{"id":14161,"date":"2018-04-25T08:58:36","date_gmt":"2018-04-25T08:58:36","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=14161"},"modified":"2021-12-03T10:35:25","modified_gmt":"2021-12-03T05:05:25","slug":"sas-market-research-procedure","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/sas-market-research-procedure\/","title":{"rendered":"6 Important SAS Market Research Procedure"},"content":{"rendered":"

We looked at different types of methods for SAS\/STAT analysis in our previous tutorials. In this SAS\/STAT tutorial<\/a><\/strong>, we are going to study SAS market research and procedures used to perform Marketing Research in SAS\/STAT.<\/p>\n

Our focus here will be to understand different SAS Market Research procedures:\u00a0PROC CORRESP, PROC MDS, PROC PRINQUAL, PROC TRANSREG, PROC BCHOICE, and PROC PHREG with examples and syntax.
\nSo, let’s start with SAS Market Research.<\/p>\n

\"SAS<\/a>

SAS Market Research<\/p><\/div>\n

SAS Market Research<\/span><\/h3>\n

Market research focuses on assessing the preferences and choices of consumers and potential consumers.
\nThe SAS System offers the techniques common to the market research arena, including simple descriptive analysis and graphing, database marketing (or database mining), geographical information systems (GIS).<\/p>\n

Everything from simple summary analysis to advanced statistical and graphical techniques is available. Users holding different levels of expertise in both software and market research methodologies, getting benefit from these tools.
\nLet’s Discuss 4 Important Longitudinal Data Analysis Procedures<\/a><\/strong><\/p>\n

Procedures for Market Research in SAS\/STAT<\/h3>\n

Following procedures performed in SAS market research of a sample data. Each procedure has a different syntax and is used with different type of data in different contexts. Let us explore each one of these.<\/p>\n

a. PROC CORRESP<\/h4>\n

CORRESP stands for correspondence<\/strong>. The PROC CORRESP procedure performs correspondence analysis as well as multiple correspondence analysis. Correspondence analysis is used for graphical representation of the rows and columns of a contingency table or a crosstabulation table.
\nA Syntax of PROC CORRESP-<\/strong><\/p>\n

PROC CORRESP DATASET <OPTIONS>;\r\nTABLES\u00a0<row-variables,> column-variables;\r\nVAR variables;\r\nID variable;<\/pre>\n
There are two separate forms of input to PROC CORRESP. One form is specified in the TABLES statement, the other in the VAR statement. Each time you run PROC CORRESP, you must specify either the TABLES or the VAR statement, but not both.
\nSpecify the TABLES statement if you are using raw, categorical data, the levels of which define the rows and columns of a table.
\nSpecify the VAR statement if your data is already in tabular form.\u00a0<\/strong>
\nPROC CORRESP Example-<\/strong><\/p>\n
title \"Population of Different cities from 1960 to 1965\";\r\ndata Population;\r\n\u00a0\u00a0 input cities $ 1-19 y1960-y1965;\r\n\u00a0\u00a0 label y1960 = '1960'\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 y1961 = '1961'\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 y1962 = '1962'\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 y1963 = '1963'\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 y1964 = '1964'\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 y1965 = '1965';\r\n\u00a0\u00a0 datalines;\r\nBangalore\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0  \u00a0\u00a0\u00a04489 4303 4402 4350 4266 4361\r\nHyderabad\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0    4101 3800 3749 3572 3410 3234\r\nPune\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a03354 3286 3344 3278 3137 3008\r\nMumbai\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0 2444 2587 2749 2878 2960 3049\r\nChennai\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a03338 3144 2959 2791 2641 2432\r\nDelhi\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a01222 1196 1149 1003\u00a0 959\u00a0 959\r\n;\r\nods graphics on;\r\nproc corresp data=Population;\r\n\u00a0\u00a0 var y1960-y1965;\r\n\u00a0\u00a0 id cities;\r\nrun;<\/pre>\n
Read About 6 Bayesian Analysis Procedures\u00a0<\/a><\/strong><\/p>\n
\"SAS\/STAT<\/a>
SAS Market Research –\u00a0PROC CORRESP<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS Market Research –\u00a0PROC CORRESP<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS Market Research –\u00a0PROC CORRESP<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS Market Research –\u00a0PROC CORRESP<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS Market Research –\u00a0PROC CORRESP<\/p><\/div>\n
Read About Mixed Models Procedures \u2013 PROC MIXED, PROC NLMIXED<\/a><\/strong><\/p>\n
b. PROC MDS<\/h4>\n
MDS stands for multidimensional scaling<\/strong>. It is a class of methods that help us find out the coordinates of a set of objects that are in space and have a specified dimensionality. The data for the MDS procedure consists of one or more square symmetric or asymmetric matrices of similarities or dissimilarities between\u00a0objects.<\/em><\/p>\n
The MDS procedure also fits two and three-way, metric and nonmetric multidimensional scaling models.
\nA Syntax of PROC MDS-<\/strong><\/p>\n
PROC MDS dataset <OPTIONS>;\r\nVAR variables;\r\nID variable<\/pre>\n
The PROC MDS statement is required. All other statements are optional.
\nPROC MDS Example-<\/strong><\/p>\n
ods graphics on;\r\nproc mds data=sashelp.mileages level=log;\r\n\u00a0 id city;\r\nrun;<\/pre>\n
\"SAS\/STAT<\/a>
Market Research in SAS\/STAT –\u00a0PROC MDS<\/p><\/div>\n
\"SAS\/STAT<\/a>
Market Research in SAS\/STAT –\u00a0PROC MDS<\/p><\/div>\n
\"SAS\/STAT<\/a>
Market Research in SAS\/STAT –\u00a0PROC MDS<\/p><\/div>\n
Let’s read about Categorical Data Analysis Procedure <\/a><\/strong><\/p>\n
c. PROC PRINQUAL<\/h4>\n
PROC PRINQUAL uses the method of alternating least squares, to find linear and nonlinear transformations of variables. The PRINQUAL procedure performs principal component analysis (PCA) of a sample data. PROC PRINQUAL provides three methods, each of which provides a different property. These methods are:<\/p>\n