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Brooklyn College, CUNYSTAT MISC TRUE/FALSE QUESTIONS CHAPTER 10 1)The main analysis obtained from a discriminant analysis is the summary of the discriminant functions

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Brooklyn College, CUNYSTAT MISC

TRUE/FALSE QUESTIONS

CHAPTER 10

1)The main analysis obtained from a discriminant analysis is the summary of the discriminant functions. [p.281] -

 

2.            A canonical correlation is a value that is equivalent to the correlation between the discriminant scores and the levels of the independent variables. [dependent variable - p.281] -

 

3.            In discriminant analysis, the IVs serve as the grouping variables and the DVs are the predictors. [opposite - p.280]

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4.            The hit rate refers to the number of correct classifications. [p.283] -

 

5.            If prediction is the goal of analysis, the researcher might use discriminant function scores in order to describe group differences. [to predict from which group participants came - p.280] -

 

6.            The correlated linear combinations of original variables identified in descriptive discriminant analysis are called discriminant functions. [uncorrelated - p.281] -

 

7.            In sequential, or hierarchical, discriminant analysis, the predictors are entered into the analysis in an order specified by the researcher. [p284] -

 

8.            Discriminant analysis seeks to identify a combination of IVs, measured at the nominal level, which best predicts membership in a particular group, as measured by an interval DV. [interval, categorical - p.279} -

 

9.            Standardized coefficients are used to assess the relative contributions of individual variables to the discriminant function(s). [p.281] -

 

10.          A high value for this correlation indicates a function that does not discriminate well between participants. [does discriminate - p.281] -

 

11.          If the goal of analysis is prediction, the researcher would determine the number of dimensions that maximize the differences among the groups in question. [to describe group differences - p.280] -

 

12.          Multivariate normality in discriminant analysis implies that the sampling distributions of the linear combinations of dependent variables are normally distributed. [predictor variables - p.285] -

 

13.          A discriminant score is analogous to a factor score. -

 

14.          Descriptive discriminant analysis refers to the process of classifying participants into groups based on a combination of measures. [prediction or classification - p.280] -

 

15.          Standardized coefficients are the basis for the calculation of discriminant scores in discriminant analysis. [unstandardized - p.281] -

 

16.          In standard, or direct, discriminant analysis, each predictor is entered into the equation simultaneously and is assigned only its unique association with the groups, as defined by the DV. [p.284] -

 

17.          An important consideration when assessing a classification procedure is the cost of classification. [cost of misclassification - p.284] -

 

18.          Discriminant analysis is often seen as the reverse of MANOVA. [p.280] -

 

19.          Misclassification is best explained through the statistical analysis rather than being assessed and explicated by the knowledge and intuition of the researcher. [the opposite - p.284] -

 

20.          Discriminant analysis is not sensitive to sample size. [might be sensitive - p.285] -

 

21.          There exists no test of the normality of all linear combinations of sampling distributions of means of predictor variables in discriminant analysis. [p.285] -

 

22.          If the discriminant analysis is being used for purposes of prediction or classification, the goal of the interpretation of discriminant functions is to determine dimensions that serve as the basis for reliably classifying participants into groups. [p.280] -

 

23.          The assumptions for discriminant analysis are basically the same as those for MANOVA, and there is no need for adjustments when classification is the goal of analysis. [of course there are some adjustments - p.284] -

 

24.          Assessing the accuracy of the hit rates cannot be accomplished through the use of the jackknife procedure. [p.283] -

 

25.          Checking for univariate normality is a good starting point for assessing possible violations of homoscedasticity. [p.285] -