Exam 20: Iscriminant, Factor and Cluster Analysis

When analyzing differences between groups 2 or more.one would use

Factor loadings and correlations are identical if each variable has its mean subtracted and is divided by its standard deviation.

Consider the following data: The clusters using the quick cluster technique.identified by the table above are

An attractive feature of varimax rotation is that it may retain the variance explained, while reducing the number of factors in the solution as compared to principal components analysis).

Discriminant analysis and ANOVA are the appropriate statistical techniques for testing the hypotheses that the group means of two or more groups are equal

In discriminant analysis, with 'm' groups and 'p' predictor variables, min p,m-1) gives the number of discriminant functions.

Factor analysis is usefully employed when it is desirable to combine several questions, thereby creating a new variable.

One rule of thumb in deciding on the number of factors to retain is to include all factors that explain at least 50 percent of the variance.

After performing a principal components analysis, a researcher finds that the cumulative variance explained by the solution is 0.56.He can increase the explained variance by performing a varimax rotation.

The ABC Company is involved in trying to segment its market so that it can better design specific marketing programs directed at each segment.One method of segmenting that it might use is cluster analysis.

In both principal components analysis and varimax rotation, the factors are constrained to be uncorrelated or geometrically perpendicular.

An attractive feature of principal components analysis is the easy interpretability of the factors.

In quick clustering, the first step is to identify the highest entry in each column of a matrix of inter-band inter-product, etc.) correlations. 38.Consider the following data: The following is an accurate graphical representation of the data shown above:

The amount of variance a variable shares with other variables is called

While analyzing and interpreting consumer perception data using factor analysis, a researcher found the factor loading on Factor 1 to be high.However, he could not interpret the factor meaningfully. A probable cause for this situation is computation error or shortsightedness in his interpretation, since a high loading ensures meaningfulness.

Consider the following: Which of the following statements is true?

It is always advisable to have as many factors as the number of input variables.

A plot of eigenvalues against the number of factors is called

Regression and Discriminant analyses are conceptually similar

All factor analysis methods constrain the factors to be uncorrelated.