Chi Square 1 Chi Square 5 Big D- Chi Square Tenika Tassin MGMT600-2202A-03 Dr. W. Cousar Colorado Technical University 3/20/2022 As presented in the last report, Chicago is an optimal region to make one’s operations and hence the company must prepare a strategy to enter the market of Chicago. Therefore, our argument alternative argument will be to retain the current position as our main argument claims that Bigg D should expand their market. Chi-square test The “chi-square distribution, also referred as the chi-square or χ2-distribution with the Kth degree of freedom is the distribution of the sum of squares of k independent standard normal random variables. The distribution of chi-square is a special case of distribution of gamma and is among the most utilized distribution in probability in statistics” (Rolke, & Gongora, 2021). It is especially important while justifying the hypothesis and in development of confidence intervals. The chi-square test can be utilized for several situations, namely: · The constructs must be measured on a nominal scale or an ordinary scale · The test of “is suitable for groups with equal and unequal sample sizes, however certain non-parametric tests only handle groups with same sizes of the sample”. · The information or statistics which needs to tested must violate the normality assumption. The assumptions for testing the test of are as follows: · The analyzes statistics in terms of “frequencies and counts, rather than percentages or other transformations”. · The groups of the constructs which are being analyzed should be exclusive. · Lastly, each substance might provide data to a single cell in the χ2 The hypothesis testing of : N0- “There is no significant difference between the outdoor sporting production and indoor sporting production frequencies”. H1- “There is a significant difference between the outdoor sporting production and indoor sporting production frequencies”. Table 1 test of Indoor sporting of Goods In-house Production Outdoor sporting of Goods completed Low High Per Capital Income Though all the facts on the corporation's core capabilities cannot be located, the Analysis employs the crucial and necessary features for the improvement and growth of . In order to undertake research on Big D's business expansion. There is a requirement for data collection on the observed and predicted frequencies of components that are participating in the expansion. It considers the salary counts, which is the salary by type of earning, for this scenario. The observed counts are for the United States, whereas the predicted figures are for Chicago in table 2. Table 2 Earnings in the Two Countries The test may be obtained in Excel by using the function Chi-square test. Through the Chi-square test, one can obtain actual range along with the anticipated range. If the p, which is lesser than 0.05, then there will be strong ...

1. Chi Square 1
Chi Square 5
Big D- Chi Square
Tenika Tassin
MGMT600-2202A-03
Dr. W. Cousar
Colorado Technical University
3/20/2022
As presented in the last report, Chicago is an optimal region to
make one’s operations and hence the company must prepare a
strategy to enter the market of Chicago. Therefore, our
argument alternative argument will be to retain the current
position as our main argument claims that Bigg D should
expand their market.
Chi-square test
The “chi-square distribution, also referred as the chi-square or
χ2-distribution with the Kth degree of freedom is the
distribution of the sum of squares of k independent standard
normal random variables. The distribution of chi-square is a
special case of distribution of gamma and is among the most
utilized distribution in probability in statistics” (Rolke, &
Gongora, 2021). It is especially important while justifying the
hypothesis and in development of confidence intervals.
The chi-square test can be utilized for several situations,
namely:
· The constructs must be measured on a nominal scale or an
ordinary scale
· The test of “is suitable for groups with equal and unequal
sample sizes, however certain non-parametric tests only handle

2. groups with same sizes of the sample”.
· The information or statistics which needs to tested must
violate the normality assumption.
The assumptions for testing the test of are as follows:
· The analyzes statistics in terms of “frequencies and counts,
rather than percentages or other transformations”.
· The groups of the constructs which are being analyzed should
be exclusive.
· Lastly, each substance might provide data to a single cell in
the χ2
The hypothesis testing of
:
N0- “There is no significant difference between the outdoor
sporting production and indoor sporting production
frequencies”.
H1- “There is a significant difference between the outdoor
sporting production and indoor sporting production
frequencies”.
Table 1
test of
Indoor sporting of Goods
In-house Production
Outdoor sporting of Goods
completed
Low High
Per Capital Income
Though all the facts on the corporation's core capabilities
cannot be located, the Analysis employs the crucial and
necessary features for the improvement and growth of . In order
to undertake research on Big D's business expansion. There is a
requirement for data collection on the observed and predicted
frequencies of components that are participating in the
expansion. It considers the salary counts, which is the salary by

