L i s t i n g o f C o m m a n d F i l e
|
|
|
- Robert Sullivan
- 6 years ago
- Views:
Transcription
1 E X P L O R E R R e l e a s e 3. 0 A Program for Common Factor Analysis and Related Models Copyright (c) , J. S. Fleming, PhD Date and Time : 12-Feb-2011, 10:30:27 Number of variables : 5 Command file : mcd.txt Listing file : mcd.lis L i s t i n g o f C o m m a n d F i l e <data> matrix = cov; file = 'mcd.cov1'; shape=lt; save; <headers> 'Covariance Data for Diener Satisfaction with Life Scale'; 'From McDonald: Test Theory: A Unified Treatment (1999, p. 87)'; <analysis> method=pf; matrix=cov; com = bigr; number = 1; <labels> 'Ideal'; 'Excellnt'; 'Satisfd'; 'GotAll'; 'NoChange'; <output> all; <end>
2 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 2 Correlational Analysis V a r i a n c e - C o v a r i a n c e M a t r i x Ideal Excellnt Satisfd GotAll NoChange 1 Ideal Excellnt Satisfd GotAll NoChange
3 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 3 Correlational Analysis C o r r e l a t i o n M a t r i x Ideal Excellnt Satisfd GotAll NoChange 1 Ideal Excellnt Satisfd GotAll NoChange
4 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 4 Latent Roots and Vectors E i g e n v a l u e s o f R No. Value % Trace Cum. % No. Value % Trace Cum. % R is the unaltered correlation matrix. Theta (reliability of first principal component) = Condition number (largest/smallest eigenvalue) = E+01 The trace of a matrix is the sum of it's diagonal elements. If the matrix is Gramian (no eigenvalues less than zero), then the trace represents the total variance. If some of the eigenvalues are negative then percentages are based on just non-negative elements. For this problem the positive trace is: Total iterations for eigenvalues was: 7.
5 Covariance Data for Diener Satisfaction with Life Scale E X P L O R E From McDonald: Test Theory: A Unified Treatment (1999, p. 87) P a g e 5 E I G E N V A L U E P L O T !...!...!...!...!...!...!...!...!...!...!...!...!...!...!...!...! * E I G E N V A L U E S * -. *.. * * !...!...!...!...!...!...!...!...!...!...!...!...!...!...!...!...! N U M B E R O F F A C T O R S
6 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 6 Partial/Multiple Correlation P a r t i a l C o r r e l a t i o n s Ideal Excellnt Satisfd GotAll NoChange 1 Ideal Excellnt Satisfd GotAll NoChange (Diagonal values are squared-multiple correlations. Off-diagonals are correlations between each variable pair, with all others partialled out.)
7 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 7 Partial/Multiple Correlation M e a s u r e s o f S a m p l i n g A d e q u a c y 1 Ideal Satisfd NoChange Excellnt GotAll Total MSA = 0.985, median = Kaiser-Myer-Olkin statistic = (Note: This is Kaiser's final (1981) version of the MSA. A value of means that the MSA is unacceptable. KMO is an earlier version of MSA)
8 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 8 Latent Roots and Vectors I n i t i a l E i g e n v a l u e s o f C* No. Value % Trace Cum. % No. Value % Trace Cum. % C* is the 'reduced' covariance matrix. The trace of a matrix is the sum of it's diagonal elements. If the matrix is Gramian (no eigenvalues less than zero), then the trace represents the total variance. If some of the eigenvalues are negative then percentages are based on just non-negative elements. For this problem the positive trace is: *** Warning: Negative eigenvalues found. Matrix is non-grammian.
