXONOMY OF EDUCATIONAL OBJECTIVES (Excerpts from Linn and Miller Measurement and Assessment in Teaching, 9 th ed)
TAXONOMY OF EDUCATIONAL OBJECTIVES (Excerpts from Linn and Miller Measurement and Assessment in Teaching, 9 th ed) Table 1 Major categories in the cognitive domain of the taxonomy of educational objectives
More informationBehavioral Entropy of a Cellular Phone User
Behavioral Entropy of a Cellular Phone User Santi Phithakkitnukoon 1, Husain Husna, and Ram Dantu 3 1 santi@unt.edu, Department of Comp. Sci. & Eng., University of North Texas hjh36@unt.edu, Department
More informationInfinite Algebra 1 supports the teaching of the Common Core State Standards listed below.
Infinite Algebra 1 Kuta Software LLC Common Core Alignment Software version 2.05 Last revised July 2015 Infinite Algebra 1 supports the teaching of the Common Core State Standards listed below. High School
More informationEXCEL EXERCISE AND ACCELERATION DUE TO GRAVITY
EXCEL EXERCISE AND ACCELERATION DUE TO GRAVITY Objective: To learn how to use the Excel spreadsheet to record your data, calculate values and make graphs. To analyze the data from the Acceleration Due
More informationCORRELATED TO THE SOUTH CAROLINA COLLEGE AND CAREERREADY FOUNDATIONS IN ALGEBRA
We Can Early Learning Curriculum PreK Grades 8 12 INSIDE ALGEBRA, GRADES 8 12 CORRELATED TO THE SOUTH CAROLINA COLLEGE AND CAREERREADY FOUNDATIONS IN ALGEBRA April 2016 www.voyagersopris.com Mathematical
More informationAn Introduction to. Metrics. used during. Software Development
An Introduction to Metrics used during Software Development Life Cycle www.softwaretestinggenius.com Page 1 of 10 Define the Metric Objectives You can t control what you can t measure. This is a quote
More informationFREE FALL. Introduction. Reference Young and Freedman, University Physics, 12 th Edition: Chapter 2, section 2.5
Physics 161 FREE FALL Introduction This experiment is designed to study the motion of an object that is accelerated by the force of gravity. It also serves as an introduction to the data analysis capabilities
More informationSimple Linear Regression in SPSS STAT 314
Simple Linear Regression in SPSS STAT 314 1. Ten Corvettes between 1 and 6 years old were randomly selected from last year s sales records in Virginia Beach, Virginia. The following data were obtained,
More informationAppendix E: Graphing Data
You will often make scatter diagrams and line graphs to illustrate the data that you collect. Scatter diagrams are often used to show the relationship between two variables. For example, in an absorbance
More informationSouth Carolina College and CareerReady (SCCCR) Probability and Statistics
South Carolina College and CareerReady (SCCCR) Probability and Statistics South Carolina College and CareerReady Mathematical Process Standards The South Carolina College and CareerReady (SCCCR)
More informationControl of affective content in music production
International Symposium on Performance Science ISBN 9789090224848 The Author 2007, Published by the AEC All rights reserved Control of affective content in music production António Pedro Oliveira and
More informationMathematics. Mathematical Practices
Mathematical Practices 1. Make sense of problems and persevere in solving them. 2. Reason abstractly and quantitatively. 3. Construct viable arguments and critique the reasoning of others. 4. Model with
More informationLOGISTIC REGRESSION ANALYSIS
LOGISTIC REGRESSION ANALYSIS C. Mitchell Dayton Department of Measurement, Statistics & Evaluation Room 1230D Benjamin Building University of Maryland September 1992 1. Introduction and Model Logistic
More informationAP1 Waves. (A) frequency (B) wavelength (C) speed (D) intensity. Answer: (A) and (D) frequency and intensity.
1. A fire truck is moving at a fairly high speed, with its siren emitting sound at a specific pitch. As the fire truck recedes from you which of the following characteristics of the sound wave from the
More informationWhen does ignorance make us smart? Additional factors guiding heuristic inference.
