For example wool and woolens have a market during

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For example, wool and woolens have a market during winter seasonally only, fans, coolers and cold drinks are in great demand in summer. Whereas rains coats and umbrellas get a market during rainy season mainly. Hence a manufacture or a stockiest has to manage production, financer and personnel in such a way that he can market his product and meet the demand properly and in time. A businessman should have a clear picture of seasonal variations occurring from year to year during the same period. 20.12. KEY WORDS 1) Short term fluctuation, 2) Simple average, 3) Seasonal indices, 4) Cyclical variation, 5) Irregular variation.
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215 LESSON 21 DATA REDUCTION TECHNIQUE [FACTOR ANALYSIS AND CLUSTER ANALYSIS] 21.1. INTRODUCTION Multivariate analysis can be easily defined as the application of methods that deal with reasonably large numbers of measurements made on each object in one or more samples simultaneously. Multivariate analysis deals with the simultaneous relationships among variables. Depending on the nature and the number of variable the researcher wishes to study, there are several multivariate technique that can analyze dependence structures. Factor analysis and cluster analysis are mainly used for data reduction techniques. The term data crunching generally refers to a process in which a considerable quantity of data is reduced down to a more manageable or consolidated whole. Principal components analysis and factor analysis are the quintessential data- crunching procedures. Their general purpose is to identify a relatively small number of themes, dimensions, components, or factors underlying a relatively large set of variables. The way they do this is by distinguishing sets of variables that have more in common with each other than with the other variables in the analysis. Cluster analysis is a data exploration (mining) tool for dividing a multivariate data set into “nature” cluster (groups). We use the methods of explore, whether, previously un defined clusters (groups) may exist in the data set. For instance a marketing department may wish to use survey results to sort is customers into categories. The basic aim of cluster analysis is to find the natural groupings, if any of a set of individuals (or objects, or points, or unit or whatever). This set of individuals may from a complete population or be a sample larger individuals may be from a complete population or be a sample from some large population. 21.2. OBJECTIVES Understand the terminology of factor analysis, including the interpretation of factor loadings, specific variances, and communalities. Understand how to apply maximum likelihood methods for estimating the parameters of factor model. Understand factor rotation, and interpret rotated factor loadings.
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