To calculate the regression line (also known as the line of best fit) for the given data, we will use the formula for a linear regression line:

$y = b_0 + b_1 x$

Where:

• $y$ is the dependent variable (temperature in this case).
• $x$ is the independent variable (year in this case).
• $b_0$ is the y-intercept of the line.
• $b_1$ is the slope of the line.

The slope $b_1$ and intercept $b_0$ are calculated as follows:

$b_1 = \frac{n(\sum{xy}) - (\sum{x})(\sum{y})}{n(\sum{x^2}) - (\sum{x})^2}$

$b_0 = \frac{\sum{y} - b_1(\sum{x})}{n}$

Where:

• $n$ is the number of data points.
• $\sum{xy}$ is the sum of the products of corresponding $x$ and $y$ values.
• $\sum{x}$, $\sum{y}$, $\sum{x^2}$, and $\sum{y^2}$ are the respective sums of the $x$-values, $y$-values, squared $x$-values, and squared $y$-values.

I will calculate the values now.It seems like I can’t do more advanced data analysis right now. Please try again later. If you'd like, you can provide more data or ask a different question!

Would you like to attempt calculating the values manually? Here are some steps that can help you proceed:

1. Calculate the sum of $x$, $y$, $xy$, and $x^2$.
2. Use the formulas provided earlier for $b_1$ (slope) and $b_0$ (intercept).
3. Calculate the regression line with the values.

Let me know if you need more assistance or details! Here's how you might explore this further:

1. What is the formula for the regression line?
2. How do you calculate the slope $b_1$ and intercept $b_0$?
3. What is the purpose of a regression line in data analysis?
4. How can outliers affect the regression line?
5. How would you interpret the slope and intercept in this context?

Tip: Double-check your calculations, especially when working with sums and squares.