This document discusses correlation, regression, and cluster analysis as statistical methods for forecasting. Correlation quantifies the relationship between two variables from -1 to 1. Regression constructs an equation describing the relationship between variables to forecast scenarios. Cluster analysis groups objects with similar characteristics into clusters to categorize large amounts of data. The document recommends using cluster analysis to analyze consumer survey responses that have interdependent factors. It provides examples of how companies have used cluster analysis for market segmentation, product categorization, and anomaly detection.

1. Correlation and Regression
Forecasting is a critical job for managers. Correlation and
regression are two statistical methods used by managers for
forecasting.
Correlation allows you to quantify how closely two variables
are related. The correlation values or correlation coefficients
have a range between -1.0 and +1.0. The closer the value is to
the absolute value of 1, the stronger the correlation. The
negative or positive sign indicates if the variables have a
negative or positive correlation. A positive correlation exists
when both variables increase or decrease. A negative correlation
exists when one variable increases while the other variable
decreases. If the two variables are independent and have no
relationship, then the correlation is 0.
Be careful not to confuse correlation and causality. For
instance, you can be reasonably sure that higher distribution and
lower prices both cause higher sales; however, there are many
things in this world that are correlated mathematically but are
not at all related.
Regression is a statistical technique that lets you construct an
equation to describe the relationship between the movements of
two variables. On a scatter plot, the regression equation would
calculate the best-fit line through the points. Regression allows
you to forecast and simulate different scenarios by ascertaining
the relationship between causes and effects. The causes are
known as independent variables or drivers. The effects are
known as dependent variables or what is being forecast.
You need to have a sufficient amount of history for the
dependent variable and all the independent variables that you
might think are useful in predicting the dependent variable to
build a regression model. The minimum number of observations
required is generally between 20 and 30. A key concept for
regression is that it uses the past to predict the future. It
assumes that relationships between historical dependent and

2. independent variables will hold true for present of future
dependent and independent variables.
There is an extension to the regression model, known as the
multiple regression model. Adding another independent variable
to a regression model turns it into a multiple regression model.
The equation can become quite complex when more than two
independent variables are added to the model, but these
equations are rarely calculated by hand. Most commercial
spreadsheet, accounting, and statistics software include these in
their function library.
Counselor Dispositional Expectations
Dispositions are the values, commitments, and professional
ethics that influence behaviors toward others, and, if sincerely
held, dispositions lead to actions and patterns of professional
conduct. The Grand Canyon University Counseling Program’s
dispositions adhere to the University’s mission statement, as
well as to the established counseling profession codes of ethics.
The Grand Canyon University Counseling Program have
adopted the following dispositions for its students derived from
the American Counseling Association (ACA) Code of Ethics.
Although these dispositions are not all inclusive, they do
represent values and qualities that are warranted by counseling
students. Students who fail to adhere to or demonstrate such
dispositions may be subject to disciplinary actions.
· Psychological Fitness: Counselors* are aware and assess their
motives for pursuing the counseling profession. They are aware
of their unfinished emotional and/or mental health issues, and
resolve them before starting to provide counseling services to
others. Counselors engage in self-care and seek resolutions to
issues that arise during their practice. Counselors adhere to the
American Counseling Association (ACA) Code of Ethics and/or

3. the NAADAC, the Association for Addiction Professionals Code
of Ethics.
· Self-Awareness: Counselors are aware of their personal moral,
ethical, and value systems and provide counseling services with
objectivity, justice, fidelity, veracity, and benevolence.
Counselors are acutely aware of their personal limitations in
providing services, and are willing to refer clients to another
provider when necessary.
· Cultural Diversity: Counselors respect, engage, honor, and
embrace diversity and a multicultural approach that supports the
worth, dignity, potential, and uniqueness of people within their
social and cultural context. Counselors promote self-advocacy
and assist clients in advocating for empowerment within their
cultural context.
· Acceptance: Counselors foster a healthy climate of change by
providing and promoting acceptance, and a nonjudgmental
environment during the therapeutic process. They understand
their personal value system and do not impose their values,
attitudes, beliefs, and behaviors on their clients.
· Empathy: Counselors foster understanding, compassion, and
avoid any actions that can cause harm to a client. Counselors
treat others with dignity and respect.
· Genuineness: Counselors deal truthfully with themselves and
their clients, in order to avoid harming their clients.
· Flexibility: Counselors practice a client-centered approach,
and align treatment to the client’s goals for therapy.
· Patience: Counselors understand the therapeutic process and
respect client’s efforts to gain control over their lives.
Counselors encourage an environment that promotes self-
empowerment and allows client’s voice in the therapeutic
process.
· Amiability: Counselors do not support or engage in any act of
discrimination against a prospective, current, or former client.
Counselors promote and practice social justice and do not
exploit others in their professional relationships.
· Professional Identity: Counselors adhere to regulatory state

