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3.4 Introduction to Multivariate Statistics — Test 1
Q1. Multivariate statistics involves the simultaneous analysis of:✓ Two or more variables together
Q2. Principal component analysis (PCA) is mainly used for:✓ Reducing the dimensionality of data while retaining most variation
Q3. Cluster analysis is a multivariate method used to:✓ Group similar objects (or individuals) into clusters
Q4. Discriminant analysis is used to:✓ Classify individuals into predefined groups based on several variables
Q5. Multiple regression differs from simple regression in that multiple regression uses:✓ Two or more predictor (independent) variables
Q6. MANOVA (multivariate analysis of variance) extends ANOVA by:✓ Allowing several dependent variables to be analysed at once
Q7. Factor analysis is a multivariate technique that aims to:✓ Identify underlying latent factors explaining correlations among variables
Q8. A study analysing one variable at a time is described as:✓ Univariate
Q9. In PCA, the first principal component is the one that:✓ Captures the largest amount of variation in the data
Q10. A major benefit of dimensionality reduction (e.g. PCA) is that it:✓ Simplifies complex data and aids visualisation
Q11. Multivariate methods are valuable in biology because biological data are often:✓ Characterised by many interrelated variables
Q12. Canonical correlation analysis examines the relationship between:✓ Two sets of variables
Q13. A correlation matrix, often used as input to multivariate methods, displays the:✓ Pairwise correlations among many variables
Q14. Compared with univariate analysis, multivariate analysis can:✓ Reveal relationships and patterns among variables that single-variable analysis misses
Q15. Cluster analysis differs from discriminant analysis in that cluster analysis:✓ Finds groups without predefined categories, whereas discriminant analysis uses known groups
Q16. The components produced by PCA are:✓ Uncorrelated (orthogonal) with one another
Q17. Multivariate techniques generally require:✓ Computational/statistical software due to their complexity
Q18. The multivariate normal distribution is the multivariable generalisation of the:✓ Normal distribution
Q19. An overall purpose of multivariate statistics is to:✓ Summarise, classify or find structure in data with many variables
Q20. Match each multivariate method with its purpose and select the correct option.✓ A-iii, B-i, C-iv, D-ii