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Unit 3 – Individual Project 

  Unit:  Use Common Statistical Tests to Draw Conclusions From Data 

 Deliverable Length:  2 pages 

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Assignment Objectives

Explain the applications and limitations of statistical hypothesis testing as applied to business situations. 

Based upon the input from Units 1 and 2, you have just received your next assignment that will contribute to your next decision. For the outdoor sporting goods client, based upon your prior decision on whether or not to expand to the next market or retain your current position, justify your decision further utilizing the chi-square distribution tool. One key criterion point: You do not have adequate data to formulate a full chi-square for the outdoor sporting goods client. However, you have sufficient data to initiate this process. You are charged to demonstrate the initial steps of a nonparametric test that are qualitative. Utilizing the null and alternative hypotheses, further present your justifications for your selection and what it means beyond the mere formulas. What is this going to tell the Board of Directors and contribute to the decision-making process?

The following information may be helpful in understanding chi-square and hypothesis testing:

Please review this helpful video. The presenter uses flipping a coin and rolling a die. These are examples and analogies used in the CTU resources.

The following are assumptions that might make the assignment more helpful and make the responses more uniform:

  • Continue to utilize the Big D scenario. Work under the assumption that the sample is based upon 2 different proposed product lines.
  • Additionally, work under the assumption that the same demographics are utilized for each product.


Bozeman Science. (2011, November 13). Chi-squared test [Video file]. Retrieved from https://www.youtube.com/watch?v=WXPBoFDqNVk


Unit 1 – Individual Project

Unit: Concepts and Terminology of Statistics Applied to Business Decision Making

Deliverable Length: 5-6 slides with speaker notes (75 word minimum per slide)


Big data is everywhere, and various businesses around the world are driven by big data. While some businesses rely on big data for organizational decision making, this does not mean that the implications and applications of big data are properly used to ensure optimal effectiveness for the organization.

For this scenario, you have been appointed as a business analyst for Big D Incorporated, charged with providing authoritative recommendations to the Board of Directors. As the business analyst, the recommendations that you provide will be based upon data calculated from statistically appropriate formulas. Be reminded that you are not the company’s statistician yet. However, as the business analyst, you are therefore responsible for interpreting statistical data and making the appropriate recommendations.

Big D Incorporated was offered a series of business opportunities, and it is your job as the business analyst to provide expert insight and justification for recommendations regarding these potential prospects.

Assignment Details

Big D Incorporated has a business opportunity to provide two different types of information to a new client. As the business analyst, you are tasked to assess the financial feasibility of this opportunity. The new client is a retailer and looking to expand its product offerings. However, the client is requesting Big D Incorporated to assist in the decision-making process.

Prepare a presentation that addresses the following:

Explain the difference between nominal and ordinal data.

List 3 qualitative attributes of outdoor sporting goods that the client may want to ask consumers. Make sure 1 of the qualitative attributes is nominal.

For each ordinal attribute, assign names for the endpoints of a 5-point rating scale.

Explain the difference between interval and ratio data.

List 2 quantitative attributes of outdoor sporting goods that market researchers might want to measure.

Explain the difference between a population and a sample.

Unit 2 – Individual Project

Unit: Summarize and Present Information in a Meaningful Way

Deliverable Length: 5-6 slides and speaker notes (75 word minimum per slide)

For this assignment, you will need to become familiar with specialized business intelligence (BI) software, and then download the software. Complete the following steps to begin:

Step 1: Watch an instructional video on the BI software.

Step 2: Review various BI Software options, including Tableau and Excel. Search the CTU Library and the Internet to learn about optional software.

Step 3: If you choose Tableau, download the Tableau BI software. After you select the link, look for the orange icon in the middle portion of your screen to begin the download or use any BI software to create a graph illustrating comparison between the United States and zip code data.

Consider the following scenario, continued from Unit 1 Individual Project:

Big D Incorporated needs to prepare an assessment regarding the feasibility of making a recommendation for expansion into another market. As the business analyst, your assignment is to prepare a short presentation for the Board of Directors that apply concepts learned in Unit 1 and concepts from Unit 2 to make the necessary recommendations and justifications for those recommendations. You will need to compare and contrast the varying markets to make the best recommendation possible. Complete the following:

Download the reports for the United States from this Web site. The Web site provides free demographical services where one can choose from a variety of criteria and generate reports based on the U.S. Census data. This particular file contains 4 reports:

General Summary

Census Trend 1980 to 2000 Summary

Occupation and Employment Summary

Income Summary (based on the entire United States)

Download the reports for zip code 60614 from this Web site. This particular file contains 4 reports:

General Summary

Census Trend 1980 to 2000 Summary

Occupation and Employment Summary

Income Summary based on the U.S. zip code 60614

Using all 4 demographic reports (General Summary, Census Trend 1980 to 2000 Summary, Occupation and Employment Summary, and Income Summary) for the United States and zip code 60614, prepare a 1-page summary slide explaining how your territory differs from the national profile. Feel free to note anything that you found surprising in the data.

