UGM June 2019 Q6
- A
The Spearman correlation coefficient is a measure of the association between two interval variables.
- B
The Pearson correlation coefficient is designed to capture the extent of the linear association between variables.
- C
In regression analysis, the dependent variable is treated as a fixed variable, while the independent variable is random.
- D
The Pearson correlation coefficient can range from +2.0 to –2.0.
- Option : B
- Explanation : The Pearson correlation coefficient (also known as the Pearson product-moment correlation) is the degree of association between variables that are interval or ratio scaled. The Pearson correlation coefficient is insightful in its own right and also plays a
key role in advanced multivariate analysis procedures. The existence of a linear scatter is a prerequisite for the Pearson correlation
coefficient to be meaningful because it is designed only to capture the extent of linear association between variables.
Two other key assumptions that must be met, especially for making statistical inferences based on the Pearson correlation coefficient, are the following:
1. The two variables have a bivariate normal distribution. In other words, the population is such that all units with a given value of one variable have values on the second variable that are normally distributed.
2. The variance of the normal distribution of one variable remains the same across all values of the second variable.
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