You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.

## Index P...

 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

 p values Interpreting p values p-chart p- and c-Charts paired experiments Paired Experiments parameter Parameters parsimonious model Modeling partial least squares Modeling with latent variables PLS - Partial Least Squares Regression PCA Literature References - Factor Analysis, Principal Components Principal Component Analysis Application Example of PCA - Classification of Wine Data Compression by PCA PCA - Loadings and Scores PCA - Different Forms PCA - Model Order Exercise - Dependence of PC scores on scaling of data Exercise - Classification of unknown wine samples by PCA Exercise - Detection of mixtures of two different wines by PCA PCR Principal Component Regression Exercise - Perform a PCR by successive application of PCA and MLR Modeling with latent variables PDF Distributions - Introduction Part 2 Pearson's correlation coefficient Pearson's Correlation Coefficient perceptron Multi-layer Perceptron permutation Matrix Determinant Counting Rules personalized textbook Courses phase angle Fourier Series phase space Phase Space pink noise Types of Noise platykurtic distribution Kurtosis PLS Modeling with latent variables PLS - Partial Least Squares Regression Poisson distribution Poisson Distribution Relationship Between Various Distributions polynomial filter Savitzky-Golay Filter - Mathematical Details polynomial fit Exercise - Calculate a polynomial fit by means of MLR Data Set - Polynomial Fit population Population and Sample power Types of Error Power of a Test precision The Data Decimal Places and Precision predictor Modeling preface Intentions of Teach/Me PRESS PCA - Model Order PRESS Validation of Models principal component regression Principal Component Regression Exercise - Perform a PCR by successive application of PCA and MLR Modeling with latent variables principal components Literature References - Factor Analysis, Principal Components Principal Component Analysis Data Compression by PCA PCA - Different Forms Principal Component Regression Exercise - Estimation of Boiling Points from Chemical Structure Exercise - Dependence of PC scores on scaling of data Exercise - Classification of unknown wine samples by PCA Exercise - Detection of mixtures of two different wines by PCA The NIPALS Algorithm principal diagonal Matrix Algebra - Fundamentals probability Distributions - Introduction Part 2 Algebra of Probabilities Bayesian Rule Conditional Probability Counting Rules Events and Sample Space Independent Events Probability - Introduction Probability Theory Exercise - Probability of Observations Exercise - Probability of a train being delayed Summation of Probabilities Additivity Rule Complementary Sets and Subsets Union and Intersection probability density function Exercise - Design a data set showing a bimodal probability density function Exercise - Design a data set showing a normal probability density function process control Control Charts p- and c-Charts x- and R-Charts process stability Control Charts process variability Variability processing unit ANN - Single Processing Unit pruning Variable Selection - Pruning pseudo random numbers Random Number Generators pseudo-inverse matrix Moore-Penrose Pseudo-Inverse Matrix

Last Update: 2004-Oct-30