The SAS/STAT market research procedures include the following:
 BCHOICE Procedure — Bayesian Discrete Choice Models
 CORRESP Procedure — Performs simple correspondence analysis and multiple correspondence analysis (MCA)
 MDS Procedure — Fits two and threeway, metric and nonmetric multidimensional scaling models
 PHREG Procedure — fit a multinomial logit choice model to discrete choice data
 PRINQUAL Procedure — Performs principal component analysis (PCA) of qualitative, quantitative, or mixed data
 TRANSREG — Fits linear models with optimal nonlinear transformations of variables
BCHOICE Procedure
The BCHOICE procedure fits Bayesian discrete choice models by using MCMC methods.
The procedure's capabilities include the following:
 fits the following types of models:
 multinomial logit
 multinomial probit
 nested logit
 multinomial logit with random effects
 multinomial probit with random effects
 samples directly from the full conditional distribution when possible
 supports the following sampling algorithms:
 MetropolisHastings approach of Gamerman
 random walk Metropolis
 latent variables via the data augmentation method
 provides a variety of Markov chain convergence diagnostics

 works with the postprocessing autocall macros that are designed for Bayesian posterior samples
 supports a CLASS statement for specifying classification variables
 supports a RESTRICT statement, enabling you to specify boundary requirements and order constraints
on fixed effects for logit models
 multithreaded
 creates an output data set that contains the posterior samples of all parameters
 creates an output data set that contains random samples from the posterior predictive distribution of the choice probabilities
 creates an output data set that corresponds to any output table
 supports BY group processing
 automatically produces graphs by using ODS Graphics

For further details, see
BCHOICE Procedure
CORRESP Procedure
The CORRESP procedure performs simple correspondence analysis and multiple correspondence analysis (MCA).
You can use correspondence analysis to find a lowdimensional graphical representation of the rows and columns
of a crosstabulation or contingency table. Each row and column is represented by a point in a plot determined
from the cell frequencies. PROC CORRESP can also compute coordinates for supplementary rows and columns.
The procedure enables you to do the following:
 use two kinds of input: raw categorical responses on two or more classification variables or a twoway contingency table
 specify the number of dimensions or axes
 specify the standardization for the row and column coordinates
 create a data set that contains coordinates and the results of the correspondence analysis

 create a data set that contains frequencies and percentages
 create a data set that corresponds to any output table
 perform BY group processing, which enebales you to obtain separate analyses on grouped observations
 automatically display the correspondence analysis plot by using ODS Graphics

For further details, see
CORRESP Procedure
MDS Procedure
The MDS procedure fits two and threeway, metric and nonmetric multidimensional scaling models.
Multidimensional scaling refers to a class of methods. These methods estimate coordinates for a set of objects in a space of
specified dimensionality. The input data are measurements of distances between pairs of objects. A variety of models can be used that
include different ways of computing distances and various functions relating the distances to the actual data.
The following are highlights of the MDS procedure's features:
 estimates the following parameters by nonlinear least squares:
 configuration — the coordinates of each object in a Euclidean or weighted
Euclidean space of one or more dimensions
 dimension coefficients — for each data matrix, the coefficients that multiply each coordinate
of the common or group weighted Euclidean space to
yield the individual unweighted Euclidean space
 transformation parameters — intercept, slope, or exponent in a linear, affine, or power transformation
relating the distances to the data
 fits either a regression model of the form
fit(datum) = fit(trans(distance)) + error
or a measurement model of the form
fit(trans(datum)) = fit(distance) + error
where
 fit is a predetermined power or logarithmic transformation
 trans is an estimated (`optimal') linear, affine, power, or monotone transformation
 datum is a measure of the similarity or dissimilarity of two objects or stimuli
 distance is a distance computed from the estimated coordinates of the two objects and estimated
dimension coefficients in a space of one or more dimensions
 error is an error term assumed to have an approximately normal distribution and to be
independently and identically distributed for all data
 performs BY group processing, whcih enables you to obtain separate analyses on grouped observations
 performs weighted analysis
creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details, see
MDS Procedure
PHREG Procedure
The PHREG procedure performs regression analysis of survival data based on the Cox proportional hazards model.
Cox's semiparametric model is widely used in the analysis of survival data to explain the effect of explanatory variables on hazard rates.
The following are highlights of the PHREG procedure's features:
 fits a superset of the Cox model, known as the multiplicative hazards model or the AndersonGill model
 fits frailty models
 fits competing risk model of Fine and Gray
 performs stratified analysis
 includes four methods for handling ties in the failure times
 provides four methods of variable selection
 permits an offset in the model
 performs weighted estimation
 enables you to use SAS programming statements within the procedure to modify values of the explanatory variables or to create ne explanatory variables
 tests linear hypotheses about the regression parameters
 estimates customized hazard ratios
 performs graphical and numerical assessment of the adequacy of the Cox regression model

 creates a new SAS data set that contains the baseline function estimates at the event times of each stratum for every specified set of covariates
 outputs survivor function estimates, residuals, and regression diagnostics
 performs conditional logistic regression analysis for matched casecontrol studies
 fits multinomial logit choice models for discrete choice data
 performs samplingbased Bayesian analysis
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 creates an output data set that contains parameter and covariance estimates
 creates an output data set that contains userspecified statistics
 creates a SAS data set that corresponds to any output table
 automatically created graphs by using ODS Graphics

