The linear regression model that best fits the data has the equation:

$s = T(p) = -0.38p + 268$

Where:

• $s$ is the weekly demand in thousands.
• $p$ is the price in dollars.

Additional Results:

• Predicted weekly demand for $p = 205$: The model predicts approximately 190,600 (nearest 100) units.
• Price for a weekly demand of 179,400 units: The price should be set at approximately $235 (nearest $1).

Would you like more details or have any other questions?

Here are 5 related questions you might explore:

1. How does the squared correlation coefficient $r^2$ assess the model fit?
2. Can this model predict demands for prices outside the provided range?
3. How would you interpret the negative slope in this context?
4. Could a quadratic model provide a better fit for this data?
5. What would happen if the price increased to $270 according to the model?

Tip: When using regression models, always check the residuals to ensure the model assumptions hold.