A) Analysis of multiple variables simultaneously
B) Analysis of a single variable
C) Analysis of two variables
D) Analysis of continuous variables only

A) T-test
B) Chi-square test
C) ANOVA
D) Principal component analysis

A) ANOVA
B) Correlation analysis
C) Regression analysis
D) Cluster analysis

A) To determine correlation coefficients
B) To determine descriptive statistics
C) To determine outliers
D) To determine which variables discriminate between two or more group

A) To determine the number of factors to retain in factor analysis
B) To plot data points
C) To identify outliers
D) To show correlation coefficients

A) To find correlation between a variable and itself
B) To test hypotheses
C) To examine the relationships between two sets of variables
D) To perform regression analysis

A) The number of factors to retain
B) The standard deviation of variables
C) The correlation between variables
D) The significance of variables

A) Grouping similar observations into clusters
B) Testing for differences between groups
C) Plotting bivariate data
D) Conducting factor analysis

A) To perform factor analysis
B) To determine sample size
C) To understand the relationships and variances between multiple variables
D) To test for outliers

A) To find outliers
B) To determine correlations
C) To predict group membership based on predictor variables
D) To perform cluster analysis

A) When variables are independent
B) When dealing with categorical data only
C) When variables are highly correlated
D) When outliers are present

A) ANOVA uses mixed-effect models, while MANOVA uses fixed-effect models
B) MANOVA is used for categorical data analysis, while ANOVA is used for continuous data analysis
C) ANOVA is appropriate for small sample sizes, while MANOVA is for large sample sizes
D) MANOVA considers multiple dependent variables simultaneously, while ANOVA focuses on a single dependent variable

A) To perform hypothesis testing
B) To determine factor loadings
C) To determine the relationship between two sets of variables
D) To determine outliers

A) Conduct factor analysis
B) Determine which variables best predict group membership
C) Test for correlations
D) Identify outliers in the data