You wish to determine if there is a positive linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.01 and round all values to 4 decimal places. Driver Age Number of Driver Deaths per 100,000 18 35 38 26 18 23 57 36 64 29 80 29 53 36 Ho: p = 0 Ha: p > 0 Find the Linear Correlation Coefficient r Find the p-value A p-value=
You wish to determine if there is a positive linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.01 and round all values to 4 decimal places. Driver Age Number of Driver Deaths per 100,000 18 35 38 26 18 23 57 36 64 29 80 29 53 36 Ho: p = 0 Ha: p > 0 Find the Linear Correlation Coefficient r Find the p-value A p-value=
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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### Exploring Correlation Between Driver Age and Driver Deaths
In this exercise, we aim to determine if there is a positive linear correlation between the age of a driver and the number of driver deaths. The table below presents the relationship between the age of a driver and the number of driver deaths per 100,000 drivers. A significance level of 0.01 will be used for this analysis, and all values will be rounded to four decimal places.
#### Data Table: Driver Age vs. Number of Driver Deaths per 100,000
| **Driver Age** | **Number of Driver Deaths per 100,000** |
|:--------------:|:--------------------------------------:|
| 18 | 35 |
| 38 | 26 |
| 18 | 23 |
| 57 | 36 |
| 64 | 29 |
| 80 | 29 |
| 53 | 36 |
#### Hypothesis Testing
- **Null Hypothesis (Ho):** ρ = 0
- **Alternative Hypothesis (Ha):** ρ > 0
#### Analysis
##### 1. Calculate the Linear Correlation Coefficient
- Denoted as **r**, the linear correlation coefficient will quantify the strength and direction of the linear relationship between driver age and the number of driver deaths.
\[ r = \_ \_ \_ \_ \_ \_ \_ \_ \]
##### 2. Determine the p-value
- The **p-value** will help in testing the null hypothesis. A p-value lower than 0.01 would indicate that we can reject the null hypothesis in favor of the alternative hypothesis, supporting the presence of a positive linear correlation.
\[ p\text{-value} = \_ \_ \_ \_ \_ \_ \_ \_ \]
---
By filling in these values, students will gain insight into statistical methods, specifically linear correlation and hypothesis testing.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fbc3b72c7-eb99-406f-83fe-82fc41b2a8b5%2F39182f77-6adb-4874-9941-0312d07fd2e6%2F1vk0j0n_processed.jpeg&w=3840&q=75)
Transcribed Image Text:---
### Exploring Correlation Between Driver Age and Driver Deaths
In this exercise, we aim to determine if there is a positive linear correlation between the age of a driver and the number of driver deaths. The table below presents the relationship between the age of a driver and the number of driver deaths per 100,000 drivers. A significance level of 0.01 will be used for this analysis, and all values will be rounded to four decimal places.
#### Data Table: Driver Age vs. Number of Driver Deaths per 100,000
| **Driver Age** | **Number of Driver Deaths per 100,000** |
|:--------------:|:--------------------------------------:|
| 18 | 35 |
| 38 | 26 |
| 18 | 23 |
| 57 | 36 |
| 64 | 29 |
| 80 | 29 |
| 53 | 36 |
#### Hypothesis Testing
- **Null Hypothesis (Ho):** ρ = 0
- **Alternative Hypothesis (Ha):** ρ > 0
#### Analysis
##### 1. Calculate the Linear Correlation Coefficient
- Denoted as **r**, the linear correlation coefficient will quantify the strength and direction of the linear relationship between driver age and the number of driver deaths.
\[ r = \_ \_ \_ \_ \_ \_ \_ \_ \]
##### 2. Determine the p-value
- The **p-value** will help in testing the null hypothesis. A p-value lower than 0.01 would indicate that we can reject the null hypothesis in favor of the alternative hypothesis, supporting the presence of a positive linear correlation.
\[ p\text{-value} = \_ \_ \_ \_ \_ \_ \_ \_ \]
---
By filling in these values, students will gain insight into statistical methods, specifically linear correlation and hypothesis testing.
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