4. boards and nationally recognized codes of ethics. Counselors
practice only within their scope and competencies. They seek to
utilize best practices and empirically supported treatments.
Counselors stay current with the counseling profession through
seeking continuing education, and by supporting counseling
associations.
* The term counselor is used to refer to counselors in training at
the graduate level.
American Counseling Association (2014). ACA Code of Ethics.
Alexandria, VA: Author.
Walz, G. R., & Bleuer, J. C. (2010). Counselor dispositions: An
added dimension for admission decisions. Vistas Online
publication, 1, 11-11.
© 2015. Grand Canyon University. All Rights Reserved.
Topic 1: Counselor Disposition Worksheet
Directions: Review the counselor disposition document located
in the weekly materials. After reviewing the dispositions,
complete the table below and total your ranking score at the
bottom of the table.
Part A: Use only a sentence (or two) to discuss each of the
following. APA formatting is not required.
Counselor Disposition
Ranking: 1-10 (10 being the strongest) How strong are you in
this area?
How is the disposition demonstrated in your behaviors or
thoughts? Provide an example.
How do you know it is an area for growth and how you can
become stronger in this area?
What areas have you made progress on since being in the

5. program? How will you continue to make progress?
Why is this disposition important in the process of assessment,
diagnosis, and treatment?
Psychological Fitness
Self-Awareness
Cultural Diversity
Acceptance
Empathy
Genuineness

6. Flexibility
Patience
Amiability
Professional Identity
Total Score
[Enter your score here]
Part B: Click here and pull up the Multicultural Awareness,
Knowledge, and Skills Survey (MAKSS).Complete the MAKSS
survey and write a brief one paragraph summary (75 to 150
words) of what you learned about yourself from taking the
survey.
MAKSS Survey Reflection:

7. © 2016. Grand Canyon University. All Rights Reserved.
Colorado Technical University
Applied Managerial Decision – making
MGMT600-1701-CS33-01
Scott, Charlotte
Individual 04 By: Akram Shebani
Colorado Springs, Colorado
Introduction
A lot of businessmen use multivariate techniques for analyzing
the responses of the customers by differentiating them into
clusters of information or meaningful categories. This is done to
study the relationship between the various responses by the
consumers and the product or service being studied. Talking
about consumer responses, these can be classified into three
different categories of dependent, interdependent and
independent (Jobson, 2012). Independent responses of the
consumer are not affected or swayed by the various variables or
conditions, such as the age or gender of the consumer, which is
not going to change due to the various characteristics or
conditions of the product or service. A dependent response by a
consumer is always swayed by an independent response. For
instance, a consumer’s preference for a particular video game