9 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 9 Initial Factor Solution Common factor extraction summary: Method = Principal Axes Initial communality estimates are largest off-diagonal elements. Iteration Summary: Iteration Criterion Pct. Meeting Criterion
10 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 10 Latent Roots and Vectors F i n a l E i g e n v a l u e s o f C* No. Value % Trace Cum. % No. Value % Trace Cum. % C* is the 'reduced' covariance matrix. The trace of a matrix is the sum of it's diagonal elements. If the matrix is Gramian (no eigenvalues less than zero), then the trace represents the total variance. If some of the eigenvalues are negative then percentages are based on just non-negative elements. For this problem the positive trace is:
11 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 11 Initial Factor Solution C o m m u n a l i t i e s Difference Unique No. Name Initial Final (Initial - Final) Variances 1 Ideal Excellnt Satisfd GotAll NoChange Means: Medians:
12 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 12 Initial Factor Solution I n i t i a l L o a d i n g s F 1 1 Ideal Excellnt Satisfd GotAll NoChange Reliabilities for first (general) factor: Coefficient omega = Coefficient alpha = 0.817
13 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 13 Initial Factor Solution S t a n d a r d i z e d L o a d i n g s F 1 1 Ideal Excellnt Satisfd GotAll NoChange 0.647
14 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 14 Residuals Analysis S t a n d a r d i z e d R e s i d u a l s Ideal Excellnt Satisfd GotAll NoChange 1 Ideal Excellnt Satisfd GotAll NoChange (Residual correlations are remainders after subtracting reproduced correlations from original correlations. If a covariance matrix was factored then residuals were standardized.)
15 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 15 Residuals Analysis Distribution of Standardized Residuals (Based on sub-diagonal elements of correlation matrix): Interval Freq. Pct. H i s t o g r a m > **** ******************** ************ **** < (Note: Each symbol represents 0.25 correlations.) Residual Summary Statistics (for sub-diagonal elements): Minimum value = (between GotAll and Excellnt) Maximum value = (between NoChange and GotAll ) Average (root-mean-square) = 0.084
16 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 16 Residuals Analysis Largest Residual for Each Variable Pair 1 Ideal with 2 Excellnt = Excellnt with 4 GotAll = Satisfd with 5 NoChange = GotAll with 2 Excellnt = NoChange with 4 GotAll = 0.173
17 From McDonald: Test Theory: A Unified Treatment (1999, p. 87) Page 17 Initial Factor Solution F a c t o r S c o r e W e i g h t s F 1 1 Ideal Excellnt Satisfd GotAll NoChange (Factor scores weights are estimated by regression method.)
E X P L O R E R. R e l e a s e A Program for Common Factor Analysis and Related Models for Data Analysis
E X P L O R E R R e l e a s e 3. 2 A Program for Common Factor Analysis and Related Models for Data Analysis Copyright (c) 1990-2011, J. S. Fleming, PhD Date and Time : 23-Sep-2011, 13:59:56 Number of
Or, in terms of basic measurement theory, we could model it as:
1 Neuendorf Factor Analysis Assumptions: 1. Metric (interval/ratio) data 2. Linearity (in relationships among the variables--factors are linear constructions of the set of variables; the critical source
VAR2 VAR3 VAR4 VAR5. Or, in terms of basic measurement theory, we could model it as:
1 Neuendorf Factor Analysis Assumptions: 1. Metric (interval/ratio) data 2. Linearity (in the relationships among the variables) -Factors are linear constructions of the set of variables (see #8 under
Applied Multivariate Analysis
Department of Mathematics and Statistics, University of Vaasa, Finland Spring 2017 Dimension reduction Exploratory (EFA) Background While the motivation in PCA is to replace the original (correlated) variables
Principal Components. Summary. Sample StatFolio: pca.sgp
Principal Components Summary... 1 Statistical Model... 4 Analysis Summary... 5 Analysis Options... 7 Scree Plot... 8 Component Weights... 9 D and 3D Component Plots... 10 Data Table... 11 D and 3D Component
Package rela. R topics documented: February 20, Version 4.1 Date Title Item Analysis Package with Standard Errors
Version 4.1 Date 2009-10-25 Title Item Analysis Package with Standard Errors Package rela February 20, 2015 Author Michael Chajewski Maintainer Michael Chajewski
Factor Analysis. Summary. Sample StatFolio: factor analysis.sgp
Factor Analysis Summary... 1 Data Input... 3 Statistical Model... 4 Analysis Summary... 5 Analysis Options... 7 Scree Plot... 9 Extraction Statistics... 10 Rotation Statistics... 11 D and 3D Scatterplots...
