From the image, we can extract the following details:

• The equation of the regression line is $y = -1.223x + 230.33$.
• The coefficient of determination $R^2 = 0.8941$, which indicates the strength of the fit.

Now, we can address each part:

a. Coefficient of correlation (r):

• The coefficient of correlation $r$ is the square root of $R^2$, but since the slope is negative, $r$ will also be negative. $r = -\sqrt{R^2} = -\sqrt{0.8941} \approx -0.945$ So, rounded to 2 decimal places, the coefficient of correlation $r$ is -0.95.

b. Coefficient of determination (R²):

• The coefficient of determination is already given as $R^2 = 0.8941$. Rounded to 2 decimal places, it is 0.89.

c. Coefficient of non-determination:

• The coefficient of non-determination is $1 - R^2$. $1 - 0.8941 = 0.1059$ Rounded to 2 decimal places, it is 0.11.

Summary:

• a. Coefficient of correlation (r): -0.95
• b. Coefficient of determination (R²): 0.89
• c. Coefficient of non-determination: 0.11

Would you like further details or have any questions?

Here are 5 related questions for deeper exploration:

1. How does the negative correlation affect the interpretation of the data?
2. What does the coefficient of determination tell us about the model?
3. How can we interpret the slope $-1.223$ in this context?
4. What factors could influence the coefficient of non-determination?
5. How would the correlation change if we transformed the data?

Tip: The closer $R^2$ is to 1, the stronger the linear relationship between the two variables.