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Use a graphing utility to graph the function and find the absolute extrema of the function on the given interval. (Round your answers to three decimal places. If an answer does not exist, enter DNE.) \[ f(x)=x^{4}-2 x^{3}+x+1,[-1,3] \] minima (smaller \( x \)-value) \( \quad(x, y)=(-1.3,3.31 \) minima (larger \( x \)-value) \( \begin{array}{ll}\text { maximum } & (x, y)=(-1.3,1\end{array} \)

Ask by Schwartz Knight. in the United States
Mar 14,2025

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Minimum (smaller \( x \)-value): \( (-1.3, 3.31) \) Minimum (larger \( x \)-value): \( (1.5, 0.812) \) Maximum: \( (3, 31) \)

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Beyond the Answer

To determine the absolute extrema of the function \( f(x) = x^4 - 2x^3 + x + 1 \) on the interval \([-1, 3]\), it’s essential to analyze the critical points and evaluate the function at these points as well as the endpoints of the interval. After using a graphing utility, we find that the critical points occur where the derivative \( f'(x) = 4x^3 - 6x^2 + 1 \) equals zero. Solving this can pinpoint where the function may achieve its minimum and maximum values. Evaluating \( f(x) \) at the critical points and the endpoints reveals that the function reaches its global minimum at approximately \( (x, y) = (-1.3, 3.31) \) and its maximum occurs at the endpoint \( (x, y) = (3, 19) \). So, the smaller \( x \)-value for the minimum is approximately \( (-1.3, 3.31) \), while the larger \( x \)-value for the maximum is \( (3, 19) \). Even though it might seem like math is all about numbers and functions, there's a whole world filled with fascinating concepts behind the equations. Many mathematicians over history have dedicated their lives to uncovering patterns and principles just like these, driving innovations that shape our real world today, from technology to architecture! If you enjoy diving deeper into such mathematical adventures, consider exploring calculus-related books and online courses. Titles like "Calculus Made Easy" by Silvanus P. Thompson simplify complex ideas, and online platforms like Khan Academy offer engaging video tutorials that could provide more insights on optimization problems and derivatives. Happy learning!

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