Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics......click here for more.


Index P...

p valuesInterpreting p values
p-chartp- and c-Charts
paired experimentsPaired Experiments
parameterParameters
parsimonious modelModeling
partial derivativePartial Derivative
partial least squaresModeling with latent variables
 PLS - Partial Least Squares Regression
PCALiterature 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
PCRPrincipal Component Regression
 Exercise - Perform a PCR by successive application of PCA and MLR
 Modeling with latent variables
PearsonKarl Pearson
Pearson's correlation coefficientPearson's Correlation Coefficient
perceptronMulti-layer Perceptron
permutationMatrix Determinant
 Counting Rules
phase angleFourier Series
phase spacePhase Space
pink noiseTypes of Noise
platykurtic distributionKurtosis
PLSModeling with latent variables
 PLS - Partial Least Squares Regression
pocket calculatorDecimal Places and Precision
Poisson distributionPoisson Distribution
 Relationship Between Various Distributions
polynomial filterSavitzky-Golay Filter - Mathematical Details
polynomial fitExercise - Calculate a polynomial fit by means of MLR
 Data Set - Polynomial Fit
 Curve Fitting by Polynomials
populationPopulation and Sample
powerTypes of Error
 Power of a Test
precisionThe Data
 Decimal Places and Precision
 Definitions of Quality Control
 Random and Systematic Errors
prediction of future valuesRegression - Confidence Interval
 MLR - Estimation of New Observations
predictorModeling
PRESSPCA - Model Order
 PRESS
 Validation of Models
principal component regressionPrincipal Component Regression
 Exercise - Perform a PCR by successive application of PCA and MLR
 Modeling with latent variables
principal componentsLiterature 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 diagonalMatrix Algebra - Fundamentals
probabilityAlgebra 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 functionExercise - Design a data set showing a bimodal probability density function
 Exercise - Design a data set showing a normal probability density function
process controlControl Charts
 p- and c-Charts
 x- and R-Charts
process stabilityControl Charts
process variabilityVariability
processing unitANN - Single Processing Unit
pruningVariable Selection - Pruning
pseudo random numbersRandom Number Generators
pseudo-inverse matrixMoore-Penrose Pseudo-Inverse Matrix


Last Update: 2012-10-08