3. type of earning, for this scenario. The observed counts are for
the United States, whereas the predicted figures are for Chicago
in table 2.
Table 2
Earnings in the Two Countries
The test may be obtained in Excel by using the function Chi-
square test. Through the Chi-square test, one can obtain actual
range along with the anticipated range. If the p, which is l esser
than 0.05, then there will be strong reason for accepting the
original hypothesis i.e., H1. Thus, it can be stated that “there is
a significant difference in the observed and expected counts of
income in the Chicago(expected) and the US (observed)”.
Through chi-square one can get clear understanding on the
variable as it denotes the possible factors which can influence
the product purchase decision of consumers along with other
outcomes. Based on above analysis, board of directors can get
an idea how earnings impact the purchasing decision in
consumers. Since the null hypothesis is rejected, it can be stated
that “there is significant difference between the outdoor
sporting production and indoor sporting production frequencies
and thus it will be profitable for Big D to expand their market
in Chicago”. The chi-square also aids in decision making
process as it determines if there is association between the two
categorical constructs. Henceforth, the process of decision
making becomes easy once the relationship is known about the
variables. For instance, after running chi-square test, it is easier
for Big D company to make decision of expanding market in
Chicago.

4. References
Bozeman, S. (2011). Chi-squared test[Video file]. Retrieved
from https://www.youtube.com/watch?v=WXPBoFDqNVk
Rolke, W., & Gongora, C. G. (2021). A chi-square goodness-of-
fit test for continuous distributions against a known
alternative. Computational Statistics, 36(3), 1885-1900.
Big D Incorporated Market Analysis Report
Tenika Tassin
Colorado Technical University
MGMT600-2202A-03
03/13/2022
Hey everyone and welcome to my presentation. In this
presentation, I will compare and contrast Chicago’s general
summary, census trends, occupation and employment statistics
and Chicago’s Income summary to that of the US. After that, I
will briefly recommend how Big D incorporated can penetrate
into that market and become competitively profitable. Let’s get
started.
1
General Summary: US vs Chicago
Leading Us Trends
Leading Chicago Trends
Understanding the educational backgrounds, race compositions,
means of transport preferred in a region and the status of
families in a potential market is crucial in determining whether

5. to penetrate the market and how to do so in a way that a
business is guaranteed to enjoy success. Using the US data as
the base standard to inform what to expect in Chicago or how to
approach Chicago ensures that the unknown can be measured
against the known thus informing key marketing strategies (Tien
and Ngoc, 2019) The above data for instance allows us to
compare the highest level of education in Chicago against that
of US in general and understand the target audience of our
products better. While in the US the highest share of populants
of 28.6% only has a high school certificate and 21.05% only
have some college education with no degree, Chicago is made
up of 44.19% college graduates and 33.99% of graduate degree
holders. This is impressive because if these values reflect in the
population’s earnings, Big D stands a great chance of
encountering robust growth in this region. One shocking
statistic from this data however is that unlike in the overall US
population where 75.7% of people prefer to drive alone to work,
only 39.5% of people in Chicago drive alone. This value
demands that Big D further investigate the spending patterns of
Chicago residents before penetrating the market.
2
Education
Race
Means of Transport
Family Status
Bachelor Degree 44.19%

6. Graduate Degree 33.99%
Not/Latino 94.6%
White 87.6%
39.5% drove alone
Married couple families 82.93%
Education
Highschool 28.6%
Race
Not/Latino 87.5%
White 75.1%
Some college, no degree 21.05%
Means of Transport

7. Drove alone 75.7%
Family Status
Married couples 75.9%
Census Trends: US vs Chicago
Three interesting findings are reflected on this slide.
Growth Trends of the US and Chicago specifically
Growth Trends in the individual earning in US vs Chicago
Housing Trends in US vs Chicago
1. Growth Trends of the US and Chicago specifically
Let’s take a look at the first graph. All compared trends grew in
both the US and Chicago. The growth of the population in
Chicago matching the overall growth in the US makes Chicago a
potential market worthy of investment especially because of its
promising growth rate. Most impressively, however, was that
these three crucial areas grew even above US values further
demonstrating the attractiveness of the Chicago market
(Blundell et al. 2018).
The median household income
Average household income
Per capita income
In marketing, regions with higher household incomes are
considered more attractive because empirical research proves
that such areas can afford to pay more for the value they
receive. For competitive businesses, these findings make
Chicago highly attractive.

