Step 1: Understand the statistical purpose.
- Factor analysis is a statistical method that examines relationships among observed variables. - It identifies latent variables (factors) that explain patterns of correlations among measured variables.
Step 2: Analyze given options.
- Correlation: Measures relationship between two variables only. (Incorrect)
- Regression: Predicts one variable based on another. (Incorrect)
- Factor Analysis: Discovers underlying structure from loading patterns. (Correct)
- Item Analysis: Evaluates test questions, not loading patterns. (Incorrect)
Thus, the correct statistical technique is: \[ \boxed{{Factor Analysis}} \]
List-I(Statistical Concepts) | List-II(Description) | ||
---|---|---|---|
A | Power of a test | I | 1- probability of making type II error |
B | Multicollinearity | II | Where the sample mean differs from the population mean |
C | Biased estimator | III | Correlation between explanatory variables in a regres sion |
D | White noise error | IV | Errors with zero mean and constant standard deviation |
If Soni got an intelligence score of 115, then what percentage of the population (% as given in the graph) will have intelligence scores higher than the score obtained by Soni? (rounded off to 2 decimal places)
Assertions:
P. Factor analysis reduces a large number of measures to a smaller number of clusters.
Q. The measures within each cluster are highly interconnected and reflect the same underlying dimensions.
Which of the following is correct?
Match the following: