91Ó°ÊÓ

In mathematical terms, a fourth-order polynomial is expressed as a polynomial of degree four. When you derive a Maclaurin series up to the fourth order, it means you are including terms up to the power of four. This is crucial, as higher-order terms provide more accurate approximations of complex functions. In the given exercise, the objective was to find the fourth-order Maclaurin polynomial of the function \( \ln \left( \frac{1+x}{1-x} \right) \). The Maclaurin series centered at \( x=0 \) uses derivatives evaluated at this point to construct the polynomial:

• First-order term: \( f'(0) \cdot x \)
• Second-order term: \( \frac{f''(0) x^2}{2!} \)
• Third-order term: \( \frac{f'''(0) x^3}{3!} \)
• Fourth-order term: \( \frac{f^{(4)}(0)x^4}{4!} \)

These terms combine to produce the fourth-order polynomial \( P_4(x) = 2x + \frac{2x^3}{3} \). This polynomial provides a simplified expression which estimates the original complex function near \( x = 0 \).

Calculating derivatives is key in forming an accurate Maclaurin series. Each derivative provides different insights into the behavior of the function at \( x = 0 \). Let's break it down step-wise:

• The **first derivative** tells us about the linear change, and here it is \( f'(x) = \frac{2}{1-x^2} \), evaluated as \( f'(0) = 2 \).
• The **second derivative** defines how the function's slope changes, given by \( f''(x) = \frac{4x}{(1-x^2)^2} \), providing \( f''(0) = 0 \).
• The **third derivative** reveals the rate of change of the second derivative, calculated as \( f'''(x) = \frac{4(1 + x^2)}{(1-x^2)^3} \) with \( f'''(0) = 4 \).
• The **fourth derivative** offers insight on the curve of the function’s behavior, \( f^{(4)}(x) = \frac{8x(3+x^2)}{(1-x^2)^4} \), evaluated at \( f^{(4)}(0) = 0 \).

These derivatives are fundamental to building the polynomial terms that mimic the function’s behavior at and near the point of expansion, \( x = 0 \).

Estimating the error when using a Maclaurin polynomial is vital to understanding how closely our polynomial approximates the actual function. This error is expressed by the remainder term \( R_n(x) \). For a fourth-order polynomial, we consider the fifth derivative because the error term \( R_4(x) \) relies on it.The remainder term is given by:\[R_n(x) = \frac{f^{(n+1)}(c)x^{n+1}}{(n+1)!}\]where \( c \) is a value between 0 and \( x \). For the interval \(-0.5 \leq x \leq 0.5\), the challenge lies in evaluating \( f^{(5)}(x) \) directly, which may be complex. Instead, we find a bound:

• Compute or approximate \( \left| f^{(5)}(-0.5) \right| \) and \( \left| f^{(5)}(0.5) \right| \).
• Use these values to determine an upper bound \( K \) for \( \left| f^{(5)}(x) \right| \) over the interval.
• The error is finally bounded by \( \left| R_4(x) \right| \approx \frac{Kx^5}{5!} \).

This analysis ensures that the polynomial provides a reliable approximation within the specified interval.

Mathematical analysis dives into the heart of how functions behave and are approximated. In this exercise, mathematical principles guide us through creating a polynomial that represents the function \( \ln \left( \frac{1+x}{1-x} \right) \) near zero. This involves understanding:

• How derivatives reflect changes in functions.
• How polynomial terms accumulate to approximate the original function.
• How errors are evaluated and bounded to quantify the accuracy of approximation.

In essence, this exercise illustrates the practical use of calculus—combining multiple mathematical tools and concepts to simplify complex functions into something usable and comprehendable.
Through this analysis, students can appreciate the process of breaking down challenging expressions into manageable parts, each representing a distinct layer of function behavior. It exemplifies the importance of calculus derivatives, polynomial representations, and error management, laying a foundation for broader applications in both theoretical and applied mathematics.