PPT-Factor Analysis and Principal Components

Removing Redundancies and Finding Hidden Variables Two Goals Measurements are not independent of one another and we need a way to reduce the dimensionality and remove collinearity Principal components. Hopefully everyone has settled into new ro utines and rhythms as we transition into autumn Pl ease join us in welcoming our newest Eagle staff members Jacqueline Damon G8 Science Humanities Jennifer Jones G6 Science Humanities Melina Dyer Mathemat Over the p ast two months I have had the opportunity to meet staff students and parents to accelerate my acclimation process D uring this time it has been fantastic to see Shahala through the eyes of those who learn and work here each and every day in Regression . Principal Components Analysis. Standing Heights and Physical Stature Attributes Among Female Police Officer Applicants. S.Q. . Lafi. and J.B. . Kaneene. (1992). “An Explanation of the Use of Principal Components Analysis to Detect and Correct for . -3-2-10123 -3-2-10123 ************************************************** -0.50.00.5 -0.50.00.5 UrbanPop Scaled -100-50050100150 -100-50050100150 First Principal ComponentSecond Principal Component *** Advanced Psychological Statistics, II. April 7, . 2011. The Plan for Today. Introduce exploratory factor analysis.. Historical applications in psychology.. Basic concepts, extraction, rotation.. Determining number of factors.. AnnMaria De Mars, PhD.. The Julia Group & 7 Generation Games . WHY?. Imagine this. What exactly were you planning on doing with that?. Let’s say you have a massive pile of data …. . You Could:. 07/05/13 DRAFT. Principal Effectiveness. Why Important and Why Now?. Effective school leadership has an impact on developing a culture focused on student achievement. As noted in the Wallace Foundation . Prepared by. Ferry . Dzulkifli. Tita. . Borshalina. FACTOR ANALYSIS. Factor Analysis Defined. Factor analysis . . .. is an interdependence technique whose primary purpose is to define the underlying structure among the variables in the analysis.. Lua Augustin, Savannah Guo, and Blair Marquardt. Learning Outcomes. To understand. :. What . is factor analysis.. What is its model.. Latent vs. observable variables; examples of each. .. Potential applications of factor analysis. Principal Component Analysis. Chapter 17. Terminology. Measured variables – the real scores from the experiment. Squares on a diagram. Latent variables – the construct the measured variables are supposed to represent. STA431: Spring . 2015. See last slide for copyright information. Factor Analysis: The Measurement Model. D. 1. D. 8. D. 7. D. 6. D. 5. D. 4. D. 3. D. 2. F. 1. F. 2. Example with 2 factors and 8 observed variables. Bamshad Mobasher. DePaul University. Principal Component Analysis. PCA is a widely used data . compression and dimensionality reduction technique. PCA takes a data matrix, . A. , of . n. objects by . The basic objective of Factor Analysis is data reduction or structure detection.. The purpose of . data reduction.  is to remove redundant (highly correlated) variables from the data file, perhaps replacing the entire data file with a smaller number of uncorrelated variables.. in the Maryland Soils Database”. Presented by James Brewer. Resource Soil Scientist - Easton, MD. Webinar. Thursday Feb. 23, 2012. 1:00 -2:00 PM. ANY QUESTIONS!!. Don’t hesitate to ask!. WEB. INAR OBJECTIVES.