2/26/2017. This is similar to canonical correlation in some ways. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2
PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 What is factor analysis? What are factors? Representing factors Graphs and equations Extracting factors Methods and criteria Interpreting
MSP Research Note. RDQ Reliability, Validity and Norms
MSP Research Note RDQ Reliability, Validity and Norms Introduction This research note describes the technical properties of the RDQ. Evidence for the reliability and validity of the RDQ is presented against
Multivariate and Multivariable Regression. Stella Babalola Johns Hopkins University
Multivariate and Multivariable Regression Stella Babalola Johns Hopkins University Session Objectives At the end of the session, participants will be able to: Explain the difference between multivariable
1 A factor can be considered to be an underlying latent variable: (a) on which people differ. (b) that is explained by unknown variables
1 A factor can be considered to be an underlying latent variable: (a) on which people differ (b) that is explained by unknown variables (c) that cannot be defined (d) that is influenced by observed variables
How to Run the Analysis: To run a principal components factor analysis, from the menus choose: Analyze Dimension Reduction Factor...
The principal components method of extraction begins by finding a linear combination of variables that accounts for as much variation in the original variables as possible. This method is most often used
Factor analysis. George Balabanis
Factor analysis George Balabanis Key Concepts and Terms Deviation. A deviation is a value minus its mean: x - mean x Variance is a measure of how spread out a distribution is. It is computed as the average
Principle Components Analysis (PCA) Relationship Between a Linear Combination of Variables and Axes Rotation for PCA
Principle Components Analysis (PCA) Relationship Between a Linear Combination of Variables and Axes Rotation for PCA Principle Components Analysis: Uses one group of variables (we will call this X) In
Manual Of The Program FACTOR. v Windows XP/Vista/W7/W8. Dr. Urbano Lorezo-Seva & Dr. Pere Joan Ferrando
Manual Of The Program FACTOR v.9.20 Windows XP/Vista/W7/W8 Dr. Urbano Lorezo-Seva & Dr. Pere Joan Ferrando urbano.lorenzo@urv.cat perejoan.ferrando@urv.cat Departament de Psicologia Universitat Rovira
Short Answer Questions: Answer on your separate blank paper. Points are given in parentheses.
ISQS 6348 Final exam solutions. Name: Open book and notes, but no electronic devices. Answer short answer questions on separate blank paper. Answer multiple choice on this exam sheet. Put your name on
The Role of Leader Motivating Language in Employee Absenteeism (Mayfield: 2009)
DATE: 12/15/2009 TIME: 5:50 Page 1 LISREL 8.80 (STUDENT EDITION) BY Karl G. J reskog & Dag S rbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue,
Structural Equation Modeling and Confirmatory Factor Analysis. Types of Variables
/4/04 Structural Equation Modeling and Confirmatory Factor Analysis Advanced Statistics for Researchers Session 3 Dr. Chris Rakes Website: http://csrakes.yolasite.com Email: Rakes@umbc.edu Twitter: @RakesChris
Multivariate Statistical Analysis
Multivariate Statistical Analysis Fall 2011 C. L. Williams, Ph.D. Lecture 4 for Applied Multivariate Analysis Outline 1 Eigen values and eigen vectors Characteristic equation Some properties of eigendecompositions
Principal Component Analysis (PCA) Theory, Practice, and Examples
Principal Component Analysis (PCA) Theory, Practice, and Examples Data Reduction summarization of data with many (p) variables by a smaller set of (k) derived (synthetic, composite) variables. p k n A
Formula for the t-test
