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Canonical correlation analysis (CC) allows the investigation of the relationship between two ,sets of variables. For example, a sociologist may want to investigate the Relationship between two predictors of social mobility based on interviews, with actual subsequent social nlobility as measured by four different indicators. A medical researcher may want to study the relationship of various risk factors to the development of a group of symptoms. In all of these cases, the researcher is interested in the relationship betweet; ' two, sets of variables, and Canonical Correlation would be the appropriate method of analysis. . Canonical Correlation combines the dependent variables to find pairs, of new variables called canonical variables, CV, 'one for each data table having the highest . correlation. However, the CV's, even when highly correlated, do not necessarily explain a large portion of the variance of the original tables. This makes the interpretation of the CV sometimes difficult, but CC is nonetheless an important theoretical tool because most multivariate techniques can be interpreted as a special case of CC.
it is said that management is equivalent to decision making? do you agree? explain
Among the students doing a given course, there are four boys enrolled in the ordinary version of the course, six girls enrolled in the ordinary version of the course,and six boys e
difference between large sample test and small sample test
slope parameter of 1.4 and scale parameter of 550.calculate Reliability, MTTF, Variance, Design life for R of 95%
Characteristics of Index Number On the analysis of various definitions of index number the following may be its characteristics: 1. Expressed in Number : Index number
Correspondence Analysis (CA) is a generalization of PCA to contingency tables. The factors of correspondence analysis give an orthogonal decomposi:ion of the Chi- square associated
Sample Standard Deviation So far, we discussed the population standard deviation. Now, let us switch to sample standard deviation(s) that is analogous to the population stand
Other Measures of Dispersion In this section, we look at relatively less used measures of dispersion like fractiles, deciles, percentiles, quartiles, interquartile range and f
Type of Variable in Regression Analysis There are two types of variable in regression analysis. These are: a. Dependent variable b. Independent variable
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