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Index P...

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