Muhammad Imdad Ullah

The post is about the MCQs Multivariate Analysis test. It includes a Variance-Covariance matrix, Principal Component Analysis, Factor Analysis, Factor Loading, etc. Let us start with the Online MCQs Multivariate Quiz.

Online Multivariate Quiz

1. Ffactor loading is

 A correlation coefficient between a variable and a factor (cluster of variables)

 Empirically based hypothetical variable consisting of items which are strongly associated with each other

 The correlation between binomial variable consisting of items which are strongly associated with each other

 The correlation of a variable with a whole score

2. ——- is used for causal analysis

 Canonical correlation

 Factor analysis

 Manova

 Path Analysis

3. In multivariate analysis, $n(\overline{x} – \mu)’ S^{-1} (\overline{x} – \mu)$ is called

 Var-Cov matrix

 Multivariate Normal Distribution

 F Distribution

 Hottleing T Square

4. Factor analysis pinpoints the clusters of correlations between variables and for each cluster

 High

 Complex

 Normal

 Low

5. In principal component analysis (PCA) the first component contains

 Mean deviation

 Maximum variance

 Average variance

 Minimum variance

6. A multivariate statistic that allows you to investigate the relationship between two sets of variables is

 Discriminant analysis

 Multiple regression

 Canonical correlation

 Manova

7. In the relation $\Sigma = V^{1/2} \rho ^{1/2} V^{1/2}$, the $V^{1/2} is called

 Mean matrix

 Covariance matrix

 Variance matrix

 Standard deviation matrix

8. If $X \sim N (\mu, \Sigma)$ then $(X-\mu)’ \Sigma^{-1} (X-\mu)$ is distributed as

 Beta Distribution

 Normally Distribution

 $chi^2$ with $P$ degrees of freedom

 $N_P(\mu, \Sigma)$

9. In multivariate analysis the distribution of $\overline{X}$ is

 Normal

 T Square

 F

 Chi-Square

10. In factor analysis the reliable variance

 Total Variance + Error Variance

 Common Variance + Specific Variance

 Both (Total Variance $-$ Error Variance) and (Common Variance + Specific Variance)

 Total Variance $-$ Error Variance

11. An advantage of using an experimental multivariate design over separate univariate designs is that using the multivariate analysis – – – – – – -.

 Allows you to look t more complex relationships compared to the univariate analysis

 Allows you not to worry about meeting restrictive assumptions characteristic of univariate statistics

 Both (More complex relationships) and (More powerful test of hypothesis)

 Provides a more powerful test of your hypotheses

12. A factor is a combination of variables

 Group

 Multiple

 Complex

 Linear

13. The goal of multiple regression is to

 Test the effect of set of independent variables on a single dependent variable

 Develop causal explanations based on correlational data

 Reduce a large number of correlated measures to a smaller number

 Explained variation in one variable based on variation in a set of others

14. Correlational multivariate analysis includes

 All of the options

 Multiple regression

 Discriminant analysis

 Canonical correlation

15. In principal component analysis, the components are

 Deviated

 Collinear

 Uncorrelated

 Correlated

16. A multivariate statistic that allows you to analyze several dependent variables from an experimental design simultaneously is

 Manova

 Path Analysis

 ANOVA

 Factor Analysis

17. In multivariate analysis Var-Cov matrix is

 $\frac{\Sigma (X_i – \mu)^2}{n-1}$

 $\frac{\Sigma (X_i – \mu)^2}{n}$

 $\Sigma = E(X-\mu)(X-\mu)’$

 $\Sigma = Var(X-\mu)’ (X-\mu)$

18. In multivariate analysis the distribution of the sample covariance matrix is

 Normal distribution

 Wishart distribution

 T-square distribution

 Chi-Square distribution

19. A factor loading of 0.80 means, generally speaking, that

 The item is poorly related to the factor

 The item correlates well with the factor, though not perfectly

 There is no relationship between that variable and the factor

 The variable is moderately related with the factor

20. In PCA, when the variables are measured in different units then PC extracted on the basis of

 Comunalities

 Correlation Matrix

 Variance Covariance matrix

 Specific Variance

 Loading …

An application of different statistical methods applied to the economic data used to find empirical relationships between economic data is called Econometrics. In other words, Econometrics is “the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference”.

Online MCQs Multivariate

• In multivariate analysis Var-Cov matrix is
• In the relation $\Sigma = V^{1/2} \rho ^{1/2} V^{1/2}$, the $V^{1/2} is called
• If $X \sim N (\mu, \Sigma)$ then $(X-\mu)’ \Sigma^{-1} (X-\mu)$ is distributed as
• In multivariate analysis, $n(\overline{x} – \mu)’ S^{-1} (\overline{x} – \mu)$ is called
• In multivariate analysis the distribution of $\overline{X}$ is
• In multivariate analysis the distribution of the sample covariance matrix is
• In factor analysis the reliable variance
• In principal component analysis (PCA) the first component contains
• In principal component analysis, the components are
• In PCA, when the variables are measured in different units then PCs extracted on the basis of
• The goal of multiple regression is to
• A multivariate statistic that allows you to investigate the relationship between two sets of variables is
• Correlational multivariate analysis includes
• An advantage of using an experimental multivariate design over separate univariate designs is that using the multivariate analysis – – – – – – -.
• A multivariate statistic that allows you to analyze several dependent variables from an experimental design simultaneously is
• ——- is used for causal analysis
• Factor loading is
• A factor loading of 0.80 means, generally speaking, that
• A factor is a combination of variables
• Factor analysis pinpoints the clusters of correlations between variables and for each cluster

• Partial Least Squares (PLS) Regression is an example of multivariate analysis (MVA).
• Multivariate Multiple Regression is a method of modeling multiple dependent variables, with a single set of predictor variables.
• Testing text and visual elements on a webpage together.
• An example of multivariate data is Vital signs recorded for a newborn baby: This includes multiple variables such as heart rate, respiratory rate, blood pressure, and temperature.

Online MCQs Multivariate

Online MCQs about various Subjects