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1

is used when you have a dichotomous criterion variable and a set of predictor variables.
2

If outliers in your data cause a negative skew, the most appropriate transformation strategy is to use a .
3

If an examination of a scatterplot of your data shows a conical pattern, then is present.
4

The most popular rotation strategy in factor analysis is rotation.
5

You would use if your goal is to reduce a large number of variables down to a smaller set and to obtain an empirical summary of the data.
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is a correlational technique that allows you to evaluate the relationship between two variables with the effects of a third removed from both of them.
7

The general linear equation formula for multiple regression is .
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In the absence of any theoretical ordering of variables, analysis is the multiple regression strategy of choice.
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is the correlation between predicted values of Y and the observed values of Y.
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is the proportion of variability in the dependent variable that is accounted for by a set of predictors in multiple regression.
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The regression weights are usually used to interpret a significant regression analysis because they express differently scaled variables in the same terms.
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Although a regression weight is often used as an index of the degree of contribution of a predictor variable, a better index is the .
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The number of possible discriminant functions in a discriminant analysis is limited to or to , whichever is less.
14

To determine the relationship between two sets of variables, you would use .
15

The produced in an SPSS MANOVA output are similar to factor loadings and can be used to evaluate the degree of contribution of a variable.
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A path model that has causal relationships that run in only one direction and have no causal loops is called a(n) model.
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are used to estimate the degree of causal relationship between variables in a path model.
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are followed when decomposing a path model into direct and indirect effects.
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is a variant of path analysis used for causal modeling when you have variables that are not directly observable.
20

According to your text, you should when considering using multivariate statistics.





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