When does ignorance make us smart? Additional factors guiding heuristic inference. C. Philip Beaman (c.p.beaman@reading.ac.uk) Rachel McCloy (r.a.mccloy@reading.ac.uk) Philip T. Smith (p.t.smith@reading.ac.uk)
More informationIntelligent Agents. Based on An Introduction to MultiAgent Systems and slides by Michael Wooldridge
Intelligent Agents Based on An Introduction to MultiAgent Systems and slides by Michael Wooldridge Denition of an Agent An agent is a computer system capable of autonomous action in some environment, in
More informationThe Relationship between the Fundamental Attribution Bias, Relationship Quality, and Performance Appraisal
The Relationship between the Fundamental Attribution Bias, Relationship Quality, and Performance Appraisal Executive Summary Abstract The ability to make quality decisions that influence people to exemplary
More informationThe aspect of the data that we want to describe/measure is the degree of linear relationship between and The statistic r describes/measures the degree
PS 511: Advanced Statistics for Psychological and Behavioral Research 1 Both examine linear (straight line) relationships Correlation works with a pair of scores One score on each of two variables ( and
More informationRegents Exam Questions A2.S.8: Correlation Coefficient
A2.S.8: Correlation Coefficient: Interpret within the linear regression model the value of the correlation coefficient as a measure of the strength of the relationship 1 Which statement regarding correlation
More informationMeasurement with Ratios
Grade 6 Mathematics, Quarter 2, Unit 2.1 Measurement with Ratios Overview Number of instructional days: 15 (1 day = 45 minutes) Content to be learned Use ratio reasoning to solve realworld and mathematical
More informationProblem of the Month Through the Grapevine
The Problems of the Month (POM) are used in a variety of ways to promote problem solving and to foster the first standard of mathematical practice from the Common Core State Standards: Make sense of problems
More informationPsychology 312: Lecture 6 Scales of Measurement. Slide #1. Scales of Measurement Reliability, validity, and scales of measurement.
Psychology 312: Lecture 6 Scales of Measurement Slide #1 Scales of Measurement Reliability, validity, and scales of measurement. In this lecture we will discuss scales of measurement. Slide #2 Outline
More informationCurrent Standard: Mathematical Concepts and Applications Shape, Space, and Measurement Primary
Shape, Space, and Measurement Primary A student shall apply concepts of shape, space, and measurement to solve problems involving two and threedimensional shapes by demonstrating an understanding of:
More information1 BPS Math Year at a Glance (Adapted from A Story of Units Curriculum Maps in Mathematics P5)
Grade 5 Key Areas of Focus for Grades 35: Multiplication and division of whole numbers and fractionsconcepts, skills and problem solving Expected Fluency: Multidigit multiplication Module M1: Whole
More informationCHARTS AND GRAPHS INTRODUCTION USING SPSS TO DRAW GRAPHS SPSS GRAPH OPTIONS CAG08
CHARTS AND GRAPHS INTRODUCTION SPSS and Excel each contain a number of options for producing what are sometimes known as business graphics  i.e. statistical charts and diagrams. This handout explores
More informationA Guide for a Selection of SPSS Functions
A Guide for a Selection of SPSS Functions IBM SPSS Statistics 19 Compiled by Beth Gaedy, Math Specialist, Viterbo University  2012 Using documents prepared by Drs. Sheldon Lee, Marcus Saegrove, Jennifer
More informationEXPERIMENT 3 Analysis of a freely falling body Dependence of speed and position on time Objectives
EXPERIMENT 3 Analysis of a freely falling body Dependence of speed and position on time Objectives to verify how the distance of a freelyfalling body varies with time to investigate whether the velocity
More informationIBM SPSS Direct Marketing 23
IBM SPSS Direct Marketing 23 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 23, release
More informationSelective attention and development of categorization: An eye tracking study
Selective attention and development of categorization: An eye tracking study Xin Yao (yao.64@osu.edu) Center for Cognitive Science The Ohio State University 209C Ohio Stadium East, 1961 Tuttle Park Place
More informationIBM SPSS Direct Marketing 22
IBM SPSS Direct Marketing 22 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 22, release
More informationMathematics. Probability and Statistics Curriculum Guide. Revised 2010
Mathematics Probability and Statistics Curriculum Guide Revised 2010 This page is intentionally left blank. Introduction The Mathematics Curriculum Guide serves as a guide for teachers when planning instruction
More informationBullet Graph Design Specification Last Revision: October 10, 2013
Bullet Graph Design Specification Last Revision: October 10, 2013 Overview The bullet graph was developed to replace the meters and gauges that are often used on dashboards. Its linear and nofrills design
More informationValor Christian High School Mrs. Bogar Biology Graphing Fun with a Paper Towel Lab
1 Valor Christian High School Mrs. Bogar Biology Graphing Fun with a Paper Towel Lab I m sure you ve wondered about the absorbency of paper towel brands as you ve quickly tried to mop up spilled soda from
More informationInfinite Campus Grade Book BETA
Infinite Campus Grade Book BETA This tool was released for an open beta testing period. This new Grade Book will continue to exist parallel to the current Grade Book. All Teachers in the Nelson County
More informationSouth Carolina College and CareerReady (SCCCR) Algebra 1
South Carolina College and CareerReady (SCCCR) Algebra 1 South Carolina College and CareerReady Mathematical Process Standards The South Carolina College and CareerReady (SCCCR) Mathematical Process
More informationSummarizing and Displaying Categorical Data
Summarizing and Displaying Categorical Data Categorical data can be summarized in a frequency distribution which counts the number of cases, or frequency, that fall into each category, or a relative frequency
More informatione = random error, assumed to be normally distributed with mean 0 and standard deviation σ
1 Linear Regression 1.1 Simple Linear Regression Model The linear regression model is applied if we want to model a numeric response variable and its dependency on at least one numeric factor variable.
