Discriminant Analysis in Statistics MCQs
Discriminant Analysis in Statistics MCQs
Welcome to MCQss.com, your trusted source for MCQs on discriminant analysis. This page offers a collection of interactive MCQs to help you assess your understanding and proficiency in discriminant analysis, including its concepts, assumptions, and applications in statistical research.
Discriminant analysis is a statistical technique used to determine the classification accuracy of objects or individuals into predefined groups based on a set of predictor variables. It helps researchers identify the most discriminative variables that distinguish between the groups of interest.
Our MCQs cover various aspects of discriminant analysis, including the fundamental concepts, assumptions, different types of discriminant analysis techniques, interpretation of discriminant functions, and practical considerations when applying discriminant analysis in research studies.
By answering these MCQs, you can enhance your knowledge of discriminant analysis, improve your understanding of its applications in different fields such as psychology, finance, and marketing, and gain confidence in using discriminant analysis as a powerful statistical tool.
Start exploring the MCQs now and test your proficiency in discriminant analysis!
1: In analyses such as regression and discriminant analysis, raw scores or z scores on variables are summed using a set of optimal weights (regression coefficients in the case of multiple regression and discriminant function coefficients in the case of discriminant analysis) is known as:
A. Discriminant Function
B. Classification Errors
C. Optimal Weighted Linear Composite
D. Territorial Map
2: A weighted linear combination of scores on discriminating variables; the weights are calculated so that the discriminant function has the maximum possible between-groups variance and the minimum possible within-groups variance is called _____________ .
A. Discriminant Function
B. Classification Errors
C. Optimal Weighted Linear Composite
D. Territorial Map
3: Odds involve comparison of the number of cases for two possible outcomes.
A. True
B. False
4: When the predicted group membership for a case (on the basis of the values of discriminant function scores or a binary logistic regression) is not the same as the actual group membership for that case, this is referred to as a classification error.
A. True
B. False
5: A graphical representation of the scores of individual cases on discriminant functions (SPSS plots only scores on the first two discriminant functions, D1 and D2) is known as:
A. Territorial Map
B. Discriminant Function Coefficients
C. Wilks’ Lambda (Λ)
D. None of these
6: These are the weights given to individual predictor variables when we compute scores on discriminant functions called _____________.
A. Territorial Map
B. Discriminant Function Coefficients
C. Wilks’ Lambda (Λ)
D. None of these
7: ____________ ia an overall goodness-of-fit measure used in discriminant analysis. It is the most widely used multivariate test statistic for the null hypothesis shown in multivariate analysis of variance: H0: μ1 = μ2 = ··· = μk.
A. Territorial Map
B. Discriminant Function Coefficients
C. Wilks’ Lambda (Λ)
D. None of these
8: Dimension Reduction Analysis is a discriminant analysis that yields several discriminant functions, the data analyst may think of each discriminant analysis as a “dimension” along which groups differ.
A. True
B. False
9: Just as univariate analysis of variance assumes equality of the population variances of the dependent variable across group is known as:
A. Homogeneity of Variance
B. Covariance Matrices
C. Both
D. None of these
10: In the context of discriminant analysis, an eigenvalue is a value associated with each discriminant function is known as:
A. Canonical Correlation (rc)
B. Eigenvalue (λ)
C. Both
D. None of these
11: Canonical Correlation (rc) is the correlation between scores on a discriminant function and group membership.
A. True
B. False
12: A multivariate mean; in discriminant analysis, the centroid of a group corresponds to the mean values of the scores on D1, D2, and any other discriminant functions is known as:
A. Centroid
B. Homogeneity of Variance
C. Covariance Matrices
D. None of these
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Discriminant Analysis in Statistics MCQs
