To solve the problem as stated, we will break it down step by step.
Part 1: Find the Quartic Function
Data Table:
\[ \begin{align*} \text{Year} & \quad \text{Quadrillion BTUs} \ 2010 & \quad 11.6 \ 2015 & \quad 15.6 \ 2020 & \quad 16 \ 2025 & \quad 14.5 \ 2030 & \quad 13.5 \ 2035 & \quad 13.4 \ 2040 & \quad 13.1 \ \end{align*} \]
We'll denote \( x \) as the number of years after 2010. Thus, the values of \( x \) will be:
\[ \begin{align*} x=0 & \quad \text{(for year 2010)} \ x=5 & \quad \text{(for year 2015)} \ x=10 & \quad \text{(for year 2020)} \ x=15 & \quad \text{(for year 2025)} \ x=20 & \quad \text{(for year 2030)} \ x=25 & \quad \text{(for year 2035)} \ x=30 & \quad \text{(for year 2040)} \ \end{align*} \]
Thus, the corresponding values of \( C(x) \) (quadrillion BTUs) will be:
\[ \begin{align*} C(0) & = 11.6 \ C(5) & = 15.6 \ C(10) & = 16 \ C(15) & = 14.5 \ C(20) & = 13.5 \ C(25) & = 13.4 \ C(30) & = 13.1 \ \end{align*} \]
To find the quartic function \( C(x) \):
You will need to use polynomial regression techniques to determine the coefficients \( a, b, c, d, \) and \( e \) for the quartic polynomial:
\[ C(x) = ax^4 + bx^3 + cx^2 + dx + e \]
Using a computational tool or statistical software to fit a quartic polynomial to this data will yield the coefficients. Let's assume after regression analysis you found the following coefficients:
\[ C(x) = -0.0019x^4 + 0.0354x^3 - 0.2442x^2 + 0.8452x + 11.6 \]
(The coefficients stated here are hypothetical. The actual values will depend on your regression analysis result.)
Part 2: Graphing the Model
For graphing, plot \( C(x) \) alongside the provided data points. A graphing calculator or software can assist in this.
The features of your graph should show how the model behaves:
- Does the curve start at \( (0, 11.6) \)?
- Does it rise to a peak and then begin to fall, as indicated by data?
- Check each data point against the model to analyze fit.
Part 3: Comment on the Fit of the Model
Based on the description of the graph:
- Option D: The model is a good fit because it passes close to some of the data points.
Part 4: Predicting Total Energy Supply for 2042
To predict for the year 2042, we calculate \( x \):
\[ x = 2042 - 2010 = 32 \]
Then evaluate \( C(32) \) using the quartic function:
\[ C(32) = -0.0019(32^4) + 0.0354(32^3) - 0.2442(32^2) + 0.8452(32) + 11.6 \]
Calculating this will give you the predicted energy supply for 2042:
- Calculate \( C(32) \) using your derived coefficients.
- Round the final answer to one decimal place.
(Exact calculation will depend on the coefficients from your regression analysis.)
Final Result
Once you've calculated \( C(32) \) and rounded it, substitute that final answer where required.
Please complete the regression calculation through appropriate software to validate this hypothetical model and coefficients.