PPT-Principal Component Analysis Applied to Polymeric Sensing Materials

Department of Chemical Engineering Institute for Polymer Research IPR University of Waterloo 4 0 th Annual Symposium on Polymer ScienceEngineering Wednesday May 9 th 2018 Alison J . 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 . Linear . Discriminant. Analysis. Chaur. -Chin Chen. Institute of Information Systems and Applications. National . Tsing. . Hua. University. Hsinchu. . 30013, Taiwan. E-mail: cchen@cs.nthu.edu.tw. Pattern Analysis. Finding patterns among objects on which two or more independent variables have been measured. . Principal Coordinates Analysis . (PCO). Principal . Components Analysis. . (PCA) (. . VARIABLE STAR LIGHT CURVES. Principal Component Analysis (PCA). Method developed by Karl Pearson in 1901. Primarily used as a statistical tool in exploratory data analysis. Linearly transforms the data matrix into a space where each orthogonal basis vector is ordered in decreasing variance along its direction. Arnaud . Czaja. (SPAT Data analysis lecture Nov. 2011). Outline. Motivation. Mathematical formulation . (on the board). Illustration: analysis of ~100yr of sea surface temperature fluctuations in the North Atlantic. 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, . One of the most pressing challenges of the new construction and maintenance of existing buildings and structures is a hydro-protection and restoration of the bearing capacity of building structures. Water acts on structures in the outer or inner side (atmospheric and groundwater).. prcomp. {stats. }. . Performs a principal components analysis on the given . data . matrix and . . . returns . the results as an object of class . prcomp. .. Usage. prcomp. (x. , . …). . VARIABLE STAR LIGHT CURVES. Principal Component Analysis (PCA). Method developed by Karl Pearson in 1901. Primarily used as a statistical tool in exploratory data analysis. Linearly transforms the data matrix into a space where each orthogonal basis vector is ordered in decreasing variance along its direction. Project report by:. Surabhi Anurag, Trushit Vaishnav, . Shikha. . Varkie. Group 2. 1. USE OF DATA ANALYTICS TO PREDICT . THE DEMAND OF BIKES. Business Objective. To determine the demand for the bike rentals based upon the various parameters such as temperature, working day, humidity, weather, windspeed etc.. 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 . Linear . Discriminant. Analysis. Chaur. -Chin Chen. Institute of Information Systems and Applications. National . Tsing. . Hua. University. Hsinchu. . 30013, Taiwan. E-mail: cchen@cs.nthu.edu.tw. 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 . Project report by:. Surabhi Anurag, Trushit Vaishnav, . Shikha. . Varkie. Group 2. 1. USE OF DATA ANALYTICS TO PREDICT . THE DEMAND OF BIKES. Business Objective. To determine the demand for the bike rentals based upon the various parameters such as temperature, working day, humidity, weather, windspeed etc..