PPT-Geochemical Modeling and Principal Component Analysis of the Dexter Pit Lake, Tuscarora,

Connor Newman University of Nevada Reno 5192014 Outline for Today Site background Methods Statistics Computer modeling Results Summary and Conclusions Shevenell et al 1999 Nevada Pit Lakes. 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) (. 2-1. Classification (Types) of Chemical Analysis. Qualitative analysis. ; determines the kinds of the constituents in the sample. Identifies the presence of the substance. Quantitative analysis. ; determines not only kinds but also exact amounts of the constituents in the samples. . 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. 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, . 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. 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 . Craig Stow. Integrated Physical & Ecological Modeling & Forecasting. Image from. 8-23-15. ^. Phosphorus. 1978 GLWQA. . Hear ye! Hear ye!. By Joint Proclamation. Henceforth and forever after. R. Scott . McCleery. , Ronald R. McDowell, Jessica P. Moore, Nagasree Garapati*, Timothy R. Carr, Brian J. Anderson. West Virginia University. GRC Annual Meeting & Expo, October 14-17, 2018, Reno, Nevada, USA. Department of Chemical Engineering. Institute . for Polymer . Research (IPR), University . of . Waterloo. 4. 0. th. Annual Symposium on Polymer Science/Engineering. Wednesday, May 9. th. , 2018. Alison J. .