Statistics Toolbox

Chapter 1
Reference

The Statistics Toolbox provides several categories of functions. These categories appear in the table below.

The Statistics Toolbox's Main Categories of Functions

Probability
Probability distribution functions.

Descriptive
Descriptive statistics for data samples.

Plots
Statistical plots.

SPC
Statistical Process Control.

Cluster Analysis
Grouping items with similar characteristics into clusters.

Linear
Fitting linear models to data.

Nonlinear
Fitting nonlinear regression models.

DOE
Design of Experiments.

PCA
Principal Components Analysis.

Hypotheses
Statistical tests of hypotheses.

File I/O
Reading data from and writing data to operating-system files.

Demos
Demonstrations.

Data
Data for examples.

The Statistics Toolbox's Main Categories of Functions
Probability	Probability distribution functions.
Descriptive	Descriptive statistics for data samples.
Plots	Statistical plots.
SPC	Statistical Process Control.
Cluster Analysis	Grouping items with similar characteristics into clusters.
Linear	Fitting linear models to data.
Nonlinear	Fitting nonlinear regression models.
DOE	Design of Experiments.
PCA	Principal Components Analysis.
Hypotheses	Statistical tests of hypotheses.
File I/O	Reading data from and writing data to operating-system files.
Demos	Demonstrations.
Data	Data for examples.

The following pages contain tables of functions from each of these specific areas. The first seven tables contain probability distribution functions. The remaining tables describe the other categories of functions.


	Parameter Estimation
`betafit`	Parameter estimation for the beta distribution.
`betalike`	Beta log-likelihood function.
`binofit`	Parameter estimation for the binomial distribution.
`expfit`	Parameter estimation for the exponential distribution.
`gamfit`	Parameter estimation for the gamma distribution.
`gamlike`	Gamma log-likelihood function.
`mle`	Maximum likelihood estimation.
`normlike`	Normal log-likelihood function.
`normfit`	Parameter estimation for the normal distribution.
`poissfit`	Parameter estimation for the Poisson distribution.
`unifit`	Parameter estimation for the uniform distribution.


	Cumulative Distribution Functions (cdf)
`betacdf`	Beta cdf.
`binocdf`	Binomial cdf.
`cdf`	Parameterized cdf routine.
`chi2cdf`	Chi-square cdf.
`expcdf`	Exponential cdf.
`fcdf`	F cdf.
`gamcdf`	Gamma cdf.
`geocdf`	Geometric cdf.
`hygecdf`	Hypergeometric cdf.
`logncdf`	Lognormal cdf.
`nbincdf`	Negative binomial cdf.
`ncfcdf`	Noncentral F cdf.
`nctcdf`	Noncentral t cdf.
`ncx2cdf`	Noncentral Chi-square cdf.
`normcdf`	Normal (Gaussian) cdf.
`poisscdf`	Poisson cdf.
`raylcdf`	Rayleigh cdf.
`tcdf`	Student's t cdf.
`unidcdf`	Discrete uniform cdf.
`unifcdf`	Continuous uniform cdf.
`weibcdf`	Weibull cdf.


	Probability Density Functions (pdf)
`betapdf`	Beta pdf.
`binopdf`	Binomial pdf.
`chi2pdf`	Chi-square pdf.
`exppdf`	Exponential pdf.
`fpdf`	F pdf.
`gampdf`	Gamma pdf.
`geopdf`	Geometric pdf.
`hygepdf`	Hypergeometric pdf.
`normpdf`	Normal (Gaussian) pdf.
`lognpdf`	Lognormal pdf.
`nbinpdf`	Negative binomial pdf.
`ncfpdf`	Noncentral F pdf.
`nctpdf`	Noncentral t pdf.
`ncx2pdf`	Noncentral Chi-square pdf.
`pdf`	Parameterized pdf routine.
`poisspdf`	Poisson pdf.
`raylpdf`	Rayleigh pdf.
`tpdf`	Student's t pdf.
`unidpdf`	Discrete uniform pdf.
`unifpdf`	Continuous uniform pdf.
`weibpdf`	Weibull pdf.


