MAB_P7
This is a realworld project that involves analyzing and evaluating a business of your choice that is in your local area. By completing this project, you will demonstrate what you have learned in this course by analyzing a business.
To complete this project, select a local business of your choice. Examples include, but are not limited to, a movie theater, stateoperated toll booth, supermarket, fast food restaurant, car wash, or a retailer like TJ Maxx, HomeGoods, or Best Buy.
Imagine you have just been hired as the new manager. As a good manager, you want to have a solid understanding of the business operations processes so you can determine if the business is operating efficiently, timely, and at a profit.
You are to go observe your business and view it from a datagathering and quantitative analysis approach. For example, if you choose a car wash, how many cars entered the wash? What times did they arrive? What type of wash did they get? (You can ask the manager if you can record data). What type of car was it? Was there correlation in the wash type and car? You have to think critically about this scenario. Remember, you are the new manager, so you want to make an impact and improve processes.
As you can see, data are gathered, recorded, and then analyzed to determine the findings (what do the data tell you?). A car wash may use the data to hire more people during certain times, to refill soap in the machine during down times, or even raise the price on certain washes for more revenue. You have to think critically and creatively when you observe your business.
After you have completed all of the quantitative findings on the processes, you are to write a paper that analyzes your selected business. At a minimum, you should accomplish the following tasks.
· Describe the business and how quantitative analysis can be used to make it more efficient.
· Explain the quantitative processes you used to analyze the business.
· Determine if the business exhibits any type of distribution? What type? Explain your findings.
· Outline the decisionmaking steps with regard to your analysis.
· Is there correlation or causation in your findings? Explain.
· Examine the coefficient of determination and the coefficient of correlation, and deduce their meanings. In your response to this, explain the four values of the correlation coefficient.
· Summarize your data findings from the business you selected.
· Display any computations you used (probability, distributions, decision trees).
Your completed project must be at least four pages in length. It should include an introduction section where you include what you will prove regarding the quantitative analysis tools you used, the main points of your paper, and a conclusion section that includes a summary of what the data display about your selected business and how it could improve.
You must use at least two academic resources in your paper, one of which must come from the CSU Online Library. Adhere to APA Style when constructing this assignment, including intext citations and references for all sources that are used. Please note that no abstract is needed.
LDR 5301, Methods of Analysis for Business Operations 1
Upon completion of this unit, students should be able to:
1. Differentiate the steps of the quantitative analysis approach.
1.1 Explain how quantitative analysis can be used to make a business more efficient.
2. Distinguish between the approaches to determining probability.
2.1 Use probability approaches to analyze a business.
3. Contrast the major differences between the normal distribution and the exponential and Poisson
distributions.
3.1 Determine the type of distribution exhibited by a business.
4. Explain the major steps in decisionmaking.
4.1 Apply the steps of decisionmaking to a business analysis.
7. Assess the differences between correlation and causation.
7.1 Explain the four values of the correlation coefficient.
7.2 Examine the coefficient of determination and the coefficient of correlation, and deduce their
meanings.
Course/Unit
Learning Outcomes
Learning Activity
1.1 Unit VII Project
2.1 Unit VII Project
3.1 Unit VII Project
4.1 Unit VII Project
7.1
Chapter 4
Unit VII Project
7.2
Unit Lesson
Chapter 4
Unit VII Project
Chapter 4: Regression Models
Unit Lesson
Introduction to Regression Analysis
What is regression analysis? According to Render et al. (2018), regression analysis serves two purposes: it
helps to explain the relationship between variables, and it can help predict the value of one variable in relation
to the other. Therefore, what we have here is a comparison between variables to see if one reacts positively
to the other, has no impact on the other, or has a negative impact on the other. Here is an example that might
occur in the real world.
UNIT VII STUDY GUIDE
Regression Models
LDR 5301, Methods of Analysis for Business Operations 2
UNIT x STUDY GUIDE
Title
a. A company, Black Swan, has created a new weatherproof coat that is lightweight and adapts to
the ambient temperature to either cool you or warm you.
b. The company must take on a massive advertising campaign to now grow sales worldwide.
c. A regression model (see Figure 1) can be used to compare sales to the costs of advertising.
Notice that Figure 1 displays regression lines comparing marketing to sales. As you can see, there are many
forms the regression can take (linear, concave, Sshaped, and convex). The data drive the curve.
Regression Analysis Definitions
Let’s take a minute to review some definitions that will assist you as you read this lesson, the chapter
readings, and watch the videos for this unit. The textbook offers five different definitions: coefficient of
correlation, coefficient of determination, dependent variable, independent variable, and scatter plot diagrams.
