Select a significance level α and reject the null-hypothesis if the p-value is less than α. Explain, in complete sentences, your findings: Is there a statistically significant association (at α level) between the provided genes? What is the magnitude and the direction of the association?

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Select a significance level α and reject the null-hypothesis if the p-value is less than α. Explain, in complete sentences, your findings: Is there a statistically significant association (at α level) between the provided genes? What is the magnitude and the direction of the association?

### Transcription of Image Content for Educational Purposes

#### R Code for Correlation Coefficient Calculation

1. **Using cor() method:**
   - Code:
     ```r
     result = cor(x, y, method = "pearson")
     ```

2. **Print the result:**
   - Code:
     ```r
     cat("Person correlation coefficient is:", result)
     ```

   - Output:
     ```
     Person correlation coefficient is: 0.9312196
     ```

3. **Using cor.test() method:**
   - Code:
     ```r
     res = cor.test(x, y, method = "pearson")
     ```

4. **Print the detailed result:**
   - Code:
     ```r
     print(res)
     ```

   - Output:
     ```
     Pearson's product-moment correlation

     data: x and y
     t = 26.553, df = 108, p-value < 2.2e-16
     alternative hypothesis: true correlation is not equal to 0
     95 percent confidence interval:
     0.901059 0.9523982
     sample estimates:
     cor 
     0.9312196
     ```

### Explanation of Output

- **Pearson's Product-Moment Correlation:** This is a measure of the linear correlation between two variables, x and y.
  
- **Data:** The variables analyzed are labeled as x and y.

- **t-value:** 26.553; this statistic is used to determine the significance of the correlation.

- **Degrees of Freedom (df):** 108; represents the number of values in the calculation that are free to vary.

- **p-value:** Less than 2.2e-16; suggests a very strong statistical significance for the correlation.

- **Alternative Hypothesis:** The hypothesis that the true correlation is not equal to zero.

- **95 Percent Confidence Interval:** Ranges from 0.901059 to 0.9523982, indicating the precision of the estimate.

- **Sample Estimate for Correlation (cor):** 0.9312196; a high value indicating a strong positive linear relationship between variables x and y.
Transcribed Image Text:### Transcription of Image Content for Educational Purposes #### R Code for Correlation Coefficient Calculation 1. **Using cor() method:** - Code: ```r result = cor(x, y, method = "pearson") ``` 2. **Print the result:** - Code: ```r cat("Person correlation coefficient is:", result) ``` - Output: ``` Person correlation coefficient is: 0.9312196 ``` 3. **Using cor.test() method:** - Code: ```r res = cor.test(x, y, method = "pearson") ``` 4. **Print the detailed result:** - Code: ```r print(res) ``` - Output: ``` Pearson's product-moment correlation data: x and y t = 26.553, df = 108, p-value < 2.2e-16 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.901059 0.9523982 sample estimates: cor 0.9312196 ``` ### Explanation of Output - **Pearson's Product-Moment Correlation:** This is a measure of the linear correlation between two variables, x and y. - **Data:** The variables analyzed are labeled as x and y. - **t-value:** 26.553; this statistic is used to determine the significance of the correlation. - **Degrees of Freedom (df):** 108; represents the number of values in the calculation that are free to vary. - **p-value:** Less than 2.2e-16; suggests a very strong statistical significance for the correlation. - **Alternative Hypothesis:** The hypothesis that the true correlation is not equal to zero. - **95 Percent Confidence Interval:** Ranges from 0.901059 to 0.9523982, indicating the precision of the estimate. - **Sample Estimate for Correlation (cor):** 0.9312196; a high value indicating a strong positive linear relationship between variables x and y.
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