. 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. Step 1. State the hypotheses. A B C D Step 2. Find the critical values. df = _______ CV = +/- (do not include +/-) DO NOT USE eTEXT TABLE C.5 - CVs ARE WRONG. Use table attached to homework instead. _________ Step 4. Make a decision about the null hypothesis. a. Reject H0 b. Retain H0
. 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. Step 1. State the hypotheses. A B C D Step 2. Find the critical values. df = _______ CV = +/- (do not include +/-) DO NOT USE eTEXT TABLE C.5 - CVs ARE WRONG. Use table attached to homework instead. _________ Step 4. Make a decision about the null hypothesis. a. Reject H0 b. Retain H0
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
100%
Q25. Using the Pearson
Step 1. State the hypotheses.
A
B
C
D
Step 2. Find the critical values. df = _______
CV = +/- (do not include +/-) DO NOT USE eTEXT TABLE C.5 - CVs ARE WRONG. Use table attached to homework instead. _________
Step 4. Make a decision about the null hypothesis.
a. Reject H0
b. Retain H0

Transcribed Image Text:### Hypothesis Testing Examples
Here are four examples of null and alternative hypotheses, represented by \( H_0 \) and \( H_1 \), respectively:
#### (A)
- **Null Hypothesis (\( H_0 \))**: \( \rho = 0.51 \)
- **Alternative Hypothesis (\( H_1 \))**: \( \rho \neq 0.51 \)
#### (B)
- **Null Hypothesis (\( H_0 \))**: \( \rho = 0 \)
- **Alternative Hypothesis (\( H_1 \))**: \( \rho \neq 0 \)
#### (C)
- **Null Hypothesis (\( H_0 \))**: \( \rho \neq 0 \)
- **Alternative Hypothesis (\( H_1 \))**: \( \rho = 0 \)
#### (D)
- **Null Hypothesis (\( H_0 \))**: \( \rho \neq 0.51 \)
- **Alternative Hypothesis (\( H_1 \))**: \( \rho = 0.51 \)
These examples illustrate how hypotheses can be structured to test specific claims about a parameter, such as a correlation coefficient (\( \rho \)) in a population.
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