Weekly Assignment #5

Instructions:  integrations in problems 1 and 2 should be evaluated by hand. Some numerical integrations should also be done by hand (whenever n ≤  4); if n>4, you may use technology. Show your work!

1. Average value: true versus approximate

Let f(x)=x((e^(2x))+(e^(3x))).

Define and plot f(x) on the interval [0,1/2].

Demonstrate integration by parts to find the true average value of f(x) on [0,1/2]. Give the name “true” to this average value.

Use the Trapezoidal and Midpoint Rules with n=2 to approximate the average value over the interval [0,1/2] (calls these values “trap” and “mid”). Combine them in an appropriate way to give the Simpson’s Rule (“simp”) approximation, with n=4 (note: this n=4 value (Subscript[S, 4]) uses a weighted average of (Subscript[T, 2])and (Subscript[M, 2])).

Compute the errors (true - approximate) for the Trapezoidal and Midpoint rules. By plotting and bounding the absolute value of the second derivative of f(x), demonstrate that the approximations are within the error bounds for each method. Demonstrate that the errors are of opposite sign, as is typical.

Compute the absolute error (|true - estimate|) for the Simpson’s Rule approximation. By plotting the absolute value of the fourth derivative of f(x) (choose K as big as, or bigger than, the largest value of |(f^((4)))(x)| on this interval), demonstrate that the approximations are within the error bounds for Simpson’s Rule.

2. Area: analytic versus numerical

Let R be the region in the first quadrant of the plane between the curve y=f(x)=(Sqrt[4-(x^2)]) and the x-axis.

Define and plot f(x) on the interval [0,2].

Find the area of R, by computing the appropriate integral using trigonometric substitution.

Estimate the area using (Subscript[S, 10]).

Compute the absolute error in the approximation.

Compute the fourth derivative of f(x), and plot it. Why is it impossible to use the error estimate for Simpson’s rule in this case? (Is it possible to bound the fourth derivative on this interval?)

3. Application

When we die, our bodies become rigid (rigor mortis sets in). Niderkorn’s (1872) observations on 113 bodies provides the main reference database for the development of rigor mortis.

The data: 

(({{hour, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, {proportion in rigor mortis , (2/123), (16/123), (47/123), (61/123), (27/41), (92/123), (33/41), (103/123), (110/123), (37/41), (37/41), (113/123)}}))

bodies={2,14,31,14,20,11,7,4,7,1,0,2};
(* bodies transitioning to rigor mortis at the associated hour: *)hours=Table[i,{i,2,13}];
cumulative=Accumulate[bodies];
data=Table[{hours[[i]],cumulative[[i]]/123},{i,1,12}];
MatrixForm[Transpose[data],TableHeadings->{{"hour","proportion in rigor mortis"},None}]
dataplot= ListPlot[data,PlotRange->{{0,15},{0,1.1}},PlotMarkers->{"x"},
AxesLabel->{"Hour","Number of Bodies"},
PlotLabel->"Number of bodies in rigor mortis by a given hour"]

One can fit a lovely model to this somewhat unlovely data, for the proportion p(t) of bodies in complete rigor mortis after t hours. It is illustrated in the graph below:

The model is (e^(-(22.47/(t^2.216)))): that is,

p[t_]:= (E^(-(22.47/(t^2.216))))

Show[Plot[p[t],{t,0,15}],dataplot]

a. Compute an integral for the average proportion of bodies in rigor mortis in the time interval from 6 to 10 hours after death, based on this model (you may use your calculator or Mathematica to produce your answer).

b. Now compute an approximation using only the data: Simpson’s rule (Subscript[S, 4]) (by hand).

c. How do the estimates compare? Both estimates for the average proportion are approximations; one is based directly on the data ((Subscript[S, 4])), whereas the other is based on a model which was estimated from the data. Do you prefer one method to the other? Why or why not?