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Multivariate analysis - Exam
Contributed by: Skelton
  • 1. Multivariate analysis is a statistical technique used to analyze data sets that contain observations on multiple variables. It allows researchers to understand the relationships between these variables and uncover patterns or trends that may not be apparent when analyzing each variable individually. By examining multiple variables simultaneously, multivariate analysis provides a more comprehensive and holistic understanding of the data, enabling researchers to make more informed decisions and draw reliable conclusions. Common methods of multivariate analysis include principal component analysis, factor analysis, cluster analysis, and multivariate regression. These techniques are widely used across various fields such as economics, psychology, biology, and marketing to explore complex relationships and extract meaningful insights from data.

    What is multivariate analysis?
A) Analysis of continuous variables only
B) Analysis of two variables
C) Analysis of multiple variables simultaneously
D) Analysis of a single variable
  • 2. Which statistical technique is commonly used in multivariate analysis?
A) ANOVA
B) T-test
C) Principal component analysis
D) Chi-square test
  • 3. Which analysis is used in multivariate analysis to group variables based on similarities?
A) Regression analysis
B) Cluster analysis
C) ANOVA
D) Correlation analysis
  • 4. What is the aim of discriminant analysis in multivariate analysis?
A) To determine descriptive statistics
B) To determine correlation coefficients
C) To determine outliers
D) To determine which variables discriminate between two or more group
  • 5. What is a scree plot used for in multivariate analysis?
A) To plot data points
B) To show correlation coefficients
C) To identify outliers
D) To determine the number of factors to retain in factor analysis
  • 6. What does cluster analysis in multivariate analysis aim to do?
A) Plotting bivariate data
B) Testing for differences between groups
C) Conducting factor analysis
D) Grouping similar observations into clusters
  • 7. What does discriminant analysis allow researchers to do?
A) Determine which variables best predict group membership
B) Test for correlations
C) Identify outliers in the data
D) Conduct factor analysis
  • 8. When should covariance matrix be used in multivariate analysis?
A) To determine sample size
B) To test for outliers
C) To understand the relationships and variances between multiple variables
D) To perform factor analysis
  • 9. What is discriminant function analysis used for in multivariate analysis?
A) To determine correlations
B) To perform cluster analysis
C) To find outliers
D) To predict group membership based on predictor variables
  • 10. What is the purpose of canonical correlation analysis?
A) To determine outliers
B) To determine factor loadings
C) To determine the relationship between two sets of variables
D) To perform hypothesis testing
  • 11. What is canonical correlation analysis used for in multivariate analysis?
A) To test hypotheses
B) To find correlation between a variable and itself
C) To examine the relationships between two sets of variables
D) To perform regression analysis
  • 12. When can principal component analysis be appropriate to use in multivariate analysis?
A) When variables are highly correlated
B) When dealing with categorical data only
C) When variables are independent
D) When outliers are present
  • 13. What does a scree test help determine in factor analysis?
A) The significance of variables
B) The standard deviation of variables
C) The correlation between variables
D) The number of factors to retain
  • 14. How is MANOVA different from ANOVA in multivariate analysis?
A) ANOVA uses mixed-effect models, while MANOVA uses fixed-effect models
B) ANOVA is appropriate for small sample sizes, while MANOVA is for large sample sizes
C) MANOVA is used for categorical data analysis, while ANOVA is used for continuous data analysis
D) MANOVA considers multiple dependent variables simultaneously, while ANOVA focuses on a single dependent variable
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