	Inverse Cumulative Distribution Functions
`betainv`	Beta critical values.
`binoinv`	Binomial critical values.
`chi2inv`	Chi-square critical values.
`expinv`	Exponential critical values.
`finv`	F critical values.
`gaminv`	Gamma critical values.
`geoinv`	Geometric critical values.
`hygeinv`	Hypergeometric critical values.
`logninv`	Lognormal critical values.
`nbininv`	Negative binomial critical values
`ncfinv`	Noncentral F critical values.
`nctinv`	Noncentral t critical values.
`ncx2inv`	Noncentral Chi-square critical values.
`icdf`	Parameterized inverse distribution routine.
`norminv`	Normal (Gaussian) critical values.
`poissinv`	Poisson critical values.
`raylinv`	Rayleigh critical values.
`tinv`	Student's t critical values.
`unidinv`	Discrete uniform critical values.
`unifinv`	Continuous uniform critical values.
`weibinv`	Weibull critical values.


	Random Number Generators
`betarnd`	Beta random numbers.
`binornd`	Binomial random numbers.
`chi2rnd`	Chi-square random numbers.
`exprnd`	Exponential random numbers.
`frnd`	F random numbers.
`gamrnd`	Gamma random numbers.
`geornd`	Geometric random numbers.
`hygernd`	Hypergeometric random numbers.
`lognrnd`	Lognormal random numbers.
`nbinrnd`	Negative binomial random numbers.
`ncfrnd`	Noncentral F random numbers.
`nctrnd`	Noncentral t random numbers.
`ncx2rnd`	Noncentral Chi-square random numbers.
`normrnd`	Normal (Gaussian) random numbers.
`poissrnd`	Poisson random numbers.
`raylrnd`	Rayleigh random numbers.
`random`	Parameterized random number routine.
`trnd`	Student's t random numbers.
`unidrnd`	Discrete uniform random numbers.
`unifrnd`	Continuous uniform random numbers.
`weibrnd`	Weibull random numbers.


	Moments of Distribution Functions
`betastat`	Beta mean and variance.
`binostat`	Binomial mean and variance.
`chi2stat`	Chi-square mean and variance.
`expstat`	Exponential mean and variance.
`fstat`	F mean and variance.
`gamstat`	Gamma mean and variance.
`geostat`	Geometric mean and variance.
`hygestat`	Hypergeometric mean and variance.
`lognstat`	Lognormal mean and variance.
`nbinstat`	Negative binomial mean and variance.
`ncfstat`	Noncentral F mean and variance.
`nctstat`	Noncentral t mean and variance.
`ncx2stat`	Noncentral Chi-square mean and variance.
`normstat`	Normal (Gaussian) mean and variance.
`poisstat`	Poisson mean and variance.
`raylstat`	Rayleigh mean and variance.
`tstat`	Student's t mean and variance.
`unidstat`	Discrete uniform mean and variance.
`unifstat`	Continuous uniform mean and variance.
`weibstat`	Weibull mean and variance.


	Descriptive Statistics
`corrcoef`	Correlation coefficients (in MATLAB).
`cov`	Covariance matrix (in MATLAB).
`geomean`	Geometric mean.
`harmmean`	Harmonic mean.
`iqr`	Interquartile range.
`kurtosis`	Sample kurtosis.
`mad`	Mean absolute deviation.
`mean`	Arithmetic average (in MATLAB).
`median`	50th percentile (in MATLAB).
`moment`	Central moments of all orders.
`nanmax`	Maximum ignoring missing data.
`nanmean`	Average ignoring missing data.
`nanmedian`	Median ignoring missing data.
`nanmin`	Minimum ignoring missing data.
`nanstd`	Standard deviation ignoring missing data.
`nansum`	Sum ignoring missing data.
`prctile`	Empirical percentiles of a sample.
`range`	Sample range.
`skewness`	Sample skewness.
`std`	Standard deviation (in MATLAB).
`trimmean`	Trimmed mean.
`var`	Variance.


	Statistical Plotting
`boxplot`	Box plots.
`errorbar`	Error bar plot.
`fsurfht`	Interactive contour plot of a function.
`gline`	Interactive line drawing.
`gname`	Interactive point labeling.
`lsline`	Add least-squares fit line to plotted data.
`normplot`	Normal probability plots.
`pareto`	Pareto charts.
`qqplot`	Quantile-Quantile plots.
`rcoplot`	Regression case order plot.
`refcurve`	Reference polynomial.
`refline`	Reference line.
`surfht`	Interactive interpolating contour plot.
`weibplot`	Weibull plotting.


