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Using the information from Units 1, 2, and 3, Big D Incorporated will be examining how multivariate techniques can serve the organization best and how they can be applied to its new client, the outdoor sporting goods customer. The Board of Directors has asked you to research and explain 3 major ways in which multivariate statistics are utilized in this scenario. In this case, be sure to justify your decision.

Research using the library and the Internet to find an example of how a real company has used each of the following multivariate techniques: 

  • Factor analysis 
  • Multidimensional scaling 
  • Cluster analysis

This can be considered a benchmark if you can justify how it could benefit Big D Incorporated.

Write a summary to upper management explaining the following: 

  • How can each multivariate technique be utilized in Big D Incorporated, and what purpose would each serve? 
  • Which technique is your preferred method, and how is your chosen multivariate technique different from the other two techniques? 
  • What will the Board of Directors learn from your selected technique and more importantly, how will it contribute to the overall decision-making process? Ensure that your explanation is clear and concise.

Business Analyst

Tenika J Tassin

Applied Managerial Decision-Making

Colorado Technical University

Dr. W. Cousar


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.


The Distinction between Nominal and Ordinal Data

Nominal Data Ordinal Data
Comprises of groupings that cannot be ranked Consists of ordered categories
Categories offered cannot be arranged in a particular order. Ordinal values are used to express discrete and ordered units of measurement
Does not work with any kind of data Its 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 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.


Qualitative Attributes of Outdoor Sporting Goods

Trust / Confidence


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.


Ordinal Attributes: 5-Point Rating Scale

Subject Highly Dissatisfied Dissatisfied Neutral Satisfied Highly Satisfied
Hunting 1
Biking 3
Target Shooting 4
Skating 2
Fishing 5

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 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.


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


Ratio Data Interval 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.


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 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.


Distinction between Population and Sample

Basis of Comparison Population Sample
Definition It is a collection of occurrences, objects, and people from which one can draw conclusions about them. a subset of a larger group
Represents All members of a group Some of the elements of the group
Attribute Parameter Statistic
Data Collection Complete enumeration or Census Sample survey or Sampling

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.


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 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.



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


Big D Incorporated Market Analysis Report

Tenika Tassin

Colorado Technical University



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.


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 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.




Means of Transport

Family Status

Bachelor Degree 44.19%

Graduate Degree 33.99%

Not/Latino 94.6%

White 87.6%

39.5% drove alone

Married couple families 82.93%


Highschool 28.6%


Not/Latino 87.5%

White 75.1%

Some college, no degree 21.05%

Means of Transport

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.

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.


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


$75+ $75 – 99.999 $100+ $100-124.999 $125 – 149.999 $150+ 1.714 1.2909999999999999 2.2069999999999999 1.9910000000000001 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 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).


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 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)


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


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.


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


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.


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




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.

Chi Square 1

Chi Square 5

Big D- Chi Square

Tenika Tassin


Dr. W. Cousar

Colorado Technical University


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 ?2-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 ?2

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


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 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.


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 Statistics36(3), 1885-1900.