Each of the regression analysis terms being defined measures data in a different way that will help the user
determine if there is a relationship between the data variables. The coefficient of correlation measures the
strength of the relationship between the variables. The relationship can be positive, negative, or neutral as
long as the variables are in line with the linear relationship. The results are determined from your calculations
where r=1, r=1, r=0, and 0
Figure 1: Regression Models
LDR 5301, Methods of Analysis for Business Operations 3
UNIT x STUDY GUIDE
Title
Value Relationship Interpretation of the Value—
What does it all mean?
0 No linear relationship
+1 Perfect positive linear relationship As one variable increases, so
does the other in an exact linear
rule
1 Perfect negative linear
relationship
As one variable increases, the
other decreases in an exact linear
rule
Between 0 and 0.3 Weak positive (or negative) linear
relationship
Shaky linear rule
Between 0.3 and 0.7
(0.3 and 0.3)
Moderate positive (or negative)
linear relationship
Fuzzyfirm linear rule
Between 0.7 and 1.0
(0.7 and 1.0)
Strong positive (or negative)
linear relationship
Firm linear rule
(Ratner, 2009)
LDR 5301, Methods of Analysis for Business Operations 4
UNIT x STUDY GUIDE
Title
Regression analysis begins with the relationship. This begins by gathering and plotting data along two axis.
The plotting of the data is used in a scatter diagram. This is where the independent variable is plotted on the
horizontal access, and the dependent variable on the vertical axis (Render et al., 2018).
Figure 2: Scatter Plot Diagrams
(Render et al., 2018)
LDR 5301, Methods of Analysis for Business Operations 5
UNIT x STUDY GUIDE
Title
Figure 3 provides an excellent example of
this. Note the independent variable payroll on
the horizontal axis and the dependent
variable on the vertical axis. So what does
this tell you? At first, it communicates that as
the payroll increases, sales should also
increase. However, there is not a perfect
relationship, because not all the data lie on
the trend line. There is a relationship because
both are increasing from the lower left to the
upper right, but it is not 100% correlated
(matching). What this indicates to the chief
executive officer (CEO) of the company is that
there is a percentage of error involved in
trying to predict sales.
Scatter Plots and Correlation
Take a few minutes to watch the following segments from the full video cited below: Understanding
Correlation (Segment 9 of 17), Correlations and Scatter PlotsWeather (Segment 10 of 17), and Correlation
CoefficientsBeach Attendance (Segment 11 of 17).
Discovery Education (Producer). (2007). Discovering math: advanced—statistics and data analysis: part 1
[Video]. Films on Demand.
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aylists.aspx?wID=273866&xtid=117914
The transcript for this video can be found by clicking on “Transcript” in the gray bar to the right of the video in
the Films on Demand database.
The video segments provide a good review of correlation with super examples of beach attendance and
weather. What to determine from this is whether there is correlation—a degree of association between
variables. In the first segment, there is the comparison between shark attacks in Florida and the number of
tourists, measured in millions. The second segment discusses the number of beachgoers to the average
temperature X. What one must consider is coincidence. Is it by chance? Does an increase in tourists mean
that more sharks arrive? Maybe not. Correlation does not indicate causation. You are encouraged to continue
watching the video if this interests you. As the segments continue, you will see how two other variables are
compared (math scores on exams to average temperature). As you would expect, there was no correlation
displayed.
When comparing two variables we also use the correlation coefficient, which is the following formula: 1< r >
1. This compares the data and determines which way the line is sloped. If the r=45, then the line is positively
sloped upward from left to right. If the r = 13, then it is negatively sloped from left to right, and the same for r
= 0: no slope. The closer r = 1, then the stronger the correlation and points along the line.
Simple Linear Regression
On page 113, Render et al. (2018) provide the simple linear regression formula, as seen below:
Y = β0+β1X+ϵY=β0+β1X+ϵ(41)
where
Y = dependent variable (response variable)
Figure 3: Scatter diagram
(Render et al, 2018)
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LDR 5301, Methods of Analysis for Business Operations 6
UNIT x STUDY GUIDE
Title
X = independent variable (predictor variable or explanatory variable)
β0 = intercept (value of Y when X = 0)
β1 = slope of regression lineϵ=random error
Render et al. (2018) provide a very good example of simple linear regression in their Triple A Construction
example on page 114 of the textbook. In this scenario, Triple A Construction is trying to predict sales in the
future. What, therefore, is the dependent variable? Sales is the dependent variable. The independent variable
is the Albany payroll. Take your time, walk through the problem given the data chart in Table 4.2, and apply
the formulas. The end result of 9.5 or $950K displays that if the payroll is $600 million the following year,
predicted sales would be $950K.
Assumptions of the Regression Model
As we have seen in decisionmaking, the decisionmaker might have some assumptions on what will occur,
therefore, leading to an assignment of probability. With regard to assumptions in the regression model,
Render et al. (2018) determined that four are important: the errors
• are independent,
• are distributed normally (recall from previous unit what a normal distribution looks like),
• have a mean of zero, and
• have a constant variance no matter the value of X.