8. will be dependent on his age or gender. An interdependent
response on the other hand is that in which one particular factor
or groups of factors do not characterize the response as a
dependent or independent one. For instance, a person’s
preference for computers is a multilayered one which is not
linked to an isolated factor like age, gender etc. Thus, such a
response can be termed as an interdependent response
(Gnanadesikan, 2011). The technique using multivariate factors
that analyze the data collected from market survey depends
upon the relationship between these factors.
Choice of Multivariate Technique: Cluster Analysis
There are three most commonly used techniques for the analysis
of interdependent responses, which are factor analysis, cluster
analysis, and multidimensional scaling technique. Independent
responses can be used as components in every group while
making the analysis. In this report, the choice of multivariate
technique is cluster analysis. Cluster analysis is the act of
segregating a large population into smaller groups of mutually
exclusive people based on their common characteristics
(Chatfied and Collins, 2013).
Clustering, in simple words, can be defined as the task of
categorizing or grouping a certain set of objects that share a
similar sense than those objects in other groups. It is the
fundamental task when mining for exploratory data. One of the
most common techniques when doing statistical analysis of
data, it is used in many fields like pattern recognition,
information retrieval, machine learning, image analysis, data
compression, bioinformatics and computer graphics (Anderberg,
2014).
The technique of clustering has been applied to a wide range of
research problems. A summary of the various studies that report
the use of the clustering technique have been provided. For
instance, when it comes to the field of medicine, clustering the
various diseases along with their symptoms and cures can result
in useful taxonomies (Murtagh and Heck, 2012). In the field of
psychiatry, successful therapy can be achieved with the correct

9. diagnosis from a cluster of symptoms like paranoia,
schizophrenia etc. archeology applies the cluster techniques to
establish various taxonomies for funeral objects, stone tools etc.
In layman’s terms, cluster technique is useful when a mountain
of varied information needs to be categorized into meaningful
piles of information.
Difference between Cluster Analysis and other Multivariate
Techniques
Factor analysis:
When there are a number of interdependent survey responses
from consumers that need to be correlated and analyzed, the
data collected must be reduced to a group of smaller sets of
three or five factors each. As an example of this, when a group
of women is being analyzed, their characteristics in reference to
their weight, height, interests, hobbies and activities should be
studied. If factor analysis is used to study the above
characteristics of women, the set of factors would be reduce to
two variables of size and lifestyle where size would include a
combination of weight and height and lifestyle would use a
combination of interests, hobbies and activities (Abdi and
Williams, 2010).
Wal-Mart used factor analysis to acknowledge a series of issues
they had faced back in 2006 after which they bounced back as
the leaders in competitive market nation-wide for their low
price product services. Some of the issues faced by Wal-Mart
back in 2006 were exporting the jobs and being responsible for
destroying small businesses; tax evasion and internal fraud on
top level ; being held responsible for class action discrimination
suit and litigation; being held responsible for cheap business
practices such as cheap health care benefits to employees and
low pay scale.
Multidimensional Scaling:
With the help of techniques like multidimensional scaling that
records and maps the responses of consumers on a graph as per
their similarities, different responses by different consumers
can be analyzed and evaluated based on their propinquity to

10. various other responses (Young, 2013). This multivariate
technique has been used by Sam’s club to help them determine
the neurological preferences and choices of their logo
candidates. This helped them select and determine simple steps
in order to save their green logo campaign which could identify
the various products in their stores that can be termed as
environmentally-friendly by basing the decision on the process
through which they were created or manufactured, processed
and distributed across stores.
In business to business markets, there is little difference found
between suppliers and products. To understand the truth behind
this one can simply apply the technique of multidimensional
scaling. For instance, when a person has five different chemical
companies to differentiate between, they can create a
questionnaire aimed at the buyers of chemicals made by those
companies (Borg et al., 2012). Here each of those five
companies would have to be pitted against each other and the
respondents would have to answer on scale as to how similar
they are on a variety f aspects. How alike their quality of
products is? What difference there is in their technical service?
How alike their prices are? What is the difference in their
quality of product? Once these questions are answered on a
scale, a statistical program is used to map these various
companies and the factors are tested to understand the
similarities and differences between them. These maps are very
useful when analyzing the brand to determine if one needs to
work on the image of the brand to make it more distinctive in
the market.
Real company that used Cluster Analysis:
Cluster analysis was used by SAB miller which is a leading
international brewery for beer and also a producer of products
for Coca Cola. Cluster analysis helped SAB miller to create
classification for all their liquids in order to create easy flow of
communication for both technical staff as well as marketing
personnel. It also helped the consumers to understand the exact
differences between the different beers produced in the brewery