More informationFast Sequential Summation Algorithms Using Augmented Data Structures
Fast Sequential Summation Algorithms Using Augmented Data Structures Vadim Stadnik vadim.stadnik@gmail.com Abstract This paper provides an introduction to the design of augmented data structures that offer
More informationSPSS: Descriptive and Inferential Statistics. For Windows
For Windows August 2012 Table of Contents Section 1: Summarizing Data...3 1.1 Descriptive Statistics...3 Section 2: Inferential Statistics... 10 2.1 ChiSquare Test... 10 2.2 T tests... 11 2.3 Correlation...
More informationTracking Moving Objects In Video Sequences Yiwei Wang, Robert E. Van Dyck, and John F. Doherty Department of Electrical Engineering The Pennsylvania State University University Park, PA16802 Abstract{Object
More informationL2 EXPERIENCE MODULATES LEARNERS USE OF CUES IN THE PERCEPTION OF L3 TONES
L2 EXPERIENCE MODULATES LEARNERS USE OF CUES IN THE PERCEPTION OF L3 TONES Zhen Qin, Allard Jongman Department of Linguistics, University of Kansas, United States qinzhenquentin2@ku.edu, ajongman@ku.edu
More informationA causal algorithm for beattracking
A causal algorithm for beattracking Benoit Meudic Ircam  Centre Pompidou 1, place Igor Stravinsky, 75004 Paris, France meudic@ircam.fr ABSTRACT This paper presents a system which can perform automatic
More informationPrentice Hall Mathematics: Algebra 1 2007 Correlated to: Michigan Merit Curriculum for Algebra 1
STRAND 1: QUANTITATIVE LITERACY AND LOGIC STANDARD L1: REASONING ABOUT NUMBERS, SYSTEMS, AND QUANTITATIVE SITUATIONS Based on their knowledge of the properties of arithmetic, students understand and reason
More informationDeployment of express checkout lines at supermarkets
Deployment of express checkout lines at supermarkets Maarten Schimmel Research paper Business Analytics April, 213 Supervisor: René Bekker Faculty of Sciences VU University Amsterdam De Boelelaan 181 181
More informationChapter 10  Practice Problems 1
Chapter 10  Practice Problems 1 1. A researcher is interested in determining if one could predict the score on a statistics exam from the amount of time spent studying for the exam. In this study, the
More informationA STATISTICS COURSE FOR ELEMENTARY AND MIDDLE SCHOOL TEACHERS. Gary Kader and Mike Perry Appalachian State University USA
A STATISTICS COURSE FOR ELEMENTARY AND MIDDLE SCHOOL TEACHERS Gary Kader and Mike Perry Appalachian State University USA This paper will describe a contentpedagogy course designed to prepare elementary
More information2.4 Motion and Integrals
2 KINEMATICS 2.4 Motion and Integrals Name: 2.4 Motion and Integrals In the previous activity, you have seen that you can find instantaneous velocity by taking the time derivative of the position, and
More informationWhat is the Probability of Pigging Out
What is the Probability of Pigging Out Mary Richardson Susan Haller Grand Valley State University St. Cloud State University richamar@gvsu.edu skhaller@stcloudstate.edu Published: April 2012 Overview of
More informationBig Ideas in Mathematics
Big Ideas in Mathematics which are important to all mathematics learning. (Adapted from the NCTM Curriculum Focal Points, 2006) The Mathematics Big Ideas are organized using the PA Mathematics Standards
More information2. Simple Linear Regression
Research methods  II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according
More informationDescriptive Statistics. Understanding Data: Categorical Variables. Descriptive Statistics. Dataset: Shellfish Contamination
