8.2 Measures of Central Tendency
In this section, we will study three measures of central tendency: the mean, the median and the mode. Each of these values determines the "center" or middle of a set of data.
Measures of Center
5.4 Simplex method: maximization with problem constraints of the form
The procedures for the simplex method will be illustrated through an example. Be sure to read the textbook to fully understand all the concepts involved.
We will solve
5.2 Linear Programming in two dimensions: a geometric approach
In this section, we will explore applications which utilize the graph of a system of linear inequalities.
A familiar example
We have seen this problem before. An extra condition will b
5.3 Geometric Introduction to the Simplex Method
The geometric method of the previous section is limited in that it is only useful for problems involving two decision variables and cannot be used for applications involving three or more decision vari
5.5 Dual problem: minimization with problem constraints of the form
Associated with each minimization problem with constraints is a maximization problem called the dual problem. The dual problem will be illustrated through an example. Read the text
5.6 Maximization and Minimization with Mixed Problem Constraints
Introduction to the Big M Method
In this section, a generalized version of the simplex method that will solve both maximization and minimization problems with any combination of co
8.3 Measures of Dispersion
In this section, you will study measures of variability of data. In addition to being able to find measures of central tendency for data, it is also necessary to determine how "spread out" the data. Two measures of variab
Boy? Girl? Heads? Tails? Win? Lose? Do any of these sound familiar? When there is the possibility of only two outcomes occuring during any single event, it is c
Properties of Logarithems 1) 2) 3) 4) 5) 6) 7) 8) logb A * B = logb A + logb B logb An = n logb A logb (A/B) = logb A - logb B logb b = 1 logb 1 = 0 logb x = undefined if x 0 loga x logb a = logd a/ logd b
8.5 Normal Distributions
We have seen that the histogram for a binomial distribution with n = 20 trials and p = 0.50 was shaped like a bell if we join the tops of the rectangles with a smooth curve. Real world data, such as IQ scores, weights of in