MCQ Quiz

Question 1 of 7

Which of the following is a necessary assumption for performing a parametric t-test on a correlation coefficient?

The variables are normally distributed.
The variables are ranked.
The variables are categorical.

Question 2 of 7

If the calculated t-statistic for a correlation coefficient exceeds the critical value, the analyst should most likely:

Reject the null hypothesis and conclude the correlation is significant.
Fail to reject the null hypothesis and conclude the correlation is zero.
Reject the alternative hypothesis and conclude the variables are independent.

Question 3 of 7

In a parametric test of the hypothesis that the population correlation coefficient equals zero involving $n$ observations, the degrees of freedom are:

n - 1
n - 2
n

Question 4 of 7

The null hypothesis ($H_0$) for a parametric test investigating whether a relationship exists between two variables is most accurately stated as:

$\rho = 0$
$r = 0$
$\rho = 1$

Question 5 of 7

When conducting a parametric test to determine if a population correlation coefficient differs from zero, which probability distribution does the test statistic follow?

t-distribution
z-distribution
Chi-square distribution

Question 6 of 7

Given a sample correlation $r = 0.50$ and a sample size of $n = 6$, the calculated t-statistic is closest to:

1.15
1.00
0.58

Question 7 of 7

An analyst tests the significance of a correlation coefficient $r = 0.40$. If the sample size $n$ increases while the correlation coefficient remains constant, the calculated t-statistic will most likely:

Increase
Decrease
Remain the same

Tests for Independence (Katas)

First Principles Thinking: Assumptions of parametric tests

First Principles Thinking: interpreting hypothesis test results

First Principles Thinking: Degrees of freedom in bivariate analysis

First Principles Thinking: Formulation of statistical hypotheses

First Principles Thinking: The role of the t-distribution in correlation testing

First Principles Thinking: Calculating the test statistic

First Principles Thinking: Impact of sample size on statistical significance