8. 2. Growth Trends in the individual earning in US vs Chicago
Looking at the growth rate of the individual income earnings in
Chicago, It was amazing to see how the growth rate of
individuals earning over $100,000 was higher than those
earning below the figure. Again, these values further emphasize
the attractiveness of this market.
3, Housing Trends in US vs Chicago
That both the number of people owning homes and renting
homes rose in Chicago points to the growing rates of the region
and further the financial status of the residents of Chicago who
are Big D’s target audience. That more people can afford homes
points to the idea that more people are financially capable to
spend more making the region attractive for business.
3
1. Growth Trends: US vs Chicago
US female male total household median h/h inc average h/h
inc per capita income 0.125 0.13900000000000001
0.14699999999999999 0.40400000000000003
0.47299999999999998 0.47599999999999998
Chicago female male total household median h/h inc
average h/h inc per capita income
3.3000000000000002E-2 7.1999999999999995E-2
3.3000000000000002E-2 0.68100000000000005
0.69499999999999995 0.65600000000000003
2. Individual Earnings: US vs Chicago
US
$75+ $75 - 99.999 $100+ $100-124.999 $125 - 149.999
$150+ 1.714 1.2909999999999999
2.2069999999999999 1.9910000000000001

9. 2.4790000000000001 2.339 Chicago
$75+ $75 - 99.999 $100+ $100-124.999 $125 - 149.999
$150+ 0.91700000000000004 0.5
1.1080000000000001 0.60099999999999998
0.68799999999999994 1.627
3. Housing type: US vs Chicago
US Owner Housing Renter Housing
0.26800000000000002 -8.2000000000000003E-2
Chicago Owner Housing Renter Housing 0.183
8.3000000000000004E-2
Occupation and Employment: US vs Chicago
Other Interesting Findings:
Asian population in Chicago grew the most by 62%
Leading Industries in Chicago Growth rates:
Professional scientific and technical services 27.7%
Finance and Insurance 15.2%
Leading occupations in Chicago:
Management occupations except farmers 18.3%
Sales and related occupations 17.4%
Business operations and specialists 8%
Analyzing the occupation and employment data of Chicago is as
vital as understanding the income of the region. Based on the

10. findings of this analysis, Big D can determine areas they will
cut HR costs by working with local talent and areas in which
the brand will be forced to pay heftily to compensate HR that
have to relocate to the region. For instance, given that the
number of residents in labor decreased, there is probably a
higher need for jobs in the area. Understanding the population
that grew the largest is also crucial in informing how to
approach targeting processes. Therefore, this data reveals that
Big D can enjoy working with locals within their management,
sales and business operations departments. Additionally, Big G
can also outsource their scientific, technical, financial and
insurance services to local firms which would further help the
business minimize their costs of setting up and running (Xu et
al. 2021).
4
Occupation: US vs Chicago
US Not in Labor In Labour Employed Unemployed Armed
Forces 1.4E-2 -1.4E-2 0.01 5.0000000000000001E-3 -
1E-3 Chicago Not in Labor In Labour Employed Unemployed
Armed Forces 4.0000 000000000001E-3 -
4.0000000000000001E-3 -4.0000000000000001E-3
4.0000000000000001E-3 0
Income Summary: US vs Chicago
As afore mentioned, in marketing, regions with higher
household incomes are considered more attractive because
empirical research proves that such areas can afford to pay more
for the value they receive. For competitive businesses, these

11. income findings make the Chicago market highly attractive.
Although the average income in Chicago is lower as compared
to the average income earned across America, the high growth
rates of Chicago’s median income and per capita income prove
why Chicago is a great place to invest in. These two statistics
inform Big D that as long as the region continues in its upward
trend on these two areas, the market will not only continue
growing but promises that consumers will remain capable pf
spending more money to access the value they seek in high
quality and competitive products (Blundell et al. 2018)
5
US Average Income Median Income Per Capita Income
56643 42257 21587 Chicago Average Income
Median Income Per Capita Income 14615 69311
64426
Recommendations and Conclusion
Recommendations
Study the competition and their influence
Investigate how the market is segmented
Conduct a thorough industrial analysis:
policies and government regulations
product demand
buyer behavior etc.
Conclusions
Chicago is definitely an optimal region to take one’s operations.
Big D should definitely continue to prepare an entry strategy to
penetrate the Chicago market