Now, let’s look at it in the following figures that reinforce these assumptions. Look at Figure 4. Based on what
you have read regarding correlation and regression in this lesson, what do you see? What you see is a
pattern of randomness, right? There are random plots of data and there is a large area of error to the center
regression line.
Look at Figure 5. What do you see? Focus your sight line moving from the error axis on the left to the end of
the regression line on the right. What you should notice is that the error rate and breadth of the scattered
points gets farther apart as X (horizontal axis) increases. An example of this was demonstrated in a previous
lesson. The closer you walk toward a sound (radio, jackhammer, honking horn alarm), the louder the sound
gets. The farther you walk away from the sound, the lower the sound is.
Figure 4: Pattern of Errors Indicating Randomness
(Render et al., 2018)
LDR 5301, Methods of Analysis for Business Operations 7
UNIT x STUDY GUIDE
Title
Finally, examine Figure 6. What do you see? In simple terms and observation, it looks like the dots present a
hill. The hill begins lower on the left, rises as it moves with higher error rate and higher X value, but then it
reverses course and comes back down on the right side of the graph to where X increases in value but the
opposite error rate is decreasing. This data indicates that the data is not linear (straight line).
Conclusion
In this unit, we examined regression. As a review, there are two purposes of regression analysis— to explain
the relationship between variables and to help predict the value of one variable in relation to the other
(Render et al., 2018).
Figure 5: Nonconstant Error Variance
(Render et al., 2018)
Figure 6: Errors Indicate Relationship is Not Linear
(Render et al., 2018)
LDR 5301, Methods of Analysis for Business Operations 8
UNIT x STUDY GUIDE
Title
Why is regression important, and what can you do with regression analysis? The takeaway from this unit is
being able to determine if there is a relationship between two variables. There are many examples in the real
world. For example, an inverse relationship exists between interest rates and investments. When interest
rates rise, stock and bond prices fall (and their yields increase). When interest rates are reduced, then stock
and bond prices rise (and their yield falls). Think about the biotech industry and pharmacy industry with drug
development to fight diseases. There is a lot of trial and error when finding a cure for a virus or disease.
Researchers want to see if Vaccine X reduces Infection A. Hence, they are looking for a correlation of
reduction in the disease spreading. If Vaccine X has no impact on Infection A, then researches know there is
no correlation, and therefore must go back to the laboratory to modify Vaccine X and retry it. Can you think of
any regression models that you have seen in society, your place of employment, or at home? Think about this
as we move through the unit.
References
Ratner, B. (2009, May 18). The correlation coefficient: Its values range between +1/1, or do they? Journal of
Targeting, Measurement and Analysis for Marketing, 17, 139–142
https://link.springer.com/article/10.1057/jt.2009.5
Render, B., Stair, R. M., Jr., Hanna, M. E., & Hale, T. S. (2018). Quantitative analysis for management (13th
ed.). Pearson. https://online.vitalsource.com/#/books/9780134518558
In order to access the following resources, click the links below.
The Chapter 4 PowerPoint Presentation will summarize and reinforce the information from this chapter in your
textbook. You can also view a PDF of the Chapter 4 presentation.
The following video segments were referenced in the unit lesson and provide some great realworld examples
of correlation. Take a few minutes to watch these segments from the full video cited below: Understanding
Correlation (Segment 9 of 17), Correlations and Scatter PlotsWeather (Segment 10 of 17), and Correlation
CoefficientsBeach Attendance (Segment 11 of 17).
Discovery Education (Producer). (2007). Discovering math: advanced—statistics and data analysis: part 1
[Video]. Films on Demand.
https://libraryresources.columbiasouthern.edu/login?auth=CAS&url=https://fod.infobase.com/PortalPl
aylists.aspx?wID=273866&xtid=117914
The transcript for this video can be found by clicking on “Transcript” in the gray bar to the right of the video in
the Films on Demand database.
Nongraded Learning Activities are provided to aid students in their course of study. You do not have to submit
them. If you have questions, contact your instructor for further guidance and information.
For an overview of the chapter equations, review the Key Equations on page 136 of the textbook.
Then, complete questions 1–12 on the SelfTest on page 139. You can use the key in the back of the book in
Appendix H to check your answers for SelfTests.
Finally, complete Problem: 4–10 on page 140. You can use the answer key in Appendix G in the back of the
textbook in order to check your answers
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Course Learning Outcomes for Unit VII
Required Unit Resources
Unit Lesson
Introduction to Regression Analysis
Regression Analysis Definitions
Scatter Plots and Correlation
Simple Linear Regression
Assumptions of the Regression Model
Conclusion
References
Suggested Unit Resources
Learning Activities (Nongraded)