Formula for the t-test: How the t-test Relates to the Distribution of the Data for the Groups Formula for the t-test: Formula for the Standard Error of the Difference Between the Means Formula for the
Principal Component Analysis & Factor Analysis. Psych 818 DeShon
Principal Component Analysis & Factor Analysis Psych 818 DeShon Purpose Both are used to reduce the dimensionality of correlated measurements Can be used in a purely exploratory fashion to investigate
Non-linear least squares
Non-linear least squares Concept of non-linear least squares We have extensively studied linear least squares or linear regression. We see that there is a unique regression line that can be determined
Principal Components Analysis using R Francis Huang / November 2, 2016
Principal Components Analysis using R Francis Huang / huangf@missouri.edu November 2, 2016 Principal components analysis (PCA) is a convenient way to reduce high dimensional data into a smaller number
Factor Analysis Continued. Psy 524 Ainsworth
Factor Analysis Continued Psy 524 Ainsworth Equations Extraction Principal Axis Factoring Variables Skiers Cost Lift Depth Powder S1 32 64 65 67 S2 61 37 62 65 S3 59 40 45 43 S4 36 62 34 35 S5 62 46 43
Item Reliability Analysis
Item Reliability Analysis Revised: 10/11/2017 Summary... 1 Data Input... 4 Analysis Options... 5 Tables and Graphs... 5 Analysis Summary... 6 Matrix Plot... 8 Alpha Plot... 10 Correlation Matrix... 11
Chemometrics. Matti Hotokka Physical chemistry Åbo Akademi University
Chemometrics Matti Hotokka Physical chemistry Åbo Akademi University Linear regression Experiment Consider spectrophotometry as an example Beer-Lamberts law: A = cå Experiment Make three known references
SEM Day 1 Lab Exercises SPIDA 2007 Dave Flora
SEM Day 1 Lab Exercises SPIDA 2007 Dave Flora 1 Today we will see how to estimate CFA models and interpret output using both SAS and LISREL. In SAS, commands for specifying SEMs are given using linear
UCLA STAT 233 Statistical Methods in Biomedical Imaging
UCLA STAT 233 Statistical Methods in Biomedical Imaging Instructor: Ivo Dinov, Asst. Prof. In Statistics and Neurology University of California, Los Angeles, Spring 2004 http://www.stat.ucla.edu/~dinov/
Implementing Horn s parallel analysis for principal component analysis and factor analysis
The Stata Journal (2009) 9, Number 2, pp. 291 298 Implementing Horn s parallel analysis for principal component analysis and factor analysis Alexis Dinno Department of Biological Sciences California State
The Sampling Distribution of Regression Coefficients.
The Sampling Distribution of Regression Coefficients. David C. Howell Revised 2/3/2003 This whole project started with a query about the sampling distribution of the standardized regression coefficient,
B. Weaver (18-Oct-2001) Factor analysis Chapter 7: Factor Analysis
B Weaver (18-Oct-2001) Factor analysis 1 Chapter 7: Factor Analysis 71 Introduction Factor analysis (FA) was developed by C Spearman It is a technique for examining the interrelationships in a set of variables
LECTURE 4 PRINCIPAL COMPONENTS ANALYSIS / EXPLORATORY FACTOR ANALYSIS
LECTURE 4 PRINCIPAL COMPONENTS ANALYSIS / EXPLORATORY FACTOR ANALYSIS NOTES FROM PRE- LECTURE RECORDING ON PCA PCA and EFA have similar goals. They are substantially different in important ways. The goal
SAS Example 3: Deliberately create numerical problems
SAS Example 3: Deliberately create numerical problems Four experiments 1. Try to fit this model, failing the parameter count rule. 2. Set φ 12 =0 to pass the parameter count rule, but still not identifiable.
MLMED. User Guide. Nicholas J. Rockwood The Ohio State University Beta Version May, 2017
MLMED User Guide Nicholas J. Rockwood The Ohio State University rockwood.19@osu.edu Beta Version May, 2017 MLmed is a computational macro for SPSS that simplifies the fitting of multilevel mediation and
Principal Component Analysis!! Lecture 11!