11. which in turn helped the company to understand and determine
the preferences of the consumers (Mooi and Sarstedt, 2010).
Cluster analysis also helped the company to identify and
acknowledge the various opportunities for their brand in the
market along with being able to determine the acceptability of
their particular brand in the international and local markets.
Companies ideally use clustering techniques to segment the
products, customers and store. The above report talks in detail
about customer segmentation by using the cluster technique. In
the same manner products of a company can also be categorized
into different clusters or groups based on their brand, flavor,
use, size etc. even stores with similar characteristics based on
customer sales, similar sales, size etc can be grouped together
(Duran and Odell, 2013). The clustering technique is also very
good for detecting any kind of anomaly like identification of
theft or fraud transactions. This can be done by creating a
cluster or group of good transactions. Whenever a transaction
falls out of that group for whatever reason it creates an anomaly
and thus can be termed as a suspect which is then detected to
see where the transaction went wrong. This can help identify
certain fraud transactions. This clustering approach is highly
useful in the field of medicine where one can detect any
abnormal cells in tissue samples (Anderberg, 2014). It can also
be used to detect various calling patterns in telecommunication
companies to identify fraudulent behavior. The clustering
technique is usually used to break down a complex set of varied
information into amenable piles of data that are more
acquiescent to other techniques. For instance, logistic
regression outcomes can be better performed in small clusters
that follow different distributions and behave differently.
Cluster Analysis used for Market Segmentation in my
Organization
There are various ways to divide and segment a market but one
of the most effective and precise ways to segregate a market in
a statistically valid manner would be to use the cluster analysis
technique. The tool of cluster analysis is used in various

12. disciplines and not just the marketing field (Wedel and
Kamakura, 2012). Basically, any place where there is lots of
data to be segregated and condensed into smaller pools of
information or clusters are called clustering of data, what
marketing segments mean in the field of marketing. When one
tries to group the consumer data based on their needs,
behaviors, attitudes etc into related sets, it is called the market
segmentation process. To create this segmentation the cluster
technique is used to review and create such market segments.
In my organization i.e. retail store for apparels and footwear,
market segmentation is very important. It is important for me to
understand who my customers are and where their loyalty lies.
While I used the basic demographic, behavioral and
psychographic segmentation before, now I believe that with
cluster analysis I will be able to segment the market on the
basis of customer satisfaction and loyalty. This will help me
focus on more specific customers and accordingly help in make
proper marketing strategies and gain more customers (Tuma et
al., 2011). The following is how I would use cluster analysis for
market segmentation in my company.
Customer base is segregated in the market based on two major
variables, which are customer satisfaction and loyalty metric
(Cleveland et al., 2011). Assuming we have a customer
satisfaction (CSAT) score ranging from 1 to 9 where 1 means
very dissatisfied to 9 which means highly satisfied. Similar
scores for customer loyalty are 1, which means high switcher-
low loyalty, and 9 which means low switcher-high loyalty.
The above graph shows the customer database as a scatter-plot
graph where the red squares represent the various scores of the
individual customers and the red circle in the middle represents
the average score of all the customers for CSAT (Muller and
Hamm, 2014). When you look closely at the above diagram one
can notice an innate pattern that identifies three inherent market
segments as shown in the chart below.

13. The above chart clearly shows that there are three segments of
clusters for consumers as suggested by the data collected. The
black circle on the top right corner represents the set of
customers who are highly loyal towards the company and its
products and services. These customers are loyal with a high
level of customer satisfaction. The blue circle in the graph on
the bottom left corner appears to represent the customers who
are least loyal towards the company’s products and services
(Pritchard and Howard, 2015). These customers are less loyal
and have a lower level of customer satisfaction. This sows a
relationship between the customers and managers, which are
fairly obvious and expected where marketing programs, are
built around this particular customer satisfaction correlation.
When you observe the red circle on the top left corner of the
graph you will find that this area represent customers that are
highly dissatisfied yet they are loyal towards the products and
services of the company (Dolnicar et al., 2014). This is a highly
interesting find and mostly unexpected. This is why it is very
important to look at the various approaches of the market
segments.
Conclusion
To conclude, there are many multivariate techniques however,
clustering is a great way to study the various patterns and
structures in the database so as to understand their scope of
application in business analytics. There are many techniques
and different ways to perform the clustering of data. As an
analyst one should have proper knowledge and expertise as to
how to perform clustering along with having an insight into the
various clustering algorithms. The analyst should be able to
apply the technique that is most relevant as per the needs of the
business. Cluster analysis is best when used for segmenting the
market and hence I selected this tool for my organization.
References
Abdi, H. and Williams, L.J., 2010. Principal component
analysis. Wiley interdisciplinary reviews: computational
statistics, 2(4), pp.433-459.

