'Scree plot' diaporamas de présentation

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Statistical Tools for Multivariate Six Sigma

Statistical Tools for Multivariate Six Sigma. Dr. Neil W. Polhemus CTO & Director of Development StatPoint, Inc. The Challenge. The quality of an item or service usually depends on more than one characteristic.

922 views • 66 slides

Statistical Tools for Multivariate Six Sigma

Statistical Tools for Multivariate Six Sigma. Dr. Neil W. Polhemus CTO & Director of Development StatPoint, Inc. The Challenge. The quality of an item or service usually depends on more than one characteristic.

788 views • 66 slides

Principal Components Analysis with SPSS

Principal Components Analysis with 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 m to be < p , but not always.

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Adaptive Data Collection in Environmental Sensor Networks

Adaptive Data Collection in Environmental Sensor Networks. Jayant Gupchup gupchup@jhu.edu. Cub Hill Network. Question. Downloading data from everyone is expensive Radio usage consumes power Most of the time nothing interesting is happening Data is strongly correlated. Proposal.

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Happiness comes not from material wealth but less desire.

Happiness comes not from material wealth but less desire. . Applied Statistics Using SAS . Topic: Factor Analysis By Prof Kelly Fan, Cal State Univ, East Bay. Outline. Introduction Principal component analysis Rotations. Introduction. Reduce data

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B AD 6243: Applied Univariate Statistics

B AD 6243: Applied Univariate Statistics. Factor Analysis Professor Laku Chidambaram Price College of Business University of Oklahoma. Factor Analysis.

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Covariance Vs Correlation Matrix

Covariance Vs Correlation Matrix Use covariance or correlation matrix? If variables are not in the same units ? Use Correlations Dim(V) =Dim(R) = pxp and if p is large ? Dimension reduction. Sample Correlation Matrix. Scatterplot Matrix. Linear Algebra

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Dimension Reduction

Dimension Reduction. Examples: 1. DNA MICROARRAYS: Khan et al (2001): 4 types of small round blue cell tumors (SRBCT) Neuroblastoma (NB) Rhabdomyosarcoma (RMS) Ewing family of tumors (EWS) Burkitt lymphomas (BL) Arrays: Training set= 63 arrays(23 EWS, 20 RMS, 12 NB, 8 BL)

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Factor Analysis Basics

Factor Analysis Basics. Why Factor?. Combine similar variables into more meaningful factors. Reduce the number of variables dramatically while retaining most of the explanatory power, for subsequent analyses, such as Clustering. Factor Types, Variables. R-type (commonly used)

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Unreduced Matrix Scree Plot for Example Data Set from Book

Unreduced Matrix Scree Plot for Example Data Set from Book.

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