Consider the following data on x = rainfall volume (m³) and y = runoff volume (m³) for a particular location. X 4 12 14 17 23 30 40 47 55 67 72 82 96 112 127 y 4 10 13 15 15 25 27 46 38 46 53 70 82 99 100 Use the accompanying Minitab output to decide whether there is a useful linear relationship between rainfall and runoff. The regression equation is runoff = -0.97 +0.824 rainfall Coef -0.971 0.82401 s = 5.211 R-sq = 97.6% State the appropriate null and alternative hypotheses. Ho: B₁ 0 H₂: B₁ = 0 O Ho: B₁ = 0 H₂: B₁ <0 ⒸH₁: B₁ = 0 H₂: B₁ > 0 Predictor Constant rainfall ⒸH₁: B₁ = 0 H₂: B₁ * 0 t= P-value= Stdev 2.349 0.03618 t-ratio -0.41 22.77 R-sq (adj) 97.4% = Compute the test statistic value and find the P-value. (Round your test statistic to two decimal places and your P-value to three P 0.686 0.000 State the conclusion in the problem context. (Use a = 0.05.) O Reject Ho. There is a useful linear relationship between runoff and rainfall at the 0.05 level. Reject Ho. There is not a useful linear relationship between runoff and rainfall at the 0.05 level. Fail to reject Ho. There is not a useful linear relationship between runoff and rainfall at the 0.05 level.

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**Title: Analyzing the Linear Relationship Between Rainfall and Runoff**

**Introduction:**
This example involves analyzing the linear relationship between rainfall volume (\(x\)) and runoff volume (\(y\)) for a particular location. We use the accompanying Minitab output to make statistical inferences about this relationship.

**Data:**

- Independent variable (\(x\)): Rainfall volume (in cm)
- Dependent variable (\(y\)): Runoff volume (in m\(^3\))

Given data pairs:
- \(x\) values: 4, 12, 13, 35, 57, 35, 48, 75, 82, 96, 112, 127
- \(y\) values: 14, 13, 15, 27, 45, 46, 57, 83, 99, 100, 111, 127

**Statistical Output from Minitab:**

The regression equation is computed as:
\[ \hat{y} = 0.971 + 0.602x \]

- **Predictor**: 
  - Constant: 0.971
  - Coefficient for rainfall: 0.602
- **Standard Error (SE) for Coefficient**: 0.021
- **T-statistic** for coefficient: 28.71
- **P-value**: 0.000

**Statistical Measures:**

- Standard deviation of residuals (\(s\)): 5.021
- Coefficient of determination (\(R^2\)): 97.6%
- Adjusted \(R^2\): 97.4%

**Hypothesis Testing:**

**Null and Alternative Hypotheses:**
- \(H_0: \beta = 0\)
- \(H_a: \beta \neq 0\)

**Conclusion Steps:**

1. **Compute the test statistic value** and find the **P-value**.

2. **Conclusion at \(\alpha = 0.05\):**
   - Reject \(H_0\): There is a significant linear relationship between rainfall and runoff at the 0.05 level.
   - Fail to reject \(H_0\): There is not a significant linear relationship between rainfall and runoff at the 0.05
Transcribed Image Text:Certainly! Below is a transcription suitable for an educational website: --- **Title: Analyzing the Linear Relationship Between Rainfall and Runoff** **Introduction:** This example involves analyzing the linear relationship between rainfall volume (\(x\)) and runoff volume (\(y\)) for a particular location. We use the accompanying Minitab output to make statistical inferences about this relationship. **Data:** - Independent variable (\(x\)): Rainfall volume (in cm) - Dependent variable (\(y\)): Runoff volume (in m\(^3\)) Given data pairs: - \(x\) values: 4, 12, 13, 35, 57, 35, 48, 75, 82, 96, 112, 127 - \(y\) values: 14, 13, 15, 27, 45, 46, 57, 83, 99, 100, 111, 127 **Statistical Output from Minitab:** The regression equation is computed as: \[ \hat{y} = 0.971 + 0.602x \] - **Predictor**: - Constant: 0.971 - Coefficient for rainfall: 0.602 - **Standard Error (SE) for Coefficient**: 0.021 - **T-statistic** for coefficient: 28.71 - **P-value**: 0.000 **Statistical Measures:** - Standard deviation of residuals (\(s\)): 5.021 - Coefficient of determination (\(R^2\)): 97.6% - Adjusted \(R^2\): 97.4% **Hypothesis Testing:** **Null and Alternative Hypotheses:** - \(H_0: \beta = 0\) - \(H_a: \beta \neq 0\) **Conclusion Steps:** 1. **Compute the test statistic value** and find the **P-value**. 2. **Conclusion at \(\alpha = 0.05\):** - Reject \(H_0\): There is a significant linear relationship between rainfall and runoff at the 0.05 level. - Fail to reject \(H_0\): There is not a significant linear relationship between rainfall and runoff at the 0.05
**Instruction for Educational Website: Confidence Interval Calculation**

**Task:**
Calculate a 95% confidence interval for the true average change in runoff volume associated with a 1 m³ increase in rainfall volume. Round your answers to three decimal places.

**Answer Box:**
( _______ , _______ ) m³

**Additional Information:**
You may need to use the appropriate table in the Appendix of Tables to answer this question.

**Support:**
If you need help, click the "Read It" button.
Transcribed Image Text:**Instruction for Educational Website: Confidence Interval Calculation** **Task:** Calculate a 95% confidence interval for the true average change in runoff volume associated with a 1 m³ increase in rainfall volume. Round your answers to three decimal places. **Answer Box:** ( _______ , _______ ) m³ **Additional Information:** You may need to use the appropriate table in the Appendix of Tables to answer this question. **Support:** If you need help, click the "Read It" button.
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