12. Recommendations
Beyond analyzing the market also looking at the competition
and their influence is important to ensuring success.
Investigating how the market is segmented is also vital in
determining and predicting expectations and informing
decisions
A thorough industrial analysis will also inform Big D about
other environmental factors that would impact their success in
the region such as policies and government regulations, product
demand, buyer behavior etc.
Conclusions
Chicago is definitely an optimal region to take one’s operations.
Big D should definitely continue to prepare an entry strategy to
penetrate the Chicago market
6
References
Blundell, R., Joyce, R., Keiller, A. N., & Ziliak, J. P. (2018).
Income inequality and the labour market in Britain and the
US. Journal of Public Economics, 162, 48-62.
Tien, N. H., & Ngoc, N. M. (2019). Comparative Analysis of
Advantages and Disadvantages of the Modes of Entrying the
International Market. International journal of advanced research
in engineering and management, 5(7), 29-36.
Xu, K., Hitt, M. A., Brock, D., Pisano, V., & Huang, L. S.
(2021). Country institutional environments and international
strategy: A review and analysis of the research. Journal of
International Management, 27(1), 100811.
Business Analyst
Tenika J Tassin

13. Applied Managerial Decision-Making
Colorado Technical University
Dr. W. Cousar
03/6/2022
Good Evening. My name is Tenika Tassin and I will be your
business analyst for Big D Incorporated. Today I will be
discussing the differences between nominal and ordinal data and
the differences between interval and ratio data. I also will be
giving examples of qualitative attributes of outdoor sporting
goods throughout this presentation.
1
The Distinction between Nominal and Ordinal DataNominal
DataOrdinal DataComprises of groupings that cannot be
rankedConsists of ordered categoriesCategories offered cannot
be arranged in a particular order.Ordinal values are used to
express discrete and ordered units of measurementDoes not
work with any kind of dataIts organized categories allow it to
be linked to any data.Meaningful distinctions can be drawn from
the order in which the values are ranked. The order of the
values indicate a higher rating. Example: categorizing
professional athletes by team. Count the number of participants.
The superiority of one group over the other is not a given.
Examples: Age groups and the frequency with which outdoor
sporting products are consumed.
Nominal data comprises identified groups, with no suggested
hierarchy on the groups. On the other hand, ordinal data
comprises organized groupings, where the variances cannot be
deemed equal. Another distinction is that whereas nominal data
is classified, ordinal data, on the other hand, are in between
discrete and quantitative parameters. Furthermore, nominal data
cannot be allocated to any form of data as it comprises

14. identified groupings, while ordinal data can be linked to any
data as it comprises ordered groups (Stine & Foster, 2018). The
order of the variables of the nominal data has a meaning. For
instance, at the finish of most college and university courses,
students must assess their course work. On the other hand, the
order of the values of ordinal data suggests a higher ranking.
2
Qualitative Attributes of Outdoor Sporting Goods
Trust / Confidence
Satisfaction
Color of athletic products
Texture or Quality
The ordinal qualitative attributes that might be questioned of
the client are their degree of trust in the items and the degree of
satisfaction they derive from the usage of the athletic goods, to
name a few examples. Besides, the nominal attributes that might
be inquired about is the preferred color of athletic products and
texture which can be classified as slicky smooth or abrasive.
3
Ordinal Attributes: 5-Point Rating Scale Subject Highly
DissatisfiedDissatisfiedNeutralSatisfiedHighly
SatisfiedHunting1Biking3Target Shooting4Skating2Fishing5
The five-point rating system that I will use for my ordinal
characteristics is based on satisfaction, with the lowest level of
satisfaction represented as highly dissatisfied, followed by
dissatisfied, neutral, and then satisfied, and finally highly
satisfied as to the highest level of satisfaction. As a result,
customers will be polled to gauge their level of satisfaction with
the new athletic events. Fishing is the most highly rated
activity, followed by target shooting. Those who participated in

