Problem Summary Prob. # Concepts Covered Level of Difficulty Notes 1.1 The management science process 1 1.2 When to use simplistic vs. complex models 4 1.3 Building a simple mathematical model 2 1.4 Writing constraints and an objective function – solving for an optimal solution. 4 1.5 Writing and objection function and a constraint for a production problem 3 1.6 Model shells, linear and integer models 5 1.7 Different objectives for the same data 3 1.8 Identifying controllable and uncontrollable inputs 3 1.9 Building a model shell 6 1.10 Substituting raw data into a model shell to build a complete model 5 1.11-1.25 Spreadsheet functions: SUM, SUMPRODUCT, MAX, IF, SUMIF, absolute cell references, F4 key, dragging, formula writing 3 1.26-1.29 Spreadsheet functions: normal probabilities, NORMDIST, NORMINV 3 1.30-1.38 Spreadsheet Data Analysis functions: DESCRIPTIVE STATISTICS, REGRESSION Hypothesis testing (p-values using TDIST) and confidence interval generation 4 1.39-1.40 Spreadsheet functions: RAND(), VLOOKUP 3 1.41 Difference between a parameter and a decision variable 2 1.42 Use of spreadsheets in management science 2 1.43 Optimization and Prediction Models 4 1.44 Input data for profit maximization 4 1.45 Potential management science studies 4 1.46 Construction of a prediction model 5 1.47 Different models for the same problem 5 1.48 Evaluation of computer output; what-if analyses 4 1.49 Writing constraints in a more readable format 2 1.50 Development and solution of a one-variable nonlinear model; supply and demand 5 Case 1.1 Reading and analyzing computer output, writing a business memo 4