Principal Component Analysis Lecture 11 1 Eigenvectors and Eigenvalues g Consider this problem of spreading butter on a bread slice 2 Eigenvectors and Eigenvalues g Consider this problem of stretching
Factor Analysis. Qian-Li Xue
Factor Analysis Qian-Li Xue Biostatistics Program Harvard Catalyst The Harvard Clinical & Translational Science Center Short course, October 7, 06 Well-used latent variable models Latent variable scale
Inter Item Correlation Matrix (R )
7 1. I have the ability to influence my child s well-being. 2. Whether my child avoids injury is just a matter of luck. 3. Luck plays a big part in determining how healthy my child is. 4. I can do a lot
Bivariate data analysis
Bivariate data analysis Categorical data - creating data set Upload the following data set to R Commander sex female male male male male female female male female female eye black black blue green green
Dimensionality Assessment: Additional Methods
Dimensionality Assessment: Additional Methods In Chapter 3 we use a nonlinear factor analytic model for assessing dimensionality. In this appendix two additional approaches are presented. The first strategy
Dimensionality Reduction: PCA. Nicholas Ruozzi University of Texas at Dallas
Dimensionality Reduction: PCA Nicholas Ruozzi University of Texas at Dallas Eigenvalues λ is an eigenvalue of a matrix A R n n if the linear system Ax = λx has at least one non-zero solution If Ax = λx
CHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA
Examples: Multilevel Modeling With Complex Survey Data CHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA Complex survey data refers to data obtained by stratification, cluster sampling and/or
Chapter 4: Factor Analysis
Chapter 4: Factor Analysis In many studies, we may not be able to measure directly the variables of interest. We can merely collect data on other variables which may be related to the variables of interest.
Simple Linear Regression
Simple Linear Regression 1 Correlation indicates the magnitude and direction of the linear relationship between two variables. Linear Regression: variable Y (criterion) is predicted by variable X (predictor)
Multivariate Statistics (I) 2. Principal Component Analysis (PCA)
Multivariate Statistics (I) 2. Principal Component Analysis (PCA) 2.1 Comprehension of PCA 2.2 Concepts of PCs 2.3 Algebraic derivation of PCs 2.4 Selection and goodness-of-fit of PCs 2.5 Algebraic derivation
The SAS System 18:28 Saturday, March 10, Plot of Canonical Variables Identified by Cluster
The SAS System 18:28 Saturday, March 10, 2018 1 The FASTCLUS Procedure Replace=FULL Radius=0 Maxclusters=2 Maxiter=10 Converge=0.02 Initial Seeds Cluster SepalLength SepalWidth PetalLength PetalWidth 1
Introduction to Factor Analysis
to Factor Analysis Lecture 10 August 2, 2011 Advanced Multivariate Statistical Methods ICPSR Summer Session #2 Lecture #10-8/3/2011 Slide 1 of 55 Today s Lecture Factor Analysis Today s Lecture Exploratory
Key Algebraic Results in Linear Regression
Key Algebraic Results in Linear Regression James H. Steiger Department of Psychology and Human Development Vanderbilt University James H. Steiger (Vanderbilt University) 1 / 30 Key Algebraic Results in
Chapter 19: Logistic regression
Chapter 19: Logistic regression Self-test answers SELF-TEST Rerun this analysis using a stepwise method (Forward: LR) entry method of analysis. The main analysis To open the main Logistic Regression dialog
RESMA course Introduction to LISREL. Harry Ganzeboom RESMA Data Analysis & Report #4 February
RESMA course Introduction to LISREL Harry Ganzeboom RESMA Data Analysis & Report #4 February 17 2009 LISREL SEM: Simultaneous [Structural] Equations Model: A system of linear equations ( causal model )
Regression ( Kemampuan Individu, Lingkungan kerja dan Motivasi)
Regression (, Lingkungan kerja dan ) Descriptive Statistics Mean Std. Deviation N 3.87.333 32 3.47.672 32 3.78.585 32 s Pearson Sig. (-tailed) N Kemampuan Lingkungan Individu Kerja.000.432.49.432.000.3.49.3.000..000.000.000..000.000.000.
Preface. List of examples
Contents Preface List of examples i xix 1 LISREL models and methods 1 1.1 The general LISREL model 1 Assumptions 2 The covariance matrix of the observations as implied by the LISREL model 3 Fixed, free,
Introduction To Confirmatory Factor Analysis and Item Response Theory
Introduction To Confirmatory Factor Analysis and Item Response Theory Lecture 23 May 3, 2005 Applied Regression Analysis Lecture #23-5/3/2005 Slide 1 of 21 Today s Lecture Confirmatory Factor Analysis.