For further details, see
PHREG Procedure
PRINQUAL Procedure
The PRINQUAL procedure performs principal component analysis (PCA) of qualitative, quantitative, or mixed data.
PROC PRINQUAL enables you to do the following:
 find linear and nonlinear transformations of variables, using the method of alternating least squares,
that optimize properties of the transformed variables' correlation
or covariance matrix. Nonoptimal transformations such as logarithm and rank are also available.
 fit metric and nonmetric principal component analyses
 perform metric and nonmetric multidimensional preference (MDPREF) analyses
 reduce the number of variables for subsequent use in regression analyses, cluster analyses, and other analyses
 choose between three methods, each of which seeks to optimize a different property of the transformed variables' covariance
or correlation matrix. These methods are as follows:
 maximum total variance, or MTV
 minimum generalized variance, or MGV
 maximum average correlation, or MAC
 transform ordinal variables monotonically by scoring the ordered categories so that order is weakly preserved (adjacent categories can be merged) and the covariance
matrix is optimized. You can undo ties optimally or leave them tied. You can also transform ordinal variables to ranks.

 transform nominal variables by optimally scoring the categories
 transform interval and ratio scale of measurement variables linearly, or transform them nonlinearly with spline transformations or monotone spline transformations.
In addition, nonoptimal transformations for logarithm, rank, exponential, power, logit, and inverse trigonometric sine are available.
 estimate missing data without constraint, with category constraints (missing values within the same group get the same value), and with order constraints (missing
value estimates in adjacent groups can be tied to preserve a specified ordering).
 detect nonlinear relationships
 perform weighted estimation
 perform BY group processing, which enables you to obtain separate analyses on grouped observations
 create a SAS data set that contains the original variables, transformed variables, components, or data approximations
 create a SAS data set that corresponds to any output table
 automatically create graphs by using ODS Graphics

For further details, see
PRINQUAL Procedure
TRANSREG Procedure
The TRANSREG (transformation regression) procedure fits linear models, optionally with smooth, spline, BoxCox, and other nonlinear
transformations of the variables. The following are highlights of the TRANSREG procedure's features:
 enables you to fit linear models including:
 ordinary regression and ANOVA
 metric and nonmetric conjoint analysis (Green and Wind 1975; de Leeuw, Young, and Takane 1976)
 linear models with BoxCox (1964) transformations of the dependent variables
 regression with a smooth (Reinsch 1967), spline (de Boor 1978; van Rijckevorsel 1982),
monotone spline (Winsberg and Ramsay 1980), or penalized Bspline (Eilers and Marx 1996)
fit function
 metric and nonmetric vector and ideal point preference mapping (Carroll 1972)
 simple, multiple, and multivariate regression with variable transformations (Young,
de Leeuw, and Takane 1976; Winsberg and Ramsay 1980; Breiman and Friedman 1985)
 redundancy analysis (Stewart and Love 1968) with variable transformations (Israels 1984)
 canonical correlation analysis with variable transformations (van der Burg and de Leeuw 1983)
 response surface regression (Meyers 1976; Khuri and Cornell 1987) with variable transformations
 enables you to use a data set that can contain variables measured on nominal, ordinal, interval, and ratio scales;
you can specify any mix of these variable types for the dependent and independent variables
 transform nominal variables by scoring the categories to minimize squared error
(Fisher 1938), or treat nominal variables as classification variables

 enables you to transform ordinal variables by monotonically scoring the ordered categories so that order is
weakly preserved (adjacent categories can be merged) and squared error is minimized. Ties
can be optimally untied or left tied (Kruskal 1964). Ordinal variables can also be transformed
to ranks.
 enables you to transform interval and ratio scale of measurement variables linearly or nonlinearly with spline
(de Boor 1978; van Rijckevorsel 1982), monotone spline (Winsberg and Ramsay 1980),
penalized Bspline (Eilers and Marx 1996), smooth (Reinsch 1967), or BoxCox (Box and
Cox 1964) transformations. In addition, logarithmic, exponential, power, logit, and inverse
trigonometric sine transformations are available.
 fits a curve through a scatter plot or fit multiple curves, one for each level of a classification variable
 enables you to constrain the functions to be parallel or monotone or have the same intercept
 enables you to code experimental designs and classification variables prior to their use in other analyses
 perform sweighted estimation
 generates output data sets including
 ANOVA results
 regression tables
 conjoint analysis partworth utilities
 coefficients
 marginal means
 original and transformed variables, predicted values, residuals, scores, and more
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 automatically creates graphs by using ODS Graphics

For further details, see
TRANSREG Procedure