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 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

Last Update: 2012-10-08