Big D Incorporated is nearing completion of its portfolio of recommendations for the outdoor sporting goods company. There are a wide range of measures that could be of value to the Board of Directors to consider. 
Think of each measure as being a paint stroke in a corporate picture. By itself, it may not have much value. However, when combined with others, a picture is formed. The more variables examined regression, the clearer the picture. For example, consumer age, income, gender, background, industry, and interests could provide guidance to the best marketing approach to take.
Although past data are used to indicate the future, the social and economic impact of the coronavirus needs to be considered. What are the impacts to unemployment, income disruption, and recovery plan by each state?
Clearly state variables that you would utilize in your path that you are recommending. Utilize a regression model to determine if the recommendation is to expand into the new market or to not expand. Ensure that you provide adequate justification for your recommendations. The Board of Directors requires your input based upon your previous exercises from Units 1, 2, and 3. 

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, neutra

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 demo

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 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


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 th

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