To solve this problem, we first need to rescale the data such that the year 2003 corresponds to $x = 0$. This means we will subtract 2003 from each year, making the years relative to 2003.

Here’s the transformation of the years:

• 2003 becomes $x = 0$
• 2004 becomes $x = 1$
• 2005 becomes $x = 2$
• 2006 becomes $x = 3$
• 2007 becomes $x = 4$
• 2008 becomes $x = 5$

Now, let’s construct the new table of values:

$x$ (Year - 2003)	Profits (in millions)
0	17.1
1	16.8
2	10.9
3	17.1
4	17.3
5	18.4

Finding $R^2$

The question asks us to find the $R^2$ value for the quadratic model fitted to this data. While we can't directly calculate the $R^2$ without regression, we are provided options.

Given the pattern of the profits and the fact that quadratic models usually fit data that rises, falls, and rises again (or vice versa), the quadratic model should fit this data relatively well. Among the provided options, $R^2 = 0.98$ suggests a very high fit, which is likely the best choice.

Conclusion:

The $R^2$ value for the quadratic model is 0.98.

Would you like to explore the process of calculating $R^2$ or regression further?

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Here are 5 related questions to expand on this topic:

1. What is the exact process for calculating $R^2$ in regression analysis?
2. How do you determine whether a quadratic model is the best fit for data?
3. How does rescaling data affect the outcome of regression models?
4. What is the significance of an $R^2$ value close to 1?
5. How can we find the equation of the quadratic model based on the given data?

Tip: The closer $R^2$ is to 1, the better the model fits the data. An $R^2$ of 0.98 means the model explains 98% of the variability in the data.