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Which statistical technique should i use? a survey and marketing case study
Author(s) -
Doutriaux J.,
Crener Maxime A.
Publication year - 1982
Publication title -
managerial and decision economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.288
H-Index - 51
eISSN - 1099-1468
pISSN - 0143-6570
DOI - 10.1002/mde.4090030209
Subject(s) - canonical correlation , principal component analysis , linear discriminant analysis , computer science , variance (accounting) , statistical analysis , correspondence analysis , regression analysis , econometrics , statistics , data mining , mathematics , artificial intelligence , machine learning , economics , accounting
Mini and microcomputers are putting an even larger number of powerful statistical analysis techniques at the disposal of the researcher. This paper uses a case study approach to show how to use the original objectives and purpose of a research project to design the study, organize the data collection and select the most appropriate multi‐variate technique. Multiple regression, correlation analysis, principal component and factor analysis, discriminant analysis, causal link analysis, cluster analysis, analysis of variance including automatic interaction detection, and canonical correlation analysis are among the models and statistical techniques described in terms of their requirements, underlying hypotheses, limits, advantages. The use of these techniques is illustrated by the study of the “enjoyment” of the tourists who visited the province of Quebec in 1975.