Popular game shows offer educators the opportunity to develop active-learning exercises that provide students with a real-world connection to analytical reasoning and methods. We describe a classroom assignment developed for quantitative business courses based on the Monty Hall Problem (MHP), a probability puzzle with ties to the long-running…

Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more accessible to applied statisticians and, in turn, have potentially transformed Bayesian education at the undergraduate level. This article provides an…

We present a pedagogic approach aimed at modelling electric conduction in semiconductors in order to describe and explain some macroscopic properties, such as the characteristic behaviour of resistance as a function of temperature. A simple model of the band structure is adopted for the generation of electron-hole pairs as well as for the carrier…

This paper describes a computational exercise at undergraduate level that demonstrates the employment of Monte Carlo simulation to study the conformational statistics of flexible polymer chains, and to predict solution properties. Three simple chain models, including excluded volume interactions, have been implemented in a public-domain computer…

X-ray fluorescence is a non-destructive technique that allows elemental composition analysis. In this paper we describe a prescription to obtain the elemental composition of homogeneous coins, like 50 cent Euro coins, and how to get the quantitative proportions of each element with the help of Monte Carlo simulation. Undergraduate students can…

This article describes several methods from discrete mathematics used to simulate and solve an interesting problem occurring at a holiday gift exchange. What is the probability that two people will select each other's names in a random drawing, and how does this result vary with the total number of participants? (Contains 5 figures.)

Presents a Monte Carlo simulation on probability using a telephone directory as a pseudorandom-number generator. Claims that Monte Carlo simulations offer a way to teach probability concretely and with understanding and that students enjoy the probability experiments. (ASK)

Described are analytical applications of the theory of random processes, in particular solutions obtained by using statistical procedures known as Monte Carlo techniques. Supercomputer simulations, sampling, integration, ensemble, annealing, and explicit simulation are discussed. (CW)

Demonstrated is the use of BASIC computer programs to simulate a binomial experiment and test a simple statistical hypothesis. The theoretical results are reached with the third programing attempt. All results, as well as computer programs, are included. (JJK)

Discusses making a computer-simulated rainbow using principles of physics, such as reflection and refraction. Provides BASIC program for the simulation. Appends a program illustrating the effects of dispersion of the colors. (YP)

Describes Newton's method to locate roots of an equation using the Newton-Raphson iteration formula. Develops an adaptive method overcoming limitations of the iteration method. Provides the algorithm and computer program of the adaptive Newton-Raphson method. (YP)

Presents sports-oriented examples (cricket and football) in which Monte Carlo methods are used on microcomputers to teach probability concepts. Both examples include computer programs (with listings) which utilize the microcomputer's random number generator. Instructional strategies, with further challenges to help students understand the role of…

Discusses the application of probabilistic ideas, especially Monte Carlo simulation, to calculus. Describes some applications using the Monte Carlo method: Riemann sums; maximizing and minimizing a function; mean value theorems; and testing conjectures. (YP)

The Monte Carlo procedure of generating random points that lie within the unit square is used to approximate pi, as the ratio of points within the first quadrant of the unit circle to the total number of randomly generated points. A BASIC computer program for this method is included. (JJK)

Describes the deterministic simulation (a given input always leads to the same output) and probabilistic simulation (new states are subject to predefined laws of chance). Provides examples of the application of the two simulations with mathematical expressions and PASCAL program. Lists seven references. (YP)