Using Structural Equation Modeling to Conduct Confirmatory Factor Analysis
Using Structural Equation Modeling to Conduct Confirmatory Factor Analysis Advanced Statistics for Researchers Session 3 Dr. Chris Rakes Website: http://csrakes.yolasite.com Email: Rakes@umbc.edu Twitter:
Application of Plausible Values of Latent Variables to Analyzing BSI-18 Factors. Jichuan Wang, Ph.D
Application of Plausible Values of Latent Variables to Analyzing BSI-18 Factors Jichuan Wang, Ph.D Children s National Health System The George Washington University School of Medicine Washington, DC 1
Strain analysis.
Strain analysis ecalais@purdue.edu Plates vs. continuum Gordon and Stein, 1991 Most plates are rigid at the until know we have studied a purely discontinuous approach where plates are
Principal component analysis
Principal component analysis Motivation i for PCA came from major-axis regression. Strong assumption: single homogeneous sample. Free of assumptions when used for exploration. Classical tests of significance
w. T. Federer, z. D. Feng and c. E. McCulloch
ILLUSTRATIVE EXAMPLES OF PRINCIPAL COMPONENT ANALYSIS USING GENSTATIPCP w. T. Federer, z. D. Feng and c. E. McCulloch BU-~-M November 98 ~ ABSTRACT In order to provide a deeper understanding of the workings
Style Insights DISC, English version 2006.g
To: From:. Style Insights DISC, English version 2006.g Bill Bonnstetter Target Training International, Ltd. www.documentingexcellence.com Date: 12 May 2006 www.documentingexcellence.com 445 S. Julian St,
G562 Geometric Morphometrics. Statistical Tests. Department of Geological Sciences Indiana University. (c) 2012, P. David Polly
Statistical Tests Basic components of GMM Procrustes This aligns shapes and minimizes differences between them to ensure that only real shape differences are measured. PCA (primary use) This creates a
FACTOR ANALYSIS AND MULTIDIMENSIONAL SCALING
FACTOR ANALYSIS AND MULTIDIMENSIONAL SCALING Vishwanath Mantha Department for Electrical and Computer Engineering Mississippi State University, Mississippi State, MS 39762 mantha@isip.msstate.edu ABSTRACT
Factor Analysis Edpsy/Soc 584 & Psych 594
Factor Analysis Edpsy/Soc 584 & Psych 594 Carolyn J. Anderson University of Illinois, Urbana-Champaign April 29, 2009 1 / 52 Rotation Assessing Fit to Data (one common factor model) common factors Assessment
SEM Day 3 Lab Exercises SPIDA 2007 Dave Flora
SEM Day 3 Lab Exercises SPIDA 2007 Dave Flora 1 Today we will see how to estimate SEM conditional latent trajectory models and interpret output using both SAS and LISREL. Exercise 1 Using SAS PROC CALIS,
Hypothesis Testing for Var-Cov Components
Hypothesis Testing for Var-Cov Components When the specification of coefficients as fixed, random or non-randomly varying is considered, a null hypothesis of the form is considered, where Additional output
Introduction
=============================================================================== mla.doc MLA 3.2 05/31/97 =============================================================================== Copyright (c) 1993-97
. a m1 a mn. a 1 a 2 a = a n
Biostat 140655, 2008: Matrix Algebra Review 1 Definition: An m n matrix, A m n, is a rectangular array of real numbers with m rows and n columns Element in the i th row and the j th column is denoted by
Mathematics. EC / EE / IN / ME / CE. for
Mathematics for EC / EE / IN / ME / CE By www.thegateacademy.com Syllabus Syllabus for Mathematics Linear Algebra: Matrix Algebra, Systems of Linear Equations, Eigenvalues and Eigenvectors. Probability
Factor Analysis and Kalman Filtering (11/2/04)
CS281A/Stat241A: Statistical Learning Theory Factor Analysis and Kalman Filtering (11/2/04) Lecturer: Michael I. Jordan Scribes: Byung-Gon Chun and Sunghoon Kim 1 Factor Analysis Factor analysis is used
Advanced Methods for Determining the Number of Factors
Advanced Methods for Determining the Number of Factors Horn s (1965) Parallel Analysis (PA) is an adaptation of the Kaiser criterion, which uses information from random samples. The rationale underlying
Factor Analysis (1) Factor Analysis
Factor Analysis (1) Outlines: 1. Introduction of factor analysis 2. Principle component analysis 4. Factor rotation 5. Case Shan-Yu Chou 1 Factor Analysis Combines questions or variables to create new
Dimensionality Reduction Techniques (DRT)
Dimensionality Reduction Techniques (DRT) Introduction: Sometimes we have lot of variables in the data for analysis which create multidimensional matrix. To simplify calculation and to get appropriate,
Nonlinear Regression. Summary. Sample StatFolio: nonlinear reg.sgp
Nonlinear Regression Summary... 1 Analysis Summary... 4 Plot of Fitted Model... 6 Response Surface Plots... 7 Analysis Options... 10 Reports... 11 Correlation Matrix... 12 Observed versus Predicted...
