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Using the Pearson correlation coefficient, a study on addiction found a positive correlation between time of cravings and time of relapse (r = .51) in a sample of 20 people with a drug addiction. Using a two-tailed test at a .05 level of significance, test whether the correlation is significantly different than 0. Follow the prompts. What conclusion should be drawn? A. The positive correlation was significant. The more time spent craving a drug, the less time a person spent in relapse. B. The positive correlation was not significant. There was no relation between time spent craving a drug and time spent in relapse. C. The positive correlation was not significant. The more time spent craving a drug, the less time a person spent in relapse. D. The positive correlation was significant. The more time spent craving a drug, the longer time a person spent in relapse.

Using the Pearson correlation coefficient, a study on addiction found a positive correlation between time of cravings and time of relapse (r = .51) in a sample of 20 people with a drug addiction. Using a two-tailed test at a .05 level of significance, test whether the correlation is significantly different than 0. Follow the prompts. What conclusion should be drawn? A. The positive correlation was significant. The more time spent craving a drug, the less time a person spent in relapse. B. The positive correlation was not significant. There was no relation between time spent craving a drug and time spent in relapse. C. The positive correlation was not significant. The more time spent craving a drug, the less time a person spent in relapse. D. The positive correlation was significant. The more time spent craving a drug, the longer time a person spent in relapse.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Q25. Using the Pearson correlation coefficient, a study on addiction found a positive correlation between time of cravings and time of relapse (r = .51) in a sample of 20 people with a drug addiction. Using a two-tailed test at a .05 level of significance, test whether the correlation is significantly different than 0. Follow the prompts.

What conclusion should be drawn?

A. The positive correlation was significant. The more time spent craving a drug, the less time a person spent in relapse.
B. The positive correlation was not significant. There was no relation between time spent craving a drug and time spent in relapse.
C. The positive correlation was not significant. The more time spent craving a drug, the less time a person spent in relapse.
D. The positive correlation was significant. The more time spent craving a drug, the longer time a person spent in relapse.
 

29. An instructor measured quiz scores and the number of hours studying among a sample 20 college students. If SSxy​ = 43, SSx = 99, My​ = 6, and Mx​ = 5, then what is the regression equation for this sample?

a. Y = 0.36X + 13.17

b. Y = 1.01X + 4.71

c. Y = 0.43X + 3.85

d. Y = 1.3X + 2.0

The image presents four different hypothesis test scenarios labeled (A), (B), (C), and (D). Each scenario includes a null hypothesis (H0) and an alternative hypothesis (H1) regarding a parameter ρ (rho). ### (A) - **Null Hypothesis (H0):** ρ = 0.51 - **Alternative Hypothesis (H1):** ρ ≠ 0.51 ### (B) - **Null Hypothesis (H0):** ρ = 0 - **Alternative Hypothesis (H1):** ρ ≠ 0 ### (C) - **Null Hypothesis (H0):** ρ ≠ 0 - **Alternative Hypothesis (H1):** ρ = 0 ### (D) - **Null Hypothesis (H0):** ρ ≠ 0.51 - **Alternative Hypothesis (H1):** ρ = 0.51 These hypotheses are often used in statistical testing to determine if there is significant evidence to support one hypothesis over the other for a given population parameter.
Transcribed Image Text:The image presents four different hypothesis test scenarios labeled (A), (B), (C), and (D). Each scenario includes a null hypothesis (H0) and an alternative hypothesis (H1) regarding a parameter ρ (rho). ### (A) - **Null Hypothesis (H0):** ρ = 0.51 - **Alternative Hypothesis (H1):** ρ ≠ 0.51 ### (B) - **Null Hypothesis (H0):** ρ = 0 - **Alternative Hypothesis (H1):** ρ ≠ 0 ### (C) - **Null Hypothesis (H0):** ρ ≠ 0 - **Alternative Hypothesis (H1):** ρ = 0 ### (D) - **Null Hypothesis (H0):** ρ ≠ 0.51 - **Alternative Hypothesis (H1):** ρ = 0.51 These hypotheses are often used in statistical testing to determine if there is significant evidence to support one hypothesis over the other for a given population parameter.
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