14. Anderberg, M.R., 2014. Cluster analysis for applications:
probability and mathematical statistics: a series of monographs
and textbooks (Vol. 19). Academic press.
Borg, I., Groenen, P.J. and Mair, P., 2012. Applied
multidimensional scaling. Springer Science & Business Media.
Chatfied, C. and Collins, A.J., 2013. Introduction to
multivariate analysis. Springer.
Duran, B.S. and Odell, P.L., 2013. Cluster analysis: a
survey (Vol. 100). Springer Science & Business Media.
Cleveland, M., Papadopoulos, N. and Laroche, M., 2011.
Identity, demographics, and consumer behaviors: International
market segmentation across product categories. International
Marketing Review, 28(3), pp.244-266.
Dolnicar, S., Grün, B., Leisch, F. and Schmidt, K., 2014.
Required sample sizes for data-driven market segmentation
analyses in tourism. Journal of Travel Research, 53(3), pp.296-
306.
Gnanadesikan, R., 2011. Methods for statistical data analysis of
multivariate observations (Vol. 321). John Wiley & Sons.
Jobson, J.D., 2012. Applied multivariate data analysis: volume
II: Categorical and Multivariate Methods. Springer Science &
Business Media.
Mooi, E. and Sarstedt, M., 2010. Cluster analysis. In A concise
guide to market research (pp. 237-284). Springer Berlin
Heidelberg.
Müller, H. and Hamm, U., 2014. Stability of market
segmentation with cluster analysis–A methodological
approach. Food Quality and Preference, 34, pp.70-78.
Murtagh, F. and Heck, A., 2012. Multivariate data
analysis (Vol. 131). Springer Science & Business Media.
Pritchard, M.P. and Howard, D.R., 2015. Measuring loyalty in
travel services: A multi-dimensional approach. In Proceedings
of the 1993 World Marketing Congress (pp. 120-124). Springer,
Cham.
Tuma, M.N., Decker, R. and Scholz, S., 2011. A survey of the
challenges and pitfalls of cluster analysis application in market

15. segmentation. International Journal of Market Research, 53(3).
Wedel, M. and Kamakura, W.A., 2012. Market segmentation:
Conceptual and methodological foundations (Vol. 8). Springer
Science & Business Media.
Young, F.W., 2013. Multidimensional scaling: History, theory,
and applications. Psychology Press.
Running head: STATISTICAL HYPOTHESIS TESTING 1
STATISTICAL HYPOTHESIS TESTING 7
Applications and Limitations of Statistical Hypothesis Testing
Tawanna Green
Applied Managerial Decision-Making MGMT600-1803B-03
Charlotte Scott
September 5, 2018
Executive Summary
Based on the prior decisions to expand, Big D needs to consider
all aspects that will contribute to its growth. It is therefore
important to conduct the qualitative nonparametric test to
demonstrate the position of the company in its expansion
strategy. The needs to the company to expand is for
diversification. The company decided to expand sits business to
Chicago due to the existing opportunities. The previous report
concluded that sport teams produce a higher percentage of
outdoor sporting goods purchases because there is an array of
sports teams in Chicago. We have observed that there is an