FACTOR ANALYSIS AS MATRIX DECOMPOSITION 1. INTRODUCTION
FACTOR ANALYSIS AS MATRIX DECOMPOSITION JAN DE LEEUW ABSTRACT. Meet the abstract. This is the abstract. 1. INTRODUCTION Suppose we have n measurements on each of taking m variables. Collect these measurements
Principal components
Principal components Principal components is a general analysis technique that has some application within regression, but has a much wider use as well. Technical Stuff We have yet to define the term covariance,
Econ671 Factor Models: Principal Components
Econ671 Factor Models: Principal Components Jun YU April 8, 2016 Jun YU () Econ671 Factor Models: Principal Components April 8, 2016 1 / 59 Factor Models: Principal Components Learning Objectives 1. Show
Principal component analysis
Principal component analysis Angela Montanari 1 Introduction Principal component analysis (PCA) is one of the most popular multivariate statistical methods. It was first introduced by Pearson (1901) and
Data Mining Lecture 4: Covariance, EVD, PCA & SVD
Data Mining Lecture 4: Covariance, EVD, PCA & SVD Jo Houghton ECS Southampton February 25, 2019 1 / 28 Variance and Covariance - Expectation A random variable takes on different values due to chance The
Passing-Bablok Regression for Method Comparison
Chapter 313 Passing-Bablok Regression for Method Comparison Introduction Passing-Bablok regression for method comparison is a robust, nonparametric method for fitting a straight line to two-dimensional
Principal Component Analysis (PCA) of AIRS Data
Principal Component Analysis (PCA) of AIRS Data Mitchell D. Goldberg 1, Lihang Zhou 2, Walter Wolf 2 and Chris Barnet 1 NOAA/NESDIS/Office of Research and Applications, Camp Springs, MD 1 QSS Group Inc.
STA Homework 1. Due back at the beginning of class on Oct 20, 2008
STA 410 - Homework 1 Due back at the beginning of class on Oct 20, 2008 The following is split into two parts. Part I should be solved by all. Part II is mandatory only for the graduate students in Statistics.
The Reliability of a Homogeneous Test. Measurement Methods Lecture 12
The Reliability of a Homogeneous Test Measurement Methods Lecture 12 Today s Class Estimating the reliability of a homogeneous test. McDonald s Omega. Guttman-Chronbach Alpha. Spearman-Brown. What to do
UNIVERSITY OF TORONTO MISSISSAUGA April 2009 Examinations STA431H5S Professor Jerry Brunner Duration: 3 hours
Name (Print): Student Number: Signature: Last/Surname First /Given Name UNIVERSITY OF TORONTO MISSISSAUGA April 2009 Examinations STA431H5S Professor Jerry Brunner Duration: 3 hours Aids allowed: Calculator
Factor Analysis of Data Matrices
Factor Analysis of Data Matrices PAUL HORST University of Washington HOLT, RINEHART AND WINSTON, INC. New York Chicago San Francisco Toronto London Contents Preface PART I. Introductory Background 1. The
Introduction to Machine Learning
10-701 Introduction to Machine Learning PCA Slides based on 18-661 Fall 2018 PCA Raw data can be Complex, High-dimensional To understand a phenomenon we measure various related quantities If we knew what
Assignment 1. SEM 2: Structural Equation Modeling
Assignment 1 SEM 2: Structural Equation Modeling 2 Please hand in a.pdf file containing your report and a.r containing your codes or screenshots of every Jasp analysis. The deadline of this assignment
An Introduction to Path Analysis
An Introduction to Path Analysis PRE 905: Multivariate Analysis Lecture 10: April 15, 2014 PRE 905: Lecture 10 Path Analysis Today s Lecture Path analysis starting with multivariate regression then arriving
Linear Dimensionality Reduction