Descriptive Statistics Understanding Data: Dataset: Shellfish Contamination Location Year Species Species2 Method Metals Cadmium (mg kg  ) Chromium (mg kg  ) Copper (mg kg  ) Lead (mg kg  ) Mercury
More informationBinary Logistic Regression
Binary Logistic Regression Main Effects Model Logistic regression will accept quantitative, binary or categorical predictors and will code the latter two in various ways. Here s a simple model including
More informationMath. MCC6.RP.1 Understand the concept of a ratio and use
MCC6.RP.1 Understand the concept of a ratio and use ratio language to describe a ratio relationship between two quantities. For example, The ratio of wings to beaks in the bird house at the zoo was 2:1,
More informationCALCULATIONS & STATISTICS
CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 15 scale to 0100 scores When you look at your report, you will notice that the scores are reported on a 0100 scale, even though respondents
More informationImprovement of Visual Attention and Working Memory through a Webbased Cognitive Training Program
. Improvement of Visual Attention and Working Memory through a Webbased Cognitive Training Program Michael Scanlon David Drescher Kunal Sarkar Context: Prior work has revealed that cognitive ability is
More informationPA Common Core Standards Standards for Mathematical Practice Grade Level Emphasis*
Habits of Mind of a Productive Thinker Make sense of problems and persevere in solving them. Attend to precision. PA Common Core Standards The Pennsylvania Common Core Standards cannot be viewed and addressed
More informationVideo in Logger Pro. There are many ways to create and use video clips and still images in Logger Pro.
Video in Logger Pro There are many ways to create and use video clips and still images in Logger Pro. Insert an existing video clip into a Logger Pro experiment. Supported file formats include.avi and.mov.
More informationAUTOMATIC PHONEME SEGMENTATION WITH RELAXED TEXTUAL CONSTRAINTS
AUTOMATIC PHONEME SEGMENTATION WITH RELAXED TEXTUAL CONSTRAINTS PIERRE LANCHANTIN, ANDREW C. MORRIS, XAVIER RODET, CHRISTOPHE VEAUX Very high quality texttospeech synthesis can be achieved by unit selection
More informationHighMix LowVolume Flow Shop Manufacturing System Scheduling
Proceedings of the 14th IAC Symposium on Information Control Problems in Manufacturing, May 2325, 2012 HighMix LowVolume low Shop Manufacturing System Scheduling Juraj Svancara, Zdenka Kralova Institute
More informationCount back in ones on a numbered number line to take away, with numbers up to 20
SUBTRACTION Stage 1 Subtract from numbers up to 20 Children consolidate understanding of subtraction practically, showing subtraction on bead strings, using cubes etc. and in familiar contexts, and are
More informationADDING DOCUMENTS TO A PROJECT. Create a a new internal document for the transcript: DOCUMENTS / NEW / NEW TEXT DOCUMENT.
98 Data Transcription The ADocs function, introduced in ATLAS.ti 6, allows you to not only transcribe your data within ATLAS.ti, but to also link documents to each other in such a way that they can be
More informationDescriptive Statistics
Y520 Robert S Michael Goal: Learn to calculate indicators and construct graphs that summarize and describe a large quantity of values. Using the textbook readings and other resources listed on the web
More informationDESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.
DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize,
More informationMetacognition and Mathematical Problem Solving: Helping Students to Ask The Right Questions
Metacognition and Mathematical Problem Solving: Helping Students to Ask The Right Questions Shirley Gartmann and Melissa Freiberg The acquisition of problem solving, reasoning and critical thinking skills
More informationRegular Expressions and Automata using Haskell
Regular Expressions and Automata using Haskell Simon Thompson Computing Laboratory University of Kent at Canterbury January 2000 Contents 1 Introduction 2 2 Regular Expressions 2 3 Matching regular expressions
More informationTHE MALTESE LABOUR MARKET AND FRICTIONAL UNEMPLOYMENT
Bank of Valletta Review, No. 32, Autumn 2005 THE MALTESE LABOUR MARKET AND FRICTIONAL UNEMPLOYMENT Mirko Mallia* Abstract. This paper discusses the extent to which unemployment in Malta was caused by frictional
More informationSoftware Metrics. Lord Kelvin, a physicist. George Miller, a psychologist
Software Metrics 1. Lord Kelvin, a physicist 2. George Miller, a psychologist Software Metrics Product vs. process Most metrics are indirect: No way to measure property directly or Final product does not
More informationHypothesis Testing  Relationships
 Relationships Session 3 AHX43 (28) 1 Lecture Outline Correlational Research. The Correlation Coefficient. An example. Considerations. One and Twotailed Tests. Errors. Power. for Relationships AHX43
More informationThis unit will lay the groundwork for later units where the students will extend this knowledge to quadratic and exponential functions.
Algebra I Overview View unit yearlong overview here Many of the concepts presented in Algebra I are progressions of concepts that were introduced in grades 6 through 8. The content presented in this course
More informationResearch Variables. Measurement. Scales of Measurement. Chapter 4: Data & the Nature of Measurement
Chapter 4: Data & the Nature of Graziano, Raulin. Research Methods, a Process of Inquiry Presented by Dustin Adams Research Variables Variable Any characteristic that can take more than one form or value.
More informationStandard Deviation Calculator
CSS.com Chapter 35 Standard Deviation Calculator Introduction The is a tool to calculate the standard deviation from the data, the standard error, the range, percentiles, the COV, confidence limits, or
More informationThe Effect of Dropping a Ball from Different Heights on the Number of Times the Ball Bounces
The Effect of Dropping a Ball from Different Heights on the Number of Times the Ball Bounces Or: How I Learned to Stop Worrying and Love the Ball Comment [DP1]: Titles, headings, and figure/table captions
More informationJitter Measurements in Serial Data Signals
Jitter Measurements in Serial Data Signals Michael Schnecker, Product Manager LeCroy Corporation Introduction The increasing speed of serial data transmission systems places greater importance on measuring
More information9 Descriptive and Multivariate Statistics
9 Descriptive and Multivariate Statistics Jamie Price Donald W. Chamberlayne * S tatistics is the science of collecting and organizing data and then drawing conclusions based on data. There are essentially
More informationASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS
DATABASE MARKETING Fall 2015, max 24 credits Dead line 15.10. ASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS PART A Gains chart with excel Prepare a gains chart from the data in \\work\courses\e\27\e20100\ass4b.xls.
More informationSummary of important mathematical operations and formulas (from first tutorial):
EXCEL Intermediate Tutorial Summary of important mathematical operations and formulas (from first tutorial): Operation Key Addition + Subtraction  Multiplication * Division / Exponential ^ To enter a
More informationData Mining. Practical Machine Learning Tools and Techniques. Classification, association, clustering, numeric prediction
Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 2 of Data Mining by I. H. Witten and E. Frank Input: Concepts, instances, attributes Terminology What s a concept? Classification,
More informationPC Postprocessing Technologies: A Competitive Analysis
PC Postprocessing Technologies: A Competitive Analysis Home Theater v4 SRS Premium Sound Waves MaxxAudio 3 Abstract In a scientifically rigorous analysis of audio postprocessing technologies for laptop
More informationSection 3 Part 1. Relationships between two numerical variables
Section 3 Part 1 Relationships between two numerical variables 1 Relationship between two variables The summary statistics covered in the previous lessons are appropriate for describing a single variable.
More informationOn the Application of Experimental Results in Dynamic Graph Drawing
On the Application of Experimental Results in Dynamic Graph Drawing Daniel Archambault 1 and Helen C. Purchase 2 1 Swansea University, United Kingdom d.w.archambault@swansea.ac.uk 2 University of Glasgow,
More informationChapter 10. Key Ideas Correlation, Correlation Coefficient (r),
Chapter 0 Key Ideas Correlation, Correlation Coefficient (r), Section 0: Overview We have already explored the basics of describing single variable data sets. However, when two quantitative variables
More informationDrawing a histogram using Excel
Drawing a histogram using Excel STEP 1: Examine the data to decide how many class intervals you need and what the class boundaries should be. (In an assignment you may be told what class boundaries to
More information