Index P...

p values	Interpreting p values
p-chart	p- and c-Charts
paired experiments	Paired Experiments
parameter	Parameters
parsimonious model	Modeling
partial derivative	Partial Derivative
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
	Zusammenhang zwischen Loadings, Scores und Originaldaten
PCR	Principal Component Regression
	Exercise - Perform a PCR by successive application of PCA and MLR
	Modeling with latent variables
Pearson	Karl Pearson
Pearson's correlation coefficient	Pearson's Correlation Coefficient
perceptron	Multi-layer Perceptron
permutation	Matrix Determinant
	Counting Rules
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
	Curve Fitting by Polynomials
population	Population and Sample
power	Types of Error
	Power of a Test
precision	The Data
	Decimal Places and Precision
	Definitions of Quality Control
	Random and Systematic Errors
prediction of future values	Regression - Confidence Interval
	MLR - Estimation of New Observations
predictor	Modeling
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
	Zusammenhang zwischen Loadings, Scores und Originaldaten
principal diagonal	Matrix Algebra - Fundamentals
probability	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