Outline Hong Chang Institute of Computing Technology, Chinese Academy of Sciences Machine Learning Methods (Fall 2012) Outline Outline I 1 Introduction 2 Principal Component Analysis 3 Factor Analysis
STATISTICAL LEARNING SYSTEMS
STATISTICAL LEARNING SYSTEMS LECTURE 8: UNSUPERVISED LEARNING: FINDING STRUCTURE IN DATA Institute of Computer Science, Polish Academy of Sciences Ph. D. Program 2013/2014 Principal Component Analysis
Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages:
Glossary The ISI glossary of statistical terms provides definitions in a number of different languages: http://isi.cbs.nl/glossary/index.htm Adjusted r 2 Adjusted R squared measures the proportion of the
Lecture 13. Principal Component Analysis. Brett Bernstein. April 25, CDS at NYU. Brett Bernstein (CDS at NYU) Lecture 13 April 25, / 26
Principal Component Analysis Brett Bernstein CDS at NYU April 25, 2017 Brett Bernstein (CDS at NYU) Lecture 13 April 25, 2017 1 / 26 Initial Question Intro Question Question Let S R n n be symmetric. 1
Optimal design of experiments
Optimal design of experiments Session 4: Some theory Peter Goos / 40 Optimal design theory continuous or approximate optimal designs implicitly assume an infinitely large number of observations are available
Dimensionality Reduction
Dimensionality Reduction Le Song Machine Learning I CSE 674, Fall 23 Unsupervised learning Learning from raw (unlabeled, unannotated, etc) data, as opposed to supervised data where a classification of
FERMENTATION BATCH PROCESS MONITORING BY STEP-BY-STEP ADAPTIVE MPCA. Ning He, Lei Xie, Shu-qing Wang, Jian-ming Zhang
FERMENTATION BATCH PROCESS MONITORING BY STEP-BY-STEP ADAPTIVE MPCA Ning He Lei Xie Shu-qing Wang ian-ming Zhang National ey Laboratory of Industrial Control Technology Zhejiang University Hangzhou 3007
Properties of the least squares estimates
Properties of the least squares estimates 2019-01-18 Warmup Let a and b be scalar constants, and X be a scalar random variable. Fill in the blanks E ax + b) = Var ax + b) = Goal Recall that the least squares
Bare minimum on matrix algebra. Psychology 588: Covariance structure and factor models
Bare minimum on matrix algebra Psychology 588: Covariance structure and factor models Matrix multiplication 2 Consider three notations for linear combinations y11 y1 m x11 x 1p b11 b 1m y y x x b b n1
Only Q8 is shown in this paper. Other Q & A are now being translated into English.
(c)ryo SASAKI, 8 Version. (Aug 8,8) Structural Equation Modeling (SEM) by AMOS (Last update:aug 8, 8) Q1 F-value Q Standardization Q3 Sample size Q Model fit Q5 Size of coefficients Q Equivalent model
Linear & Non-Linear Discriminant Analysis! Hugh R. Wilson
Linear & Non-Linear Discriminant Analysis! Hugh R. Wilson PCA Review! Supervised learning! Fisher linear discriminant analysis! Nonlinear discriminant analysis! Research example! Multiple Classes! Unsupervised
MULTIVARIATE HOMEWORK #5
MULTIVARIATE HOMEWORK #5 Fisher s dataset on differentiating species of Iris based on measurements on four morphological characters (i.e. sepal length, sepal width, petal length, and petal width) was subjected
Correspondence Analysis
STATGRAPHICS Rev. 7/6/009 Correspondence Analysis The Correspondence Analysis procedure creates a map of the rows and columns in a two-way contingency table for the purpose of providing insights into the
The European Commission s science and knowledge service. Joint Research Centre
The European Commission s science and knowledge service Joint Research Centre Step 7: Statistical coherence (II) Principal Component Analysis and Reliability Analysis Hedvig Norlén COIN 2018-16th JRC Annual