Variables analyzed (Yahoo finance) Boeing (BA) NASDAQ Dell General Electric (GE) General Motors (GM) IBM McDonalds (MD) Microsoft (MSFT) Coca Cola Pepsi.

Slides:

Advertisements

Similar presentations

$100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300.

Advertisements

Chi square.  Non-parametric test that’s useful when your sample violates the assumptions about normality required by other tests ◦ All other tests we’ve.

Engineering experiments involve the measuring of the dependent variable as the independent one has been altered, so as to determine the relationship between.

Overview of Factor Analysis Construct combinations of quantitative variablesConstruct combinations of quantitative variables Reduce a large set of variables.

LISA Short Course Series Multivariate Analysis in R Liang (Sally) Shan March 3, 2015 LISA: Multivariate Analysis in RMar. 3, 2015.

Engineering Probability and Statistics - SE-205 -Chap 4 By S. O. Duffuaa.

Factor Analysis Research Methods and Statistics. Learning Outcomes At the end of this lecture and with additional reading you will be able to Describe.

These slides are additional material for TIES4451 Lecture 5 TIES445 Data mining Nov-Dec 2007 Sami Äyrämö.

BUILDING Historians. © 2008 GMM We are experts in consumerism.

Chapter 13 Analyzing Quantitative data. LEVELS OF MEASUREMENT Nominal Measurement Ordinal Measurement Interval Measurement Ratio Measurement.

Dimension reduction : PCA and Clustering by Agnieszka S. Juncker

Chapter 14 Analyzing Quantitative Data. LEVELS OF MEASUREMENT Nominal Measurement Nominal Measurement Ordinal Measurement Ordinal Measurement Interval.

Lecture 4 Normal distribution? Sample data at :

Overview of STAT 270 Ch 1-9 of Devore + Various Applications.

Goals of Factor Analysis (1) (1)to reduce the number of variables and (2) to detect structure in the relationships between variables, that is to classify.

Inequalities What are the meanings of these symbols?

1 Statistical Tools for Multivariate Six Sigma Dr. Neil W. Polhemus CTO & Director of Development StatPoint, Inc. Revised talk:

The Tutorial of Principal Component Analysis, Hierarchical Clustering, and Multidimensional Scaling Wenshan Wang.

Chapter 2 Dimensionality Reduction. Linear Methods

Principal Components Analysis BMTRY 726 3/27/14. Uses Goal: Explain the variability of a set of variables using a “small” set of linear combinations of.

Normal Distribution u Note: other distributions –hypergoemetric - sampling with replacement –beta –bimodal –VanGenuchten u Normal Probability Density Function.

$100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300.

Factor Analysis Istijanto MM, MCom. Definition Factor analysis  Data reduction technique and summarization  Identifying the underlying factors/ dimensions.

Research Project Statistical Analysis. What type of statistical analysis will I use to analyze my data? SEM (does not tell you level of significance)

1 Applied Statistics Using SAS and SPSS Topic: Factor Analysis By Prof Kelly Fan, Cal State Univ, East Bay.

Data set Proteins consumption shows the estimates of the average protein consumption from different food sources for the inhabitants of 25 European countries.

Techniques for studying correlation and covariance structure Principal Components Analysis (PCA) Factor Analysis.

Principal Component Analysis. Objective: Project the data onto a lower dimensional space while minimizing the information loss.

* Extra Credit* 100pts 50pts and 25 pts for the students that have the closest predictions for the Close of the DOW and/or the NASDAQ on June 2 th Thursday.

EE4-62 MLCV Lecture Face Recognition – Subspace/Manifold Learning Tae-Kyun Kim 1 EE4-62 MLCV.

Principal components were computed for normalized intensities from samples of the given tissue-chip combination. The first two principal components are.

Analyzing Statistical Inferences July 30, Inferential Statistics? When? When you infer from a sample to a population Generalize sample results to.

CHAPTER 10 Analysing quantitative data and formulating conclusions.

Statistics for Business and Economics 8 th Edition Chapter 7 Estimation: Single Population Copyright © 2013 Pearson Education, Inc. Publishing as Prentice.

