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
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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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