PPT-Principal Components Analysis with

SPSS Karl L Wuensch Dept of Psychology East Carolina University When to Use PCA You have a set of p continuous variables You want to repackage their variance into m components You will usually want . 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 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 . 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. Pattern Analysis. Finding patterns among objects on which two or more independent variables have been measured. . Principal Coordinates Analysis . (PCO). Principal . Components Analysis. . (PCA) (. 1. Code of Virginia. § 22.1-294. Principals and assistant principals who have achieved continuing contract status shall be formally evaluated at least once every three years and evaluated informally at least once each year that they are not formally evaluated. Probationary principals and assistant principals shall be evaluated each school year. . Key Ideas, Your Role, and Your School’s Leading and Lagging Indicators . Alexis . Nordin. Research Associate III. Goals for Today. Overview of the Principal Evaluation Components & Timeline. Introduction to the MPES Goal-Setting & Scoring Process. OF MULTIVARIATE STATISTICAL . METHOD . IN THE STUDY OF . MORPHOLOGICAL. . FEATURES OF TILAPIA CABREA.  . By.  . Bartholomew A. . Uchendu. (. Ph.D. ).  . Department of . Maths. /Statistics, Federal Polytechnic, . Gavin Band. Why do PCA?. PCA is good at detecting “directions” of major variation in your data. This might be:. Population structure – subpopulations having different allele frequencies.. Unexpected (“cryptic”) relationships.. AND IF THEY’RE NOT… THEY SHOULD BE!. *30, 40, 50% OR MORE OF YOUR STUDENT BODY PARTICIPATING IN ONE PROGRAM. *ONE PERSON DIRECTLY RESPONSIBLE FOR THE WHOLE PROGRAM!. AND VICE VERSA. THE PRINCIPAL SHOULD BE THE ATHLETIC DIRECTOR’S BEST FRIEND. Presented By Rebecca Crawford and Shane F. Lis. We will begin with a review. Common definitions create a common understanding.. While each individual perceives events, ideas, theories, and practices differently based on previous experiences creating a common definition will allow us to collaborate more effectively . Gavin Band. Why do PCA?. PCA is good at detecting “directions” of major variation in your data. This might be:. Population structure – subpopulations having different allele frequencies.. Unexpected (“cryptic”) relationships.. Karl L. Wuensch. Dept of Psychology. East Carolina University. When to Use PCA. You have a set of . p. continuous variables.. You want to repackage their variance into . m. components.. You will usually want . Components - fastenings. Components. Components are the bits and pieces that you add to your fabric to make a textile product. They are pre-manufactured. They can be either FUNCTIONAL, like a zip, or DECORATIVE, like lace edging, or both.. Karl L. Wuensch. Dept of Psychology. East Carolina University. When to Use PCA. You have a set of . p. continuous variables.. You want to repackage their variance into . m. components.. You will usually want .