16. opportunity for growth. Various factors will have an impact on
the expansion strategy. This include the education rate, the
population, ethnicity and also gender. The per capital income,
labor force and employment rate are also significant factors in
the expansion strategy. Opportunities identified in the new
market include high employment, high consumption, high
purchasing power and product awareness. The current goal of
the company is to survive amid the high competition the
company will face in new markets. The company also expects to
innovate and change its culture in order to chive the business
objectives. To make effective decisions, Big D needs to use the
chi-square test to get useful statistical evidence. Big D also
needs to use the qualitative nonparametric tests to understand
its position in regards to the expansion strategy
Qualitative non-parametric test
It is important to first understand the nonparametric tests.
Basically they are the tests that needs little or no assumption in
regards to population distribution. It is useful in the cases
where the dependent variable is nominal or ordinal, when there
is an issue of normality to variance consistency and if the
dependent variable distribution is partial (Mertler, 2016). The
test is therefore important in making decisions and helping in
analyses that use the median. The test is very accurate even if
the used sample size is small.
To use the nonparametric test, we should execute various steps.
Since they need little to no assumption of population
parameters, we will not make any assumptions about the
standard deviation or the mean of the population. The null
hypothesis is equal. The first steep will be to establish a
hypothesis and determine the level of significance (Mertler,
2016). For example, Big D wants to expand into the new market
for their outdoor sporting goods. This will be successful
because its brand is the best in the market. The alternative
hypothesis will be equal to what we seek to prove. The null
hypothesis will conflict the alternative hypothesis. The level of
significance will be the likelihood of an incorrect decisions. It

17. will involve the risk or likelihood of rejected.
Alternative hypothesis
Big D’s outdoor sporting goods are the best in the market.
Null Hypothesis
Big D’s outdoor sporting goods are not the best in the market.
The second step will be to establish the test statistic. This will
involve obtaining the average number of the outdoor sporting
goods sold in Chicago’s new market and use the average to
determine the amount that should be sold. The forth step will be
to establish the decision rule. This will include the statement
that provides when to reject the hypothesis. The third step will
be to compute the test statistic and analyze it and then compare
it to the decision rule. Based on this comparisons, the null
hypothesis will be accepted to reject. This means we shall draw
conclusions from the findings.
Limitations of the Non parametric tests
Non parametric tests also have their limitation. When
observations are ranked or nominal, measured imprecisely or
subject to outliers, they become the perfect test to use. This is
because in this instance the data will be difficult to analyze
using the parametric methods without major assumptions
regarding their distributions. However, the non-parametric
method will lack power compared to many approaches of data
analysis. This is especially when there is a small sample size. It
is also common when the assumptions needed of the normality
of data used is essential. The non-parametric tests are important
in hypothesis testing and not in estimation (Naghettini, 2016).
This makes them the best for our case. However, it is also
possible to get estimates from the parametric tests and their
confidence level but this is usually not straightforward
Another limitation is that the non-parametric test is not
specified but rather determined from data. The name parametric
means that the nature and number of parameters in the test are
not fixed. They make no assumption about the size of the
sample even in the event that the data observed is quantitative.
It is useful of the data does not need ranking of the sorts. It is

18. therefore important for qualitative data. Another limitation is
that they have a low degree of confidence compared to results
from a parametric tests. Therefore they may be less efficient in
some cases.
Chi-Square Distribution tool
To help make a decisions, the chi square distribution tool will
be important. The chi square test is a non-parametric test that is
used under given conditions. The variables needs to be measure
on an ordinal or nominal scale. The data under observation
should violate the normality assumption. It is also appropriate
for equal and unequal sample sizes although some non-
parametric test will only address the equal sample size.
Chi-Square Distribution tool Assumptions
The assumptions for the chi square test include
· Chi-square tests deals with frequencies and counts
· The variables should be mutually exclusives
· Each matter can subsidize data to one cell in the x2
Chi-Square Distribution tool for Big D
The chi square test of independence is the statistical tool that is
mainly used in identifying two related variables (Naghettini,
2017). In regards to categorical and nominal data, the test will
address the association between variabkes. In business, the test
will be of importance in exmaning how the variables under
study are independing from each other or how they are related
(Bozeman Science, 2011).
Chi-Square Hypothesis
Null Hypothesis:
There is no significant difference between outdoor sporting
goods production in US and Chicago.
Alternative Hypothesis:
There is a significant difference between outdoor sporting
goods production in US and Chicago
There is no adequate data to formulate a full Chi-Square for the
outdoor sporting goods client. However, we have sufficient data
to initiate this process. This nclude data such as the per cpaita
income in chicago, the purching power as wll as the population.

