Linear Discriminant Analysis
Linear Discriminant Analysis Diabetes Data Set I Two input variables computed from the principal components of the original 8 variables. I Prior probabilities: ˆπ 1 = 0.651, ˆπ 2 = 0.349. I µˆ 1 = (−0.4035,−0.1935)T, ˆµ 2 = (0.7528,0.3611)T. I Σ =ˆ 1.7925 −0.1461 −0.1461 1.6634 I Classification rule: Gˆ(x) = ˆ 1 0.7748−0.6771x
Tags:
Analysis, Linear, Principal, Discriminant, Linear discriminant analysis
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Advertisement
Documents from same domain
Robin Hood Case Analysis - Pennsylvania State …
personal.psu.eduRobin Hood and his band of Merrymen is the subject of this case study. Throughout this study, you will find several problems that face the group and …
ELECTRIC POWER SYSTEMS - Pennsylvania State …
personal.psu.edu1.1.1 Introduction 1 ... 1.3.2 Electric Circuits 12 ... write about electric power systems in a way that is accessible to audiences who have
Introduction, System, Electric, Power, Circuit, Electric power systems, Electric circuits
Gelatin Sepharose - Pennsylvania State University
personal.psu.eduinstructions i 71-7094-00 Edition AD Gelatin Sepharose Gelatin Sepharose™ 4B is gelatin coupled to Sepharose 4B by the cyanogen bromide method. Gelatin binds …
Agilent, Sepharose, Gelatin sepharose, Gelatin sepharose gelatin sepharose
Game Theory Lecture Notes
personal.psu.eduGame Theory: Penn State Math 486 Lecture Notes Version 1.1.1 Christopher Gri n « 2010-2012 Licensed under aCreative Commons Attribution-Noncommercial-Share Alike 3.0 United States License
Lecture, Notes, Lecture notes, Games, Theory, Game theory, Game theory lecture notes
“A Raisin in the Sun” Pre-Reading Lesson
personal.psu.edu“A Raisin in the Sun” Pre-Reading Lesson Prior to the class beginning to read the play “A Raisin in the Sun”, we will use two outside texts to discuss a few of the larger themes in …
Spectroscopic Notation - Pennsylvania State University
personal.psu.eduSimilarly, spectroscopic notation just gives one number for the sum of all the electron spins, i.e., S⃗ = ∑ i ⃗si (12:03) Since electrons can only have spin-up or spin-down, a 2-electron system can only have S = 0 or S = 1. For light elements, \LS coupling" is a good rule; this simply means that the total angular momentum of an atom is ...
Dissection of the Spiny Dogfish Shark – Squalus acanthias ...
personal.psu.eduThe ventral aorta is the main ventral blood vessel in the head. Branches from the ventral aorta, the afferent branchial arteries, carry the deoxygenated blood to the gills, where oxygenation of the blood occurs. Circulatory system - Arteries As mentioned above, the ventral aorta and the afferent branchial arteries transport the deoxygenated blood
Logistic Regression
personal.psu.eduLogistic Regression Fitting Logistic Regression Models I Criteria: find parameters that maximize the conditional likelihood of G given X using the training data. I Denote p k(x i;θ) = Pr(G = k |X = x i;θ). I Given the first input x 1, the posterior probability of its class being g 1 is Pr(G = g 1 |X = x 1). I Since samples in the training data set are independent, the
Model, Logistics, Regression, Logistic regression, Logistic regression models
THE INFORMATION SYSTEMS ENVIRONMENT
personal.psu.eduThe Information Systems Environment and IS Accreditation An information systems environment is an area in which information systems professionals can apply technology skills professionally in an organization. To fit the intent of the Information Systems Accreditation criteria, the complex processes that
Introduction to log-linear models
personal.psu.eduof PROC FREQ and PROC GENMOD procedures. Statistics for Table of pview by choice Statistic DF Value Prob-----Chi-Square 4 238.5354 <.0001 Likelihood Ratio Chi-Square 4 247.6951 <.0001... Criteria For Assessing Goodness Of Fit Criterion DF Value Value/DF Deviance 4 247.6951 61.9238
Related documents
A Tutorial on Principal Component Analysis
www.cs.cmu.eduPrincipal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but poorly understood. The goal of this paper is to dispel the magic behind this black box. This tutorial focuses on building a solid intuition for how and why principal component
Lecture 15 Factor Models - MIT OpenCourseWare
ocw.mit.eduStatistical Factor Models: Factor Analysis Principal Components Analysis Statistical Factor Models: Principal Factor Method. Fama-French Approach (Eugene Fama and Kenneth French) For every time period t;apply cross-sectional sorts to de ne factor realizations. For a given asset attribute, sort the assets at
Lecture, Analysis, Model, Factors, Principal, Mit opencourseware, Opencourseware, Lecture 15 factor models, Analysis principal
Title stata.com pca — Principal component analysis
www.stata.com2pca— Principal component analysis Syntax Principal component analysis of data pca varlist if in weight, options Principal component analysis of a correlation or covariance matrix pcamat matname, n(#) optionspcamat options matname is a k ksymmetric matrix or a k(k+ 1)=2 long row or column vector containing the
Methodological Analysis of Principal Component Analysis ...
ijcem.orgMethodological Analysis of Principal Component Analysis (PCA) Method. PCA is a statistical approach used for reducing the number of variables which is most widely used in face recognition. In PCA, every image in the training set is represented as a linear combination of weighted eigenvectors called eigenfaces. ...
Analysis, Principal component analysis, Principal, Component
A tutorial for Discriminant Analysis of Principal ...
adegenet.r-forge.r-project.orgUsual approaches such as Principal Component Analysis (PCA) or Principal Coordinates Analysis (PCoA / MDS) focus on VAR(X). That is, they only describe the global diversity, possibly overlooking di erences between groups. On the contrary, DAPC optimizes B(X) while minimizing W(X): it seeks synthetic variables, the discriminant functions, which show
An Introduction to Instrumental Methods of Analysis
blamp.sites.truman.eduInstrumental methods of chemical analysis have become the principal means of obtaining information in diverse areas of science and technology. The speed, high sensitivity, low limits of detection, simultaneous detection capabilities, and automated operation of modern instruments, when compared to classical methods of analysis, have
Getting Started in Factor Analysis (using Stata 10)
www.princeton.eduFactor analysis is used mostly for data reduction purposes: – To get a small set of variables (preferably uncorrelated) from a large set of ... Principal-components factoring. Total variance accounted by each factor. The sum of all eigenvalues = total number of variables.
Analysis, Using, Factors, Principal, Getting, Started, Getting started in factor analysis
An Introduction to Latent Semantic Analysis
lsa.colorado.edudecomposition performed by a computer algorithm, an analysis that captures much indirect information contained in the myriad constraints, structural relations and mutual entailments latent in the local observations available to experience. The principal support for these claims has come from using LSA to derive measures
Analysis, Principal, Talent, Semantics, Latent semantic analysis
Principal Components Analysis - Carnegie Mellon University
www.stat.cmu.edu354 CHAPTER 18. PRINCIPAL COMPONENTS ANALYSIS Setting the derivatives to zero at the optimum, we get wT w = 1 (18.19) vw = λw (18.20) Thus, desired vector w is an eigenvector of the covariance matrix v, and the maxi-