Need a MAYLAB code for this problem

2) One of the most popular courses at the University of Hard Knocks is Underwater Basket Weaving 1400. After many years of collecting data from this course, it has been found that the grade distribution can be modeled with the probability density function, f(x)- -(x-80) 50 2 so that the integral, thatthengral, Jsc)d, will compute the probability that a andom student wil x compute the probability that a random student will score between a % and b % for the course average, including the interval endpoints. For example, if the integral evaluated to 0.56, then 56 out of every 100 students would expect a grade from a % to b %. (You may notice that this probability density function represents a classic Gaussian "bell" curve, but that the antiderivative of this function is not known!) Suppose this integral is used to compute the probability that a random student will pass the course, i.e., earn a score from 70 to 100 a) First, write a function m-file to approximate any definite integral with the Composite Trapezoid Rule on m panels. The function should accept any number of panels m, any integration bounds, a and b, and any function,func, as input function int trapfun (m,a,b,func). You may find it helpful to refer to the addendum on p. 18 of the MATLAB tutorial posted in cLcarning Then, run trapfun to approximate the specific definite integral used to compute the probability requested above. Use ,n = 100 panels, and output the approximation. How many students out of 100 can expect a passing grade? (Round to two significant digits.) b) Now, write a function m-file to approximate any definite integral with the Composite Simpson's Rule on m panels. The function should accept any number of panels , any integration bounds, a and b, and any function, fune, as input function intsimpfun (m,a,b, func). Then, run simpfun to approximate the specific definite integral used to compute the probability requested above. Use m-50 panels, and output the approximation. Confirm that your result is similar to that of part (a)