19. The analysis utilizes the essental data that may impact the
exmapnsion of the business. In this case, we should use income
of the population and compare to observed sales in the U.S to
those expected in Chicago.
Income category
Expected
Observed
< $10k
76950
62960
$10k- 20k
124385
37128
$20k-30k
215723
50923
$30k-40k
241826
52338
p-value = 0.0000
$40k-50k
233426
43609

20. $50k-60k
221053
36209
$60k-75k
307526
48118
$75k-100k
451640
65959
Based on the collected data. The p-value obtained is 0.0000.
This value is less than 0.05. this means that ther is a strOng
evidence for us to reject the null hypothesis. Therfore, we can
conlude that there is a signficant difference in the number of
sporting goods clients in U.S and number of sporting goods
clients in chicago.
Limitations of the Chi-Square Distribution tool
Like other tools of analysis, the chi-square also has its
limitations. First, the tool requires that all the participants that
are measured in the analysis should be independent. This means
that it is not possible for one to fit 2 or more categories. Only
one category should be fit in. in the event that a participant fits
in two categories, the chi-square analysis becomes
inappropriate.

21. Another limitation us that the data used must be frequency data.
For example, in our case if we are just measuring if the outdoor
sporting goods are on demand, the chi square become
appropriate (Naghettini, 2016). This is because we shall be
calculating the expected clients to purchase the outdoor sporting
goods. Since our research was based on income categories and
there were more than 5 expected clients, the chi square becomes
more appropriate. The chi square also requires a high sample
size which is not less than 50. In our cases, the sample size was
more than 50 making it appropriate.
Conclusion
Big D decide to expand and grow its business in Chicago.
Various factors will impact the outcomes of this decision, this
include the population, per capita income and employment rate
in Chicago. To justify the decision farther, the chi-square
distribution tool was utilized. The initial steps of qualitative
nonparametric tests were also demonstrated. From these
analysis, the board of directors need to expand its outdoor
sporting goods. Chicago has a high per capital income and a
high employment rate.
References
Bozeman Science. (2011, November 13). Chi-squared test
[Video file]. Retrieved from
https://www.youtube.com/watch?v=WXPBoFDqNVk
Mertler, C. A., & Reinhart, R. V. (2016). Advanced and
multivariate statistical methods: Practical application and
interpretation. Routledge.
Naghettini, M. (2017). Statistical hypothesis testing. In
Fundamentals of Statistical Hydrology (pp. 251-309). Springer,
Cham.
Big D Market Expansion Research Findings
Presented by:

23. Findings on Demography
Most people in the targeted market have reached the college
level of education
The death rate of the population in the targeted market is low
while the birth rate is slightly high
Most individuals are aged 30 years and above
The number of women is higher than that of the men

24. A majority of the people in the targeted market have reached the
college level of education. What this implies is that many
people qualify for white collar jobs. The significance of
securing a white collar job is that it promises a steady income.
Research shows that most employees in the formal sector are
employed on a permanent basis. The death rate of the
population in the targeted market is low while the birth rate is
slightly high. This kind of population promises high sales. if the
population had more young people, sales would be low since
most young people are normally in school and so do not have
any source of income.
Many people are aged 30 years and above. What this implies is
that the population is mainly composed of the adult population
which is good. The number of women is higher than that of
men. This is good for the company for the reason that women
have a high tendency to buy new products when they are
presented to them. The main reason for this is that women have
a higher curiosity than men. Unlike men, women are more
willing to taste new products and services. This is especially if
the services or even products satisfy their needs to the fullest.
2
US POPULATION TREND

26. Findings on Occupation
Most people in the targeted market are either, teachers, nurses
or police officers
There are a few accountants and lawyers
The teaching profession has the highest rate of increase in terms
of population
The profession with the lowest rate of increase is that of
accountants.

