Here is a bivariate data set. y 45.6 79.6 74.1 49 51.8 80.3 50.4 67.9 49.2 83.3 55.9 66.4 42.1 92.7 39.5 84.3 96.3 28.6 Find the correlation coefficient and report it accurate to three decimal places. r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place. R2 =

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### Bivariate Data Analysis

#### Data Set

We have a bivariate data set represented in the table below with variables \( x \) and \( y \).

| x    | y    |
|------|------|
| 45.6 | 79.6 |
| 74.1 | 49.0 |
| 51.8 | 80.3 |
| 50.4 | 67.9 |
| 49.2 | 83.3 |
| 55.9 | 66.4 |
| 42.1 | 92.7 |
| 39.5 | 84.3 |
| 96.3 | 28.6 |

#### Instructions

1. **Correlation Coefficient:** Calculate the correlation coefficient (\( r \)) and report it to three decimal places.
   \[
   r = \_\_\_\_\_
   \]

2. **Proportion of Variation Explained ( \( R^2 \)):** Calculate the proportion of the variation in \( y \) that can be explained by the variation in the values of \( x \). Report the answer as a percentage accurate to one decimal place.
   \[
   R^2 = \_\_\_\_\_ \%
   \]

### Detailed Explanation of Concepts

1. **Correlation Coefficient ( \( r \) ):**
   - The correlation coefficient measures the strength and direction of the linear relationship between two quantitative variables.
   - It ranges from -1 to 1, where:
     - 1 indicates a perfect positive linear relationship,
     - -1 indicates a perfect negative linear relationship,
     - 0 indicates no linear relationship.

2. **Coefficient of Determination ( \( R^2 \) ):**
   - The \( R^2 \) value, also known as the coefficient of determination, represents the proportion of the variance in the dependent variable (\( y \)) that can be explained by the independent variable (\( x \)).
   - It is calculated as the square of the correlation coefficient (\( r^2 \)) and expressed as a percentage.
   - For example, an \( R^2 \) value of 0.64 (or 64%) indicates that 64% of the variance in \( y \) can be explained by the variance in \( x \).

Complete the calculations to find the values of \( r \) and \( R^2 \) as per the instructions provided
Transcribed Image Text:### Bivariate Data Analysis #### Data Set We have a bivariate data set represented in the table below with variables \( x \) and \( y \). | x | y | |------|------| | 45.6 | 79.6 | | 74.1 | 49.0 | | 51.8 | 80.3 | | 50.4 | 67.9 | | 49.2 | 83.3 | | 55.9 | 66.4 | | 42.1 | 92.7 | | 39.5 | 84.3 | | 96.3 | 28.6 | #### Instructions 1. **Correlation Coefficient:** Calculate the correlation coefficient (\( r \)) and report it to three decimal places. \[ r = \_\_\_\_\_ \] 2. **Proportion of Variation Explained ( \( R^2 \)):** Calculate the proportion of the variation in \( y \) that can be explained by the variation in the values of \( x \). Report the answer as a percentage accurate to one decimal place. \[ R^2 = \_\_\_\_\_ \% \] ### Detailed Explanation of Concepts 1. **Correlation Coefficient ( \( r \) ):** - The correlation coefficient measures the strength and direction of the linear relationship between two quantitative variables. - It ranges from -1 to 1, where: - 1 indicates a perfect positive linear relationship, - -1 indicates a perfect negative linear relationship, - 0 indicates no linear relationship. 2. **Coefficient of Determination ( \( R^2 \) ):** - The \( R^2 \) value, also known as the coefficient of determination, represents the proportion of the variance in the dependent variable (\( y \)) that can be explained by the independent variable (\( x \)). - It is calculated as the square of the correlation coefficient (\( r^2 \)) and expressed as a percentage. - For example, an \( R^2 \) value of 0.64 (or 64%) indicates that 64% of the variance in \( y \) can be explained by the variance in \( x \). Complete the calculations to find the values of \( r \) and \( R^2 \) as per the instructions provided
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