Enter your answer in the form
y
=
m
x
+
b
, with
m
and
b
both rounded to two decimal places.    x	y
0	3.16
1	4.72
2	4.5
3	6.6
4	7.72
5	7.39
6	11.11
7	10.57
8	11.06

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.

Enter your answer in the form y = mx + b, with m and b both rounded to two decimal places. x y 0 3.16 1 4.72 2 4.5 3 6.6 4 7.72 5 7.39 6 11.11 7 10.57 8 11.06

Learn how to calculate the best-fit line for a set of data points using linear regression. In this case, the equation y = 1.05x + 3.21 represents the relationship between x and y. Ideal for high school students studying linear regression and data analysis.

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