BUS 231 - Quantitative Methods/Bus Decisions

1. Examine how quantitative analysis is used in business.
   1. Explain the quantitative analysis approach.
   2. Describe the application of quantitative analysis in a real situation.
   3. Identify possible problems in using quantitative analysis.
   4. Illustrate how computers can be used to perform quantitative analysis.
2. Analyze how probability concepts relate to analysis.
   1. Describe the two basic rules of probability and the foundations of probability analysis.
   2. Distinguish between mutually exclusive and collectively exhaustive events.
   3. Distinguish between events that are statistically dependent and those that are statistically independent.
   4. Apply formulas for joint, marginal, and conditional probabilities under both dependent and independent conditions.
   5. Use Bayes’ theorem to establish posterior probabilities.
3. Examine how probability distributions relate to quantitative analysis.
   1. Illustrate through examples both discreet and continuous random variables.
   2. Explain the difference between discreet and continuous probability distributions.
   3. Calculate the expected value and variance of a discreet probability distribution.
   4. Use the binomial table to solve problems.
   5. State the 1, 2, and 3 standard deviation areas under the normal curve.
   6. Use the standard normal probability table.
   7. Express the meaning of Z in the normal distribution.
4. Assess the roles of different families of forecasting techniques and when each should be used.
   1. Compare theories and applications of moving averages, exponential smoothing and trend time-series models.
   2. Adjust seasonal data.
   3. Describe the Delphi-decision making approach.
   4. Analyze data by computer and interpret the outputs.
5. Summarize how businesses apply decision theory to their operations.
   1. List the steps of the decision-making process.
   2. Describe the types of decision-making environments.
   3. Use probability values to make decisions under risk.
   4. Make decisions under uncertainty, where there is risk but probability values are not known.
   5. Use computer to solve basic decision-making problems.
6. Evaluate how useful decision trees and utility theory are to the decision process.
   1. Develop accurate and useful decision trees.
   2. Revise probability estimates using Bayesian analysis.
   3. Explain the importance and use of utility theory in decision making.
   4. Utilize computers to solve more complex decision problems.
7. Investigate the relationship between variables in a regression model.
   1. Develop and interpret simple linear regression models.
   2. Use a multiple regression model to predict.
   3. Use dummy variables to model categorical data.
8. Summarize how inventory control impact organizational operations and profitability.
   1. Explain the importance of inventory control.
   2. Use the economic order quantity (EOQ) to determine how much to order.
   3. Computer the reorder point (ROP) in determining when to order more inventory.
   4. Determine other inventory control quantities, including the optimal number of orders per year and the number of days between orders.
9. Develop strategies which utilize several more complex inventory control models.
   1. Determine the economic order quantity without the instantaneous receipt assumption.
   2. Handle inventory problems that allow quantity discounts or have planned shortages.
   3. Illustrate the use of safety stock with known and unknown stockout costs.
   4. Perform ABC analysis and joint ordering.
10. Relate graphical methods of linear programming to organizational decision making.
    1. Describe the basic assumptions of linear programming and the properties of linear.
    2. Formulate small to moderate sized LP problems.
    3. Solve graphically any LP problem that has only two variables by both the corner point and iso-line methods.
11. Relate how simplex method of linear programming compares with graphical methods.
    1. Convert LP constraints to equalities with slack, surplus, and artificial variables.
    2. Set up and solve both maximization and minimization LP problems with simplex tableaus.
    3. Explain the meaning of every number in a simplex tableau.
12. Contrast a variety of linear programming applications.
    1. Describe major applications areas from marketing and production.
    2. Identify major applications from labor scheduling and finance.
13. Structure several linear programming
    1. Use the northwest corner method and Vogel?s approximation method to find initial solutions to transportation problems.
    2. Apply the stepping-stone and the MDOI methods to find optimal solutions to transportation problems.
    3. Solve facility location and other application problems with the transportation model.
14. Analyze the differences between linear programming and integer programming.
    1. Understand and solve the 3 types of integer programming probems.
    2. Apply the branch and bound method to solve integer programming problems.
    3. Solve graphically a goal programming problem that has only two variables.
15. Assess the importance of queuing theory to an organization?s decision making.
    1. Describe the trade-off curves for cost of waiting time and cost of service provided.
    2. Discuss the three parts of a queuing system ? the calling population, the queue itself and the service facility.
    3. Identify the basic queuing system configurations and assumptions of the common models.
    4. Analyze by computer a variety of operating characteristics of waiting lines.
16. Appraise what role simulation plays in organizational decision making.
    1. Identify the seven steps of conducting a simulation.
    2. Explain advantages and disadvantages of simulation.
    3. Develop random number intervals and use them to generate outcomes.
17. Explain how network models relate to project control.
    1. Describe how to plan, monitor, and control projects with the use of PERT.
    2. Determine earliest and latest starts, earliest and latest finishes, and slack times for each activity as well as overall project time.
    3. Find the shortest path through a network using the shortest-route technique.