 Coke, Nike, Apple.  Would you consider buying stock in the companies that make these products?  Why? Why Not?

Statistics and Probability Theory

Beginning Statistics Table of Contents HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008 by Hawkes Learning Systems/Quant Systems, Inc.

Genotype x Environment Interactions Analyses of Multiple Location Trials.

Basic statistical concepts and techniques Mean and variance Probability distribution, and statistical significance Harmonic analysis and power spectrum.

Factor Analysis Basics. Why Factor? Combine similar variables into more meaningful factors. Reduce the number of variables dramatically while retaining.

Oceanography 569 Oceanographic Data Analysis Laboratory Kathie Kelly Applied Physics Laboratory 515 Ben Hall IR Bldg class web site: faculty.washington.edu/kellyapl/classes/ocean569_.

Chapter 14 EXPLORATORY FACTOR ANALYSIS. Exploratory Factor Analysis  Statistical technique for dealing with multiple variables  Many variables are reduced.

FACTOR ANALYSIS & SPSS. First, let’s check the reliability of the scale Go to Analyze, Scale and Reliability analysis.

Stat240: Principal Component Analysis (PCA). Open/closed book examination data >scores=as.matrix(read.table(" hs.leeds.ac.uk/~charles/mva-

Statistics -Continuous probability distribution 2013/11/18.

Chapter 12 REGRESSION DIAGNOSTICS AND CANONICAL CORRELATION.

Engineering Probability and Statistics - SE-205 -Chap 4

EXPLORATORY FACTOR ANALYSIS (EFA)

Statistics in WR: Lecture 11

Showcasing the use of Factor Analysis in data reduction: Research on learner support for In-service teachers Richard Ouma University of York SPSS Users.

Dimension reduction : PCA and Clustering by Agnieszka S. Juncker

احد حیدری پژوهشگر و مشاور مهارتهای تجاری

AP Statistics: Chapter 7

Applied Statistics Using SAS and SPSS

Measuring latent variables

Quality Control at a Local Brewery

Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.

Measuring latent variables

Measuring latent variables

Principal Components Analysis

Chapter_19 Factor Analysis

Data Transformation, T-Tools and Alternatives

Factor Analysis (Principal Components) Output

Applied Statistics Using SPSS

MSFT GE About the Stocks April 16, 1997 – January 25, days

Copyright © 2014 Elsevier Inc. All rights reserved.

财务管理案例教学法研究及示例 ——王遐昌 2006/11/10.

Measuring latent variables

CSIC 5011 Project 1: Finance Data PCA, Parallel Analysis

Presentation transcript:

Variables analyzed (Yahoo finance) Boeing (BA) NASDAQ Dell General Electric (GE) General Motors (GM) IBM McDonalds (MD) Microsoft (MSFT) Coca Cola Pepsi Cola

Original data (Yahoo finance)

Logarithm base 10 of data

Scree plot (values of PC’s variances)

First two principal components

Loadings

First two principal components less the means, sign corrected

Detrended time series (STL)

Normal Probability Plots for Detrended Data BANASDAQ MSFTPEPSI

Scree plot (values of PC’s variances)

Loadings

Chi-square Normality test using original data (p=10)

Chi-square Normality test (BA+NASDAQ)

Normality test (BA+NASDAQ) Boeing NASDAQ

Chi-square Normality test (NASDAQ+DELL)

Normality test (NASDAQ+DELL) NASDAQ DELL

Chi-square Normality test using PCs (p=10)

Chi-square Normality test using PC1+PC2

Normality test using PC1+PC2 PC1 PC2

Normality test, using PC1 + PC2 Principal component 1 Principal component 2

Chi-square Normality test using PC3 + …+ PC10

Relationship between PC1 and data Principal component 1 BOEING

Relationship between PC1 and data Principal component 1 NASDAQ

Relationship between PC3 and data Principal component 3 COCA

Time series of statistical distance for PC3, …, PC10 Time series of statistical distance for PC1 and PC2

First two PCs for 3000 initial data points PC1 PC2

First two PCs for 3000 initial data points PC1 PC2

First two PCs for data points PC1 PC2

First two PCs for data points PC1 PC2

First two PCs for data points PC1 PC2