The data below shows the selling price (in hundred thousands) and the list price (in hundred thousands) of homes sold. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between the two variables? 300 320 315 353 420 431 332 Selling Price (x) List Price (y) 402 375 434 457 477 413 390 440 486 476 322 366 343 What are the null and alternative hypotheses? O A. Ho: p#0 Hip=0 O B. Ho: p=0 H,:p>0 OC. Ho: p=0 O D. Ho: p=0 H,ip<0 H,:p#0

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The data below shows the selling price (in hundred thousands) and the list price (in hundred thousands) of homes sold. Construct a scatterplot, find the value of the linear correlation coefficient \( r \), and find the P-value using \( \alpha = 0.05 \). Is there sufficient evidence to conclude that there is a linear correlation between the two variables?

| Selling Price (x) | 402 | 300 | 375 | 434 | 457 | 477 | 315 | 353 | 420 | 332 |
|-------------------|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|
| List Price (y)    | 413 | 320 | 390 | 440 | 486 | 476 | 322 | 366 | 431 | 343 |

What are the null and alternative hypotheses?

- **A.** \( H_0: \rho \neq 0 \) \( \quad \) \( H_1: \rho = 0 \)
- **B.** \( H_0: \rho = 0 \) \( \quad \) \( H_1: \rho > 0 \)
- **C.** \( H_0: \rho = 0 \) \( \quad \) \( H_1: \rho < 0 \)
- **D.** \( H_0: \rho = 0 \) \( \quad \) \( H_1: \rho \neq 0 \)

**Explanation:**
- The table lists pairs of selling and list prices of homes. 
- The task is to create a scatterplot to visually assess any correlation.
- Calculation of the correlation coefficient \( r \) and the P-value will help determine the statistical significance.
- Different hypotheses setups are provided, and it's necessary to choose the appropriate one for testing the linear correlation.
Transcribed Image Text:The data below shows the selling price (in hundred thousands) and the list price (in hundred thousands) of homes sold. Construct a scatterplot, find the value of the linear correlation coefficient \( r \), and find the P-value using \( \alpha = 0.05 \). Is there sufficient evidence to conclude that there is a linear correlation between the two variables? | Selling Price (x) | 402 | 300 | 375 | 434 | 457 | 477 | 315 | 353 | 420 | 332 | |-------------------|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----| | List Price (y) | 413 | 320 | 390 | 440 | 486 | 476 | 322 | 366 | 431 | 343 | What are the null and alternative hypotheses? - **A.** \( H_0: \rho \neq 0 \) \( \quad \) \( H_1: \rho = 0 \) - **B.** \( H_0: \rho = 0 \) \( \quad \) \( H_1: \rho > 0 \) - **C.** \( H_0: \rho = 0 \) \( \quad \) \( H_1: \rho < 0 \) - **D.** \( H_0: \rho = 0 \) \( \quad \) \( H_1: \rho \neq 0 \) **Explanation:** - The table lists pairs of selling and list prices of homes. - The task is to create a scatterplot to visually assess any correlation. - Calculation of the correlation coefficient \( r \) and the P-value will help determine the statistical significance. - Different hypotheses setups are provided, and it's necessary to choose the appropriate one for testing the linear correlation.
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