To determine the average increase in electricity usage (in kilowatts) per degree increase in temperature, we can use linear regression. The slope of the best-fit line will tell us how much the kilowatt usage increases for each degree increase in temperature.

We can calculate the slope (denoted as $m$) using the formula for linear regression:

$m = \frac{n(\sum xy) - (\sum x)(\sum y)}{n(\sum x^2) - (\sum x)^2}$

Where:

• $n$ is the number of data points.
• $x$ is the temperature.
• $y$ is the kilowatt usage.

Let me calculate that for you.The slope of the best-fit line is approximately $34.858$. This means that for every 1-degree increase in temperature, the kilowatt usage increases by approximately 34.858 kilowatts, on average.

Would you like to see further details or have any questions?

Here are 5 related questions to expand on this information:

1. What would the electricity usage be at 100 degrees Fahrenheit based on this model?
2. How do we interpret the y-intercept of the regression line in this context?
3. How can we check the strength of the linear relationship using correlation?
4. What would happen if we included more data points with extreme temperatures?
5. How does linear regression help in making predictions in other fields?

Tip: When interpreting regression results, always check the fit of the model (e.g., R-squared) to ensure the relationship is strong enough for predictions.