	Statistical Process Control
`capable`	Quality capability indices.
`capaplot`	Plot of process capability.
`ewmaplot`	Exponentially weighted moving average plot.
`histfit`	Histogram and normal density curve.
`normspec`	Plot normal density between limits.
`schart`	Time plot of standard deviation.
`xbarplot`	Time plot of means.


	Cluster Analysis
`cluster`	Create clusters from `linkage` output.
`clusterdata`	Create clusters from a dataset.
`cophenet`	Calculate the cophenetic correlation coefficient.
`dendrogram`	Plot a hierarchical tree in a dendrogram graph.
`inconsistent`	Calculate the inconsistency values of objects in a cluster hierarchy tree.
`linkage`	Link objects in a dataset into a hierarchical tree of binary clusters.
`pdist`	Calculate the pairwise distance between objects in a dataset.
`squareform`	Reformat output of `pdist` function from vector to square matrix.
`zscore`	Normalize a dataset before calculating the distance.


	Linear Models
`anova1`	One-way Analysis of Variance (ANOVA).
`anova2`	Two-way Analysis of Variance.
`lscov`	Regression given a covariance matrix (in MATLAB).
`polyconf`	Polynomial prediction with confidence intervals.
`polyfit`	Polynomial fitting (in MATLAB).
`polyval`	Polynomial prediction (in MATLAB).
`regress`	Multiple linear regression.
`ridge`	Ridge regression.
`rstool`	Response surface tool.
`stepwise`	Stepwise regression GUI.


	Nonlinear Regression
`nlinfit`	Nonlinear least-squares fitting.
`nlintool`	Prediction graph for nonlinear fits.
`nlparci`	Confidence intervals on parameters.
`nlpredci`	Confidence intervals for prediction.
`nnls`	Nonnegative least squares (in MATLAB).


	Design of Experiments
`cordexch`	D-optimal design using coordinate exchange.
`daugment`	D-optimal augmentation of designs.
`dcovary`	D-optimal design with fixed covariates.
`ff2n`	Two-level full factorial designs.
`fullfact`	Mixed level full factorial designs.
`hadamard`	Hadamard designs (in MATLAB).
`rowexch`	D-optimal design using row exchange.


	Principal Components Analysis
`barttest`	Bartlett's test.
`pcacov`	PCA from covariance matrix.
`pcares`	Residuals from PCA.
`princomp`	PCA from raw data matrix.


	Hypothesis Tests
`ranksum`	Wilcoxon rank sum test.
`signrank`	Wilcoxon signed rank test.
`signtest`	Sign test for paired samples.
`ttest`	One sample t-test.
`ttest2`	Two sample t-test.
`ztest`	Z-test.


	File I/O
`caseread`	Read casenames from a file.
`casewrite`	Write casenames from a string matrix to a file.
`tblread`	Retrieve tabular data from the file system.
`tblwrite`	Write data in tabular form to the file system.


	Demonstrations
`disttool`	Interactive exploration of distribution functions.
`randtool`	Interactive random number generation.
`polytool`	Interactive fitting of polynomial models.
`rsmdemo`	Interactive process experimentation and analysis.
`statdemo`	Demonstrates capabilities of the Statistics Toolbox.


	Data
`census.mat`	U. S. Population 1790 to 1980.
`cities.mat`	Names of U.S. metropolitan areas.
`discrim.mat`	Classification data.
`gas.mat`	Gasoline prices.
`hald.mat`	Hald data.
`hogg.mat`	Bacteria counts from milk shipments.
`lawdata.mat`	GPA versus LSAT for 15 law schools.
`mileage.mat`	Mileage data for three car models from two factories.
`moore.mat`	Five factor - one response regression data.
`parts.mat`	Dimensional runout on 36 circular parts.
`popcorn.mat`	Data for popcorn example.
`polydata.mat`	Data for polytool demo.
`reaction.mat`	Reaction kinetics data.
`sat.dat`	ASCII data for `tblread` example.

[ Previous | Help Desk | Next ]

Chapter 1Reference

Chapter 1
Reference