Contents -- 1. S-language -- In the beginning -- Three data types�and some input conventions -- Reading values into SPLUS -- S-tools�a beginning set -- S-arithmetic -- More S-tools�intermediate set -- S-tools for statistics -- Statistical distributions in SPLUS -- Arrays and tables -- Matrix algebra tools -- Some additional S-tools -- Four S-code examples -- The .Data file -- Addendum: Built-in editors -- Problem set I -- 2. Descriptive Techniques -- Description of descriptive statistics -- Basic statistical measures
Histogram smoothing�density estimationStem-and-leaf display -- Comparison of groups�t-test -- Comparison of groups�boxplots -- Comparison of data to a theoretical distribution�quantile plots -- Comparison of groups�qqplots -- xy-plot -- Three-dimensional plots�perspective plots -- Three-dimensional plots�contour plots -- Three-dimensional plots�rotation -- Smoothing -- Two-dimensional smoothing of spatial data -- Clusters as a description of data -- Additivity�sweeping an array -- Example�geographic calculations using S-functions
Estimation of the center of a two-dimensional distributionAddendum: S-geometry -- Problem set II -- 3. Simulation: Random Values -- Random uniform values -- An example -- Sampling without and with replacement -- Random sample from a discrete probability distribution�acceptance/rejection sampling -- Random sample from a discrete probability distribution�inverse transform method -- Binomial probability distribution -- Hypergeometric probability distribution -- Poisson probability distribution -- Geometric probability distribution
Random samples from a continuous distributionInverse transform method -- Simulating values from the normal distribution -- Four other statistical distributions -- Simulating minimum and maximum values -- Butler's method -- Random values over a complex region -- Multivariate normal variables -- Problem set III -- 4. General Linear Models -- Simplest case�univariate linear regression -- Multivariable case -- Multivariable linear model -- A closer look at residual values -- Predict�pointwise confidence intervals -- Formulas for glm()
Polynomial regressionDiscriminant analysis -- Linear logistic model -- Categorical data�bivariate linear logistic model -- Multivariable data�linear logistic model -- Goodness-of-fit -- Poisson model -- Multivariable Poisson model -- Problem set IV -- 5. Estimation -- Estimation: Maximum Likelihood -- Estimator properties -- Maximum likelihood estimator -- Scoring to find maximum likelihood estimates -- Multiparameter estimation -- Generalized scoring -- Estimation: Bootstrap -- Background -- General outline
