The scatter plot shows a negative correlation between the hours spent watching TV the day before a test and the test scores earned by the students. To determine the equation of the line of best fit, we should identify the general trend of the data points. The line of best fit will have the form:

$y = mx + b$

Where:

• $m$ is the slope (rate of change of test scores with respect to hours watching TV),
• $b$ is the y-intercept (test score when hours watching TV is 0).

Observing the Scatter Plot:

1. The data points indicate that as hours watching TV increase, test scores decrease.
2. For approximate points:
   • When $x = 0$, $y \approx 95$,
   • When $x = 7$, $y \approx 20$.

Slope Calculation:

The slope $m$ is calculated using the formula: $m = \frac{y_2 - y_1}{x_2 - x_1}$

Substitute: $m = \frac{20 - 95}{7 - 0} = \frac{-75}{7} \approx -10.71$

Writing the Equation:

Using the point-slope form $y - y_1 = m(x - x_1)$ and the y-intercept ($b \approx 95$): $y = -10.71x + 95$

This represents the equation for the line of best fit.

Would you like a detailed explanation or have questions about this process?

Here are 5 related questions to extend your learning:

1. How does a line of best fit help interpret relationships in data?
2. What would happen if the correlation was positive instead of negative?
3. How would outliers affect the line of best fit?
4. Can the equation of a line of best fit be used to predict values outside the observed range?
5. What does the slope of $-10.71$ represent in the context of this problem?

Tip: Always check if the data has a clear linear trend before calculating the line of best fit.