Listed below are amounts of court income and salaries paid to the town justices. All amounts are in thousands of dollars. Construct a scatterplot, find t 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 court incomes and justice salaries? Based on the results, does it appear that justices might profit by levying larger fines? Court Income 66.0 404.0 1567.01130.0 271.0 251.0 112.0 154.0 32.0 e Justice Salary 30 46 90 57 44 61 25 26 18 ..... What are the null and alternative hypotheses? O A. Ho: p=0 H1: p#0 O B. Ho: p=0 H,:p>0 OC. Ho: p#0 H4:p=0 O D. Ho: p=0 H:p<0
Listed below are amounts of court income and salaries paid to the town justices. All amounts are in thousands of dollars. Construct a scatterplot, find t 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 court incomes and justice salaries? Based on the results, does it appear that justices might profit by levying larger fines? Court Income 66.0 404.0 1567.01130.0 271.0 251.0 112.0 154.0 32.0 e Justice Salary 30 46 90 57 44 61 25 26 18 ..... What are the null and alternative hypotheses? O A. Ho: p=0 H1: p#0 O B. Ho: p=0 H,:p>0 OC. Ho: p#0 H4:p=0 O D. Ho: p=0 H:p<0
MATLAB: An Introduction with Applications
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Author:Amos Gilat
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Chapter1: Starting With Matlab
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Transcribed Image Text:**Title: Analysis of Court Income and Justice Salaries**
**Introduction:**
Listed below are amounts of court income and salaries paid to town justices. All amounts are in thousands of dollars. Construct a scatterplot, find the value of the linear correlation coefficient \( r \), and determine the P-value using \( \alpha = 0.05 \). Is there sufficient evidence to conclude that there is a linear correlation between court incomes and justice salaries? Based on the results, does it appear that justices might profit by levying larger fines?
**Data:**
| Court Income | 66.0 | 404.0 | 1567.0 | 1130.0 | 271.0 | 251.0 | 112.0 | 154.0 | 32.0 |
|----------------|------|-------|--------|--------|-------|-------|-------|-------|------|
| Justice Salary | 30 | 46 | 90 | 57 | 44 | 61 | 25 | 26 | 18 |
**Hypotheses:**
- **Null Hypothesis ( \( H_0 \) ):** \( \rho = 0 \)
- **Alternative Hypothesis ( \( H_1 \) ):** \( \rho \neq 0 \)
**Scatterplot Options:**
The following scatterplots are provided as options to represent the relationship between Court Income and Justice Salary:
- **Graph A:** Positively sloped data points correlating Justice Salary with Court Income.
- **Graph B:** A more scattered distribution with no clear trend.
- **Graph C:** A negatively sloped line indicating an inverse relationship.
- **Graph D:** A random dispersion with no clear pattern.
**Linear Correlation Analysis:**
- **Linear Correlation Coefficient** \( r = \_\_\_ \).
(Round to three decimal places as needed.)
- **Test Statistic** \( t = \_\_\_ \).
(Round to three decimal places as needed.)
This analysis will aid in understanding whether the justice salaries are potentially influenced by the court income, potentially suggesting monetary incentives in decision making.
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