PPT-Variance and its components

Introduction Population mean gives no idea about the phenotypic values recorded on different individuals whether values are same or different If values are same or similar then population mean also will be the same If values are different from individual to individual then population mean cannot tell about the distribution of values around the central value the population mean. 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 . 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. 1) Values & means: summary. 2) Variance. 3) . Epigenetics. (. prof. . Slagboom. LUMC). 4) Assignments chapter 7. Values & means: summary. (Falconer & Mackay: chapter 7). Sanja Franic. VU University Amsterdam 2011. Mohammadreza Mohebbi. Department of epidemiology and preventive medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne . . 1) Basics. 2) Means and Values (Ch 7): summary. 3. ) Variance (Ch 8): summary. 4) Resemblance between relatives. 5) Homework (8.3). Values & means: summary. (Falconer & Mackay: chapter 7). Sanja Franic. 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. Mohammadreza Mohebbi. Department of epidemiology and preventive medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne . . Prof. Andy Field. Slide . 2. Aims. Explore factor . a. nalysis and . p. rincipal . c. omponent . a. nalysis (PCA). What . Are . factors. ?. Representing . factors. Graphs and Equations. Extracting factors. 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 . Gage R&R Estimating measurement components Gage capability and acceptability measures Prof. Tom Kuczek, Purdue Univ. 1 Terms and Definitions • Repeatability refers to the measurement variation obtained when one person repeatedly measures the same item with the same gage. PCA: principal component analysis. Correspondence analysis. Canonical correlation. Discriminant function analysis. Cluster analysis. MANOVA. PCA. Given a set of variables . x. 1. , . x. 2. , …, . x. 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 . From . prices. to . returns. . Mean. => =AVERAGE(X). Variance. => =VARP(X). Standard . deviation. => =STDEVP(X). Covariance. and . correlation. Degree. to . which. the . returns. on the .