Practice Problems: Suppose you will perform a test to determine whether there is sufficient evidence to support a claim of a linear correlation between two variables. Find the critical values of r given the number of pairs of data n and the significance level a. n = 15, a = 0.01

MATLAB: An Introduction with Applications
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### Practice Problems:

Suppose you will perform a test to determine whether there is sufficient evidence to support a claim of a linear correlation between two variables. Find the critical values of r given the number of pairs of data \( n \) and the significance level \( \alpha \).

Given: 
\( n = 15 \)
\( \alpha = 0.01 \)

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Explanation:

- **Number of pairs of data (n)**: This is the number of paired data points you are using to determine the correlation. 
- **Significance level (\(\alpha\))**: This is the probability of rejecting the null hypothesis when it is actually true. In this case, \(\alpha = 0.01\) indicates a 1% risk of concluding that a correlation exists when there is actually no correlation.

To solve this problem, you would typically use a table of critical values for Pearson's correlation coefficient or perform the calculations using statistical software.

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This section is designed to help students understand how to determine the critical values in testing for linear correlation and apply these concepts in statistical analysis.

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Transcribed Image Text:--- ### Practice Problems: Suppose you will perform a test to determine whether there is sufficient evidence to support a claim of a linear correlation between two variables. Find the critical values of r given the number of pairs of data \( n \) and the significance level \( \alpha \). Given: \( n = 15 \) \( \alpha = 0.01 \) --- Explanation: - **Number of pairs of data (n)**: This is the number of paired data points you are using to determine the correlation. - **Significance level (\(\alpha\))**: This is the probability of rejecting the null hypothesis when it is actually true. In this case, \(\alpha = 0.01\) indicates a 1% risk of concluding that a correlation exists when there is actually no correlation. To solve this problem, you would typically use a table of critical values for Pearson's correlation coefficient or perform the calculations using statistical software. --- This section is designed to help students understand how to determine the critical values in testing for linear correlation and apply these concepts in statistical analysis. ---
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