Sign In
Not register? Register Now!
Essay Available:
Pages:
3 pages/≈825 words
Sources:
Check Instructions
Style:
APA
Subject:
Management
Type:
Case Study
Language:
English (U.S.)
Document:
MS Word
Date:
Total cost:
$ 14.58
Topic:

Favorable or Unfavorable Market Unit V Case Study Open

Case Study Instructions:

Unit V Case Study Open
Weight: 12% of course grade
Grading Rubric
Instructions
See Problem 3-17 on page 101 of the textbook. Kenneth Brown is facing three alternatives with two possible outcomes—a favorable or an unfavorable market—for those alternatives. In no less than three pages, describe and justify the decision-making steps Brown may perform in his case. What decision would you make?
Be sure to provide research to support your ideas. Use APA style, and cite and reference your sources to avoid plagiarism.

 

mm4.4 MEASURING THI FIT OP THE REGRESSION MODEL One of the purposes of regression is to understand the relationship among variables.
helps Triple4,4 Measuring the Fit of the Regression ModelOne of the purposes of regression is to understand tne reiauonsmp mo*»» v“*“— -
model tells us that for each $100 million (represented by X) increase in the payroll, we wou expect the sales to increase by $ 125,000 since b{ * 1.25 ($100,000s). This model helps Triple A Construction see how the local economy and company sales are related.A regression equation can be developed for any variables X and Y, even random numbers. We certainly would not have any confidence in the ability of one random number to predict the value of another random number. How do we know that the model is actually helpful in predicting Y based on XI Should we have confidence in this model? Does the model provide better predictions (smaller errors) than simply using the average of the Y values?
In the Triple A Construction example, sales figures (F) varied from a low of 4.5 to a high of 9.5, and the mean was 7. If each sales value is compared with the mean, we see how far they deviate from the mean and we could compute a measure of the total variability in sales. Because Y is sometimes higher and sometimes lower than the mean, there may be both positive and negative deviations. Simply summing these values would be misleading because the negatives would cancel out the positives, making it appear that the numbers are closer to the mean than they actually are. To prevent this problem, we will use the sum of squares total (SST) to measure the total variability in Y:SST = 5l(Y - YfIf we did not use X to predict Y, we would simply use the mean of Y as the prediction, and the SST would measure the accuracy of our predictions. However, a regression line may be used to predict the value of Y, and while there are still errors involved, the sum of these squared errors will be less than the total sumof squares just computed. The sum of squares error (SSE) is
SSEK%3S^#=!2(y- Yf (4-7)
Table 4.3 provides the^Calculations for the Triple A Construction example. The mean ( F == 7) is compared to eaoh value and we get
: SST = 22.5
The prediction ($) for each observation is computed andr compared to the actual value. This results in
SSE = 6.875
The SSE is much lower than the SST. Using the regression Mne has reduced the variability in the sum of squares by 22.5 ~ 6.875 = 15.625. This is called the sum of squares regression (SSR) and indicates how much of the total variability in Y is explained by the regression model. Mathematically, this can be calculated as
SSR = - Yf (4-8)Deviations (errors) may be positive or negative.The SST measures the total variability in Y about the mean.(4-6)The SSE measures the variability in Y about the regression line.TABLE 4.3 Sum of Squares for Triple A Construction X 0 - >)! ' y (V - Yf (Y - Yf6 3 BHB 2 + 1,25(3.) « 5.75 0.0625 1.563$ 4 OO  I  >3  fa  II 2 +' 1.25(4) fjp 7.00 1 ' t ' 0f 6 (9 * 7)2 -= 4 k 2 + 1.25(6) ]S| 9.50 0-25 6.255 4 (5 I If | 4 : 2 + 1.25(4) 7.0Q 4 04.5 2 (4.5 - if = 6.25 2 +. 1.25(2) * 4,50 0 6.259.5 5 (9.5 IBI | 6.25 Igj 1.25(5) f 8.25 1.5625 1.563W 1  H  Hi £(F - ff = 22.5 SST § 22.5 Mg U yf * 6.875 SSE 3 6-875 — yf 33 15.625 SSR ft 15.625

 

TABLE 4.2
Regression Calculations for THple A Constructionas the one with the minimum sum of the squared errors. For this reason, regression analyst| sometimes called least-squares regression. #
Statisticians have developed formulas that we can use to find the equation of a straight M that would minimize the sum of the squared errors. The simple linear regression equation isY=bQ + b\X
The following formulas can be used to compute the intercept and the slope:
average (mean) of X values- 2Y
Y — JW7‘—' average (mean) of Y values
S(jr-I)(r-y)
1 S(x-jf)2 (4|
b0 = Y-biX (4-9.
The preliminary calculations are shown in Table 4.2. There are other “shortcut” formulas that a® helpful when doing the computations on a calculator, and these are presented in Appendix A They will not be shown here, as computer software will be used for most of the other examples in this chapter.
Computing the slope and intercept of the regression equation for the Triple A Constructs* Company example, we have
m 24
6 6 * 46 6 S(X-g)(F- Y) 12.5 2(X-X)J 106o = y - biX = 7 - (1.25)(4) = 2 The estimated regression equation therefore is
y = 2 + 1.25X= 1.25or

Case Study Sample Content Preview:

Favorable or Unfavorable Market
Unit V Case Study Open
Name
Course
Instructor
Date
Favorable or Unfavorable Market
Equipment

Favorable Market ($)

Unfavorable Market ($)

Sub 100

300,000

-200,000

Oiler J

250,000

-100,000

Texan

75,000

-18,000

This is a decision under uncertainty where Ken has to make a decision on purchasing the equipment for brown oil. Purchasing the Sub 100 machine would likely result in the highest profitability compared to the other two options, but in unfavorable outcomes he would incur a loss of $ 200,000 (Render et al., 2018). Thus, the most risky option is purchasing Sub 100, where the possible outcomes are higher returns and higher risks Ken is an optimistic decision maker, and this helps to evaluate his level of risk taking as pessimistic decision makers chose low risk and low returns options, but high risk takers not risk averse. The decision- making steps in this case would involve constructing a decision table and decision tree, then considering decision under different criteria under conditions of uncertainty
The decision n making process begins with problem identification or diagnosis when there is a discrepancy between a desired personal outcome and the expected outcome. In the scenario all the important information has been provided on the three alternatives with two possible outcomes. The second phase in decision making is evaluation of alternatives depending on the possible courses of action, and the factors that affect the alternatives (Ellsberg, 2016). The next phase is the choosing of one of the courses of action based on appropriate criteria and what is most relevant. The last phase of the decision making is verifying if the chosen alternative has solved the problem and if not corrective action taken.
There are different criteria when assessing the outcomes of decision in conditions of uncertainty:
Maximin Criterion: This criterion consists of choosing that strategy that maximizes the worst possible outcome. It would be associated with a pessimistic person and try to provide the highest level of security possible.
Optimistic criteria: according to this criterion, it would be necessary to choose that strategy or course of action that maximizes the best possible payoff. It is also called maximax criterion criteria because it is what an optimistic person would choose. Maximin denote extreme optimism when choosing an alternative decision in the decision making process.
Criterion of Realism (Hurwicz Criterion): This criterion considers that the decision maker ought to order the alternatives according to a weighted average ...
Updated on
Get the Whole Paper!
Not exactly what you need?
Do you need a custom essay? Order right now:
Order

👀 Other Visitors are Viewing These APA Case Study Samples:

HIRE A WRITER FROM $11.95 / PAGE
ORDER WITH 15% DISCOUNT!
Order Now