Answer:
Answer explained
Step-by-step explanation:
From the information, the true statements for a hypothesis test for correlation is,
If the P-value is less than or equal to the significance level, we should reject the null hypothesis and conclude that there is sufficient evidence to support the claim of a linear correlation.
If |r| less than or equals critical value, we should fail to reject the null hypothesis and conclude that there is not sufficient evidence to support the claim of a linear correlation.
If the P-value is greater than the significance level, we should fail to reject the null hypothesis and conclude that there is not sufficient evidence to support the claim of a linear correlation.
<u>Explanation</u>
According to the decision rule of P-value approach, observe that if the P-value is less than or equal to the significance level, then there is evidence to reject the null hypothesis and conclude that there is sufficient evidence to support the claim of a linear correlation.
Similarly, if the P-value is greater than the significance level, we should fail to reject the null hypothesis and conclude that there is not sufficient evidence to support the claim of a linear correlation.
According to the critical value approach, observe that if the test statistic value is greater than the critical value, then there is evidence to reject the null hypothesis and conclude that there is sufficient evidence to support the claim of a linear correlation.
- If |r| greater than critical value, we should fail to reject the null hypothesis and conclude that there is not sufficient evidence to support the claim of a linear correlation.
<u>Explanation
</u>
Based on the decision rules of the hypothesis test, the false statement about the test for a correlation is, if |r| less than or equals critical value, we should fail to reject the null hypothesis and conclude that there is not sufficient evidence to support the claim of a linear correlation.
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