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To find the equation of the line $y = mx + b$ that best fits the given data, we can perform a linear regression analysis. Here’s how it’s done:

1. We calculate the slope $m$ and intercept $b$ using the least-squares method.
2. The slope $m$ and intercept $b$ are rounded to two decimal places as per the instructions.

Let's calculate these values.The equation of the line that best fits the given data is:

$y = 1.05x + 3.21$

Here, $m = 1.05$ and $b = 3.21$, both rounded to two decimal places.

Would you like more details on the calculation or have any questions?

Here are some related questions for further exploration:

1. How is the least-squares method used in linear regression?
2. What is the significance of the slope and intercept in a linear model?
3. How can we measure the goodness of fit for a regression line?
4. What are some applications of linear regression in real-world data analysis?
5. How would this regression change if there were more outliers in the data?

Tip: Linear regression can also be extended to multiple variables (multivariable linear regression) when there is more than one predictor.