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 |

| Relations between Loadings, Scores and Original Data |

| PCA of Transposed Matrices |

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 |

pocket calculator | Decimal Places and Precision |

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 |

| Relations between Loadings, Scores and Original Data |

| PCA of Transposed Matrices |

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 |