ITM 107 Lecture Notes - Lecture 1: Natural Number, Empty Set, Null Set

If all the members of the set can be listed, the set is said to be a finite set. 2, 3, 4} and b = {x, y, z} are examples of finite sets: for an infinit

ITM 107 Lecture Notes - Lecture 2: Function Composition, Abscissa And Ordinate

If x is an element in the domain of a function f, then the element in b that f associates with x is written f (x(cid:895) (cid:894)read (cid:862)f of x

ITM 107 Lecture Notes - Lecture 3: Commutative Property, Natural Number, Plasma Display

Matrices: matrices are classified in terms of the numbers of rows and columns they have. M has three rows and four columns, so we say this is a 3 4 (ci

ITM 107 Lecture Notes - Lecture 4: Elementary Matrix, Augmented Matrix, Coefficient Matrix

Each row of the matrix gives the corresponding coefficients of an equation. In the first two matrices, the circled element must be changed to obtain a 

ITM 107 Lecture Notes - Lecture 7: Lincoln Near-Earth Asteroid Research, Solution Set, Cappuccino

ITM 107 Lecture Notes - Lecture 8: Sensitivity Analysis, Feasible Region, Shadow Price

Graphical sensitivity analysis: we can use the graph of an lp to see what happens when, an ofc changes, or, a rhs changes, recall the flair furniture p

ITM 107 Lecture Notes - Lecture 9: Quadratic Formula, Xterm, Social Security Trust Fund

ITM 107 Lecture Notes - Lecture 10: Exponential Growth, Special Functions, Exponential Decay

ITM 107 Lecture Notes - Lecture 12: Sample Space, Fair Coin, Empirical Probability

Lecture 9 introduction to probability: when we toss coin, it can land in one of two ways, heads or tails. Each sample point in the sample space is assi

ITM 107 Lecture Notes - Lecture 13: Sample Space, Conditional Probability, Product Rule

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The course emphasizes how to apply various mathematical techniques in the support of managerial decisions in the various functional areas of business. It is subdivided into three major parts: part one covers the study of linear phenomena, including linear functions, matrices, systems of linear equations, linear inequalities, and linear programming;  part two covers non-linear phenomena and focuses mainly on quadratic, exponential, and logarithmic functions;  and part three examines probability concepts including uncertainties, conditional probability, and Bayes' formula.

Managerial Decision Making

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