The data below are ages and systolic blood pressures (measured in millimeters of mercury) of 9 randomly selected adults.   a)Identify the null and alternative hypotheses b)Find the linear correlation coefficient. Round the answer to 3 decimal places. c)Find the test statistics d)Find the p-value andcritical value e)Reject H0" or "Do not reject H0"? f)State the conclusion. g)Find the equation of the regression line for the given data. Round the regression line values to 2 decimal places. h)What would be the predicted pressure if the age was 60? Round the predicted pressure to the nearest whole number.

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The data below are ages and systolic blood pressures (measured in millimeters of mercury) of 9 randomly selected adults.

 

a)Identify the null and alternative hypotheses

b)Find the linear correlation coefficient. Round the answer to 3 decimal places.

c)Find the test statistics

d)Find the p-value andcritical value

e)Reject H0" or "Do not reject H0"?

f)State the conclusion.

g)Find the equation of the regression line for the given data. Round the regression line values to 2 decimal places.

h)What would be the predicted pressure if the age was 60? Round the predicted pressure to the nearest whole number.

### Analyzing the Correlation Between Age and Systolic Blood Pressure

In this educational example, we will examine the linear correlation between age (denoted as \( x \)) and systolic blood pressure (denoted as \( y \)) at a significance level of \( \alpha = 0.05 \).

#### Provided Data:

| Age, \( x \) | 39 | 41 | 45 | 48 | 51 | 53 | 57 | 61 | 65 |
|--------------|----|----|----|----|----|----|----|----|----|
| Pressure, \( y \) | 115 | 120 | 123 | 131 | 142 | 145 | 148 | 150 | 152 |

#### Objective:

To determine whether there is a statistically significant linear correlation between the variables age and systolic blood pressure at the \( \alpha = 0.05 \) significance level.

#### Steps to Determine Linear Correlation:

1. **Plot the Data**: Create a scatter plot with 'Age' on the x-axis and 'Systolic Blood Pressure' on the y-axis to visually assess the relationship.
  
2. **Calculate the Correlation Coefficient** \( r \): Use the formula for Pearson's correlation coefficient to quantify the strength and direction of the linear relationship between the two variables.

3. **Test the Significance of the Correlation Coefficient**:
   - Null Hypothesis (\( H_0 \)): There is no linear correlation between age and systolic blood pressure (\( \rho = 0 \)).
   - Alternative Hypothesis (\( H_1 \)): There is a linear correlation between age and systolic blood pressure (\( \rho \neq 0 \)).

4. **Determine the Critical Value**: Check the critical value for the correlation coefficient based on the given \( \alpha \) level and degrees of freedom (\( df = n - 2 \)) where \( n \) is the number of pairs.

5. **Compare the Computed \( r \) Value**: If the computed \( r \) value is greater than the critical value, reject the null hypothesis.

6. **Conclusion**: Based on the comparison, conclude whether there is a significant linear correlation between age and systolic blood pressure.

This process allows us to scientifically determine if age is a predictor of systolic blood pressure, which can be crucial for
Transcribed Image Text:### Analyzing the Correlation Between Age and Systolic Blood Pressure In this educational example, we will examine the linear correlation between age (denoted as \( x \)) and systolic blood pressure (denoted as \( y \)) at a significance level of \( \alpha = 0.05 \). #### Provided Data: | Age, \( x \) | 39 | 41 | 45 | 48 | 51 | 53 | 57 | 61 | 65 | |--------------|----|----|----|----|----|----|----|----|----| | Pressure, \( y \) | 115 | 120 | 123 | 131 | 142 | 145 | 148 | 150 | 152 | #### Objective: To determine whether there is a statistically significant linear correlation between the variables age and systolic blood pressure at the \( \alpha = 0.05 \) significance level. #### Steps to Determine Linear Correlation: 1. **Plot the Data**: Create a scatter plot with 'Age' on the x-axis and 'Systolic Blood Pressure' on the y-axis to visually assess the relationship. 2. **Calculate the Correlation Coefficient** \( r \): Use the formula for Pearson's correlation coefficient to quantify the strength and direction of the linear relationship between the two variables. 3. **Test the Significance of the Correlation Coefficient**: - Null Hypothesis (\( H_0 \)): There is no linear correlation between age and systolic blood pressure (\( \rho = 0 \)). - Alternative Hypothesis (\( H_1 \)): There is a linear correlation between age and systolic blood pressure (\( \rho \neq 0 \)). 4. **Determine the Critical Value**: Check the critical value for the correlation coefficient based on the given \( \alpha \) level and degrees of freedom (\( df = n - 2 \)) where \( n \) is the number of pairs. 5. **Compare the Computed \( r \) Value**: If the computed \( r \) value is greater than the critical value, reject the null hypothesis. 6. **Conclusion**: Based on the comparison, conclude whether there is a significant linear correlation between age and systolic blood pressure. This process allows us to scientifically determine if age is a predictor of systolic blood pressure, which can be crucial for
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