# 1-1 Discussion

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To reinforce your knowledge built in this area, and to verify that you have grasped the concepts, review discussion questions 1-1, 1-2, 1-3, and 1-4 in the textbook as a check to verify that you are comfortable with the material.

Discussion Question: In Figure 1.1 on page 5 of the textbook, at which point in the decision-modeling approach would you be able to determine if your best choice of solutions would be a probabilistic model or a deterministic model? Explain your answer.

Respond to at least two of your classmates by agreeing or disagreeing with their identification of where in the decision-modeling approach they would determine which model to use. Give details to support your point of view.

Refer to the Module One Discussion Rubric for directions on completing these discussions.

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Discrete vs Continuous Patrick Tully posted Jul 7, 2018 2:23 PM In theory, data can be discrete and continuous. It all depends on what scale is being used. First, it is important to understand the different between discrete and continuous data. Discrete data is data that can be quantified into finite states (Groebner, Shannon, & Fry, 2014). Assume that in a jar, there is an assortment of coins. The jar could have 0 quarters, 1 quarter, 5 quarters, etc. There is no possibility for a jar to have a number of quarters between two whole numbers, thus making the data discrete. Continuous data is something that can have any number of outcomes (Groebner, Shannon, & Fry, 2014). For example, somebody could weigh 150 pounds, 151 pounds, or any number inbetween 150 and 151 pounds (ex. 150.1, 150.10002, etc). Now, imagine a bucket of water. The weight of that water is continuous, just like the weight of a person. However, the amount of water is discrete if you count the number of molecules in the water (“Discrete vs Continuous”, n.d.). Therefore, the water can be measured on a continuous scale and on a discrete scale. References Discrete vs continuous. (n.d.). Retrieved July 7, 2018, from https://www.norsys.com/WebHelp/NETICA/X_CY_discrete_vs_continuous.htm Groebner, D. F., Shannon, P. W., & Fry, P. C. (2014). Business statistics: a decision -making approach (9th ed.). Harlow: Pearson. Discussion One Sean Delaney posted Jul 9, 2018 1:04 PM Subscribe Previous Next This page automatically marks posts as read as you scroll. Adjust automatic marking as read setting Discrete data is data that can be categorized into a classification and is based on counts. Only a finite number of values are possible, and the values cannot be meaningfully subdivided. An example of discrete data is the number of parts damaged in a shipment. The data is counted in whole numbers. For example, in the number of parts damaged in a shipment there would be no such thing as 'half a defect' (ISixSigma, 2018). Continuous data is data that can be measured on a continuum or scale and can have almost any numeric value and can be broken down into smaller increments depending on the precision of the measurement system being used. Continuous data can be recorded at many different points. Examples of continuous data include money, temperature, time and volume. Another example is the size of a marble and to be within specification the marble needs to be at least 25mm but no larger than 27mm. Examples of marbles that meet in the criteria could be 25.12mm or 26.99mm (ISixSigma, 2018). Data from an experiment can be both discrete and continuous; however the data would need to be properly converted. For example, we could use a shipping company and measure the number of late deliveries. This data would be considered discrete as we cannot subdivide into fractions or decimals. Continuous data would be the time per delivery. An example of which could be that a particular delivery took 2 hours, 24 minutes, and 34.22 seconds. References ISixSigma. (2018). Continuous Data. Retrieved July 9, 2018, from https://www.isixsigma.com/dictionary/continuous-data/ ISixSigma.(2018). Discrete Data. Retrieved July 9, 2018, from https://www.isixsigma.com/dictionary/discrete-data/ Step 1: Formulation Formulation is the most challenging step in decision modeling. Formulation is the process by which each aspect of a problem scenario is translated and expressed in terms of a mathematical model. This is perhaps the most important and challenging step in decision modeling because the results of a poorly formulated problem will almost surely be incorrect. It is also in this step that the decision maker’s ability to analyze a problem rationally comes into play. Even the most sophisticated software program will not automatically formulate a problem. The aim in formulation is to ensure that the mathematical model completely Figure 1.1 The Decision Modeling Approach addresses all the issues relevant to the problem at hand. Formulation can be further classified into three parts: (1) defining the problem, (2) developing a model, and (3) acquiring input data. Defining the Problem, the first part in formulation (and in decision modeling) is to develop a clear, concise statement of the problem. This statement gives direction and meaning to all the parts that follow it. Defining the problem can be the most important part of formulation. In many cases, defining the problem is perhaps the most important, and the most difficult, part. It is essential to go beyond just the symptoms of the problem at hand and identify the true causes behind it. One problem may be related to other problems, and solving a problem without regard to its related problems may make the situation worse. Thus, it is important to analyze how the solution to one problem affects other problems or the decision-making environment in general. Experience has shown that poor problem definition is a major reason for failure of management science groups to serve their organizations well. When a problem is difficult to quantify, it may be necessary to develop specific, measurable objectives. For example, say a problem is defined as inadequate health care delivery in a hospital. The objectives might be to increase the number of beds, reduce the average number of days a patient spends in the hospital, increase the physician-to-patient ratio, and so on. When objectives are used, however, the real problem should be kept in mind. It is important to avoid obtaining specific and measurable objectives that may not solve the real problem.
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Two discussions

Discrete vs Continuous
Patrick Tully posted Jul 7, 2018 2:23 PM
In theory, data can be discrete and continuous. It all depends on what scale is being used.
First, it is important to understand the different between discrete and continuous data.
Discrete data is data that can be quantified into finite states (Groebner, Shannon, & Fry,
2014). Assume that in a jar, there is an assortment of coins. The jar could have 0 quarters, 1
quarter, 5 quarters, etc. There is no possibility for a jar to have a number of quarters between
two whole numbers, thus making the data discrete.
Continuous data is something that can have any number of outcomes (Groebner, Shannon, &
Fry, 2014). For example, somebody could weigh 150 pounds, 151 pounds, or any number inbetween 150 and 151 pounds (ex. 150.1, 150.10002, etc).
Now, imagine ...

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