15. biking events reported a neutral level of happiness, while those
who participated in skating events reported dissatisfaction.
Finally, the clients were extremely dissatisfied with the hunting
event.
4
Distinction between Ratio and Interval Data
Possible Populations For The Tests
The populations that the researcher will use in this study
includes:
College students,
Single adults,
Teenagers, and
ParentsRatio DataInterval Data Zero point signifies that the
quantity being measurement does not exist.The zero point is
artificially induced.
The interval data type does not have a true and natural zero
point, whereas the ratio data type does, and the zero point
signifies that the quantity being measured does not exist. It's
important to note that a sample is a subset of the population that
serves as a proxy for the complete group. The types of
population that will be studied in this research are college and
university students considering that most are enrolled in
sporting activities in their schools, adults with children who
attend sporting events to bond with their families, teenagers,
and also single adults.
5
Nominal, Ordinal Data, and Quantitative Elements.
Nominal data
It is not ranked at all, but is used for proof of identity purposes.
i.e. 46274825 (SSN No.), 90253, (William Hills , NY).
Ordinal data
It is described in various ways, with variable phases placed in

16. descending order relative to their values. It represents
categories with relevant metrics, such as the Likert Scale;
Highly Satisfied, Dissatisfied, Neutral, Satisfied, and Highly
Satisfied.
It can also be scored by including a ratings system.
Quantitative attributes
A product or service's cost in outdoor sports.
Length of time that an athletic event is scheduled to last
outside.
It is termed nominal data when the observations or values may
be assigned numerical values or codes, and these numerical
values or codes are just used as labels for the observed or
measured quantities. Consider the case of a student
identification number. It is possible to count nominal data but
not measure or arrange it logically. On the other hand, Ordinal
data can be ranked by, for example, assigning a rating scale to
it. It, on the other hand, cannot be measured. In the case of an
outdoor athletic event, one of the quantitative features that can
be measured is the cost of the product or service offered at the
event. For example, what is the cost of taking a boat out and
fishing on the water? Furthermore, another quantitative aspect
that can be examined is the duration of an outdoor sporting
event and the number of people who take part in it.
6
Distinction between Population and SampleBasis of Comparison
PopulationSampleDefinition It is a collection of occurrences,
objects, and people from which one can draw conclusions about
them.a subset of a larger groupRepresentsAll members of a
groupSome of the elements of the groupAttribute
ParameterStatistic Data CollectionComplete enumeration or
CensusSample survey or Sampling

17. A population is a collection of events, items, and people about
which one can make inferences while a sample is a subset of a
population. It focuses on gaining information about the overall
population by selecting a smaller number of individuals cases
from the population (Keller, 2017). While a population includes
all the elements of the group, a sample consists of one or more
unknown character tics of the population. A population is also a
parameter and its data are collected through complete
enumeration or census while a sample is a statistic and is
typically collected through sample survey or sampling.
7
Target Market, and Why Avoid Bias
The target market – refers to all persons about whom the
researcher wishes to collect data.
Single people, college students, and parents
Why avoid bias
Is possibly deceptive.
It leads to erroneous business judgements
It has an impact on the results, the dependability, and the
validity of the findings.
People who are engaged in sports as a form of social interaction
are the primary focus of this study, which includes students,
singles, and parents. Market research is a time and money sink
for businesses. In order to acquire accurate results and retain
the research's integrity, it is critical that the information
gathered during a study be truthful and honest. An unreliable
study may lead to incorrect business decisions and conclusions,
which will ultimately undermine the research's aim. Because of
this, the underlying organization or company may make
unneeded product modifications, target the wrong
demographics, and waste time and money. Bias has a negative
impact on the quality and accuracy of data obtained, and it is

18. therefore critical to avoid it in order to avoid compromising the
importance of a research (Stine & Foster, 2018). Bias also
affects the results, reliability, and validity of the findings thus
affecting business decisions.
8
References
Keller, G. (2017). Statistics for Management and Economics +
XLSTAT Bind-in. Boston: Cengage Learning.
Stine, R. & Foster, D. (2018). Statistics for business : decision
making and analysis. Boston: Pearson
9