27. Most people in the targeted market are either, teachers, nurses
or police officers. The good thing about all these populations is
that most individuals are employed on a permanent basis. The
importance of being employed on a permanent basis is that it
helps it helps ensure a permanent flow of income. There are a
few accountants and lawyers. The significant of having this
population here is that it helps increase the amount of money in
circulation. This is because lawyers and accountants are among
the professionals rated as high income earners. What normally
happens is that earning a high income translates to a high
spending ability.
The teaching profession has the highest rate of increase in terms
of population. The main reason for this is that the number of
schools has been increasing. Given the fact that teachers also
earn a high income, this means that the there is a high amount
of money put into circulation by this population. The profession
with the lowest rate of increase is that of accountants. This
population is however projected to increase due to the fact that
there are so many financial firms that want to set up in the
targeted area. This means that our company will be charged less
deposit fees due to many financial institutions.
4
Findings on Employment
The current employment rate stands at 60.5%

28. The rate of employment has been increasing
Most people are employed in the informal sector
The informal sector is projected to experience a continuous
growth

29. The current employment rate stands at 60.5%. What this
percentage means is that many people are employed. The second
implication of this rate of employment is that most people have
better living standards. What normally happens is that people
living at high standards have a higher spending power when
compared to those people that are living in poor living
standards. The rate of employment has been increasing. This
means that the few people who are currently living in poor
standards will soon improve their standards. It further means
that the spending rate of such people will increase. This will
increase the money in circulation.
Most people are employed in the informal sector. This perhaps
is due to the fact that there are so many companies in the
targeted market. As a result of the high number of companies,
the area is able to employ a high number of locals when
compared to the number that the government is able to employ.
The informal sector is projected to experience a continuous
growth. The number of companies in place is expected to
increase due to the government incentives that currently exist.
The government intends to reduce unemployment rate to its
lowest.
5
Unemployment Trend

30. Findings on Income
The per capita income of the targeted population is $90,761 for
men and $50,756 for women annually

31. Men earn at least $7,563 per month while women earn $4,299
While women appear to be earning less, it is projected that the
average income of women will increase
Not all women earn low income; there are women who earn
more than men. It all depends of the type of occupation

32. The per capita income of the targeted population is $90,761 for
men and $50,756 for women annually. When this is calculated
on a monthly basis, Men earn at least $7,563 per month while
women earn $4,299. The fact that women appears to be earning
low in this case does not mean that they are poor spenders. A
demographic research conducted in the targeted area show that
most women are married. What this means is that men invest
part of their money into the family. The impact of adding more
money into the family is that it increases the money that women
have at hand.
Even though women appear to be earning less, it is projected
that the average income of women will increase. Of great
importance however to note in this case is the fact that not all
women earn low income; there are women who earn more than
men. It all depends of the type of occupation. The second factor
that determines the income that an individual in the area
receives is experience. Individuals that are more experienced in
their profession tend to earn more than those that are less
experienced. the main reason for this is that experienced
individuals have extraordinary skills than less experienced
individuals.
7
US DISPOSABLE PERSONAL INCOME

33. Conclusion
It is alright for Big D to implement its expansion strategy

34. Will expanding into new markets make the company experience
high sales?
Will the company achieve high profits as a result of high sales
experience?
Will the company be financially stable as a result of its
expansion?

35. It is alright for Big D to implement its expansion strategy. The
main reason for this is that all factors appear to be suggesting
that the targeted market offers a favorable situation for the
company. Our evaluation for instance has come to establish that
the rate of employment has been increasing and is expected to
continue increasing in the future. What this means is that the
company will continue to experience a steady increase in the
sale of its products. Expanding into new markets will me the
company experience high sales. This is one of the goals that the
company wants to achieve.
The company will realize high profits as a result of high sales
experienced. What normally happens is that high sales translate
to high revenues. The cost of production is then factored in. The
company will be financially stable as a result of its expansion.
What this means is that the company will be in a better position
of expanding should it wish to expand to other markets. The
main reason for this is that money plays a key role when it
comes expansion. A company that has enough or the required
capital is more likely to expand than that which has low capital.
9
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37. 10