Use the BI software you choose to depict information from these reports.

Prepare a presentation of 4–5 slides of your major findings. You may group a category of data from the Total United States and zip code 60614, and create a graph to show how they differ. The presentation should have at least 2 graphs and a headline that summarizes a key takeaway from the graph.

Your presentation should include 5–6 slides in PowerPoint, plus title and reference slides with speaker notes (75-word minimum per slide).

For assistance with the usage of Tableau for analysis and reporting, review and use the instructions below. Those should be helpful if you are having problems with the software usage or data. However, you do not need to use Tableau. For BI software usage such as Excel, resort to using the Help functions and other resources on the Internet and in videos to learn how to use Excel to create graphical presentations including table usage.

The instructions to create a bar graph in Tableau (optional) or in another BI software are as follows.

Open the assignment CTU instructions.

Download the BI software.

Create your Tableau Public Account.

Click the download link for the 60614 file (file will open in Excel).

Click the download link for the U.S. file (file will open in Excel).

The following are some options:

Create separate demographical worksheets (e.g., Educational Attainment or Household Income). Include zip code and U.S. data.

See a sample of the file available with column headings and extraneous blanks.

Household Income Chicago U.S.

1980 1990 1980 to 1990 2000 1990 to 2000 1980

Median Household Income 18,438 41,227 1.236 69,311 0.681 16,902

Average household Income 24,245 67,607 1.788 114,615 0.695 20,382

Per Capita Income 13,564 38,518 1.84 63,791 0.656 7,321

See a sample of the file available with column headings and extraneous blanks.

2000 Educational Attainment U.S. Chicago

College: Associates Degree 6.3% 1.9%

College: Bachelor’s Degree 15.5% 44.2%

College: Graduate Degree 8.9% 34.0%

College: Some College, no Degree 21.1% 8.7%

School: 9th to 11th grade, no diploma 12.1% 3.2%

School: Grade K-9 6.1% 2.0%

School: High School Graduate 28.6% 5.6%

Create a file with data organized without extraneous rows and columns but including headings.

The idea is to clean up the file before usage. Separating files and worksheets may be the best way to make this happen

Note that in the real situation, when using BI software, you will want to ensure that your data are clean and that you have similar data in your files.

Save the file(s) on your computer.

Open Tableau. Then do a File > Open > Open on your saved file.

At the bottom of the screen on the left, click on the little box next to Sheet 1 (looks like a bar graph with a plus in the top left corner).

Under “Measures” (on the left), you should see “Chicago” and “U.S.” Click on “U.S.,” and pull it into rows at the top of the screen. Then, click on Chicago and pull it over into rows.

Under “Dimensions” on the left, you should see “Household Income.” Click on “Household Income,” and pull it into column.

In the second column from the left, you should see “Measures.”

In the top right corner, you should see a “show me” box illustrating ways you can view the data based on the values you have chosen. To do side-by-side comparisons, put your cursor over the rightmost picture on the third line with the side-by-side blue and orange bars. Click on it. Your graph should appear on the page. You can play around with how you want to portray your data.

To save in Tableau Public, click File > Save to Tableau Public as… > give the file a descriptive name. You will be asked to log into Tableau Public using your e-mail and password.

When your graph is saved in public, at the bottom right, there is a download button. Decide how you will save the file. (You can create an image and name the .png file. It will be saved in Tableau Public.)

After the file is saved, open the .png file, and edit or copy the graph.

Open your PowerPoint file, and then paste (special) it on the slide. You can adjust the size.

Repeat the same in Tableau opening of any other prepped and cleaned file.

Note: Measure values are your number fields; measure names are the descriptors.

For more information on creating PowerPoint Presentations, please visit the Microsoft Office Applications Lab.

Additional Resources to Use

American Marketing Association. (n.d.). Summary reports. Retrieved from http:/www.marketingpower.com/content753.php

Igines. (2012, December 9). How to make a line graph in Excel (Scientific data) [Video file]. Retrieved from https://www.youtube.com/watch?v=Xn7Sd5Uu42A

Microsoft. (2018). Create a chart from start to finish. Retrieved from https://support.office.com/en-us/article/Create-a-chart-from-start-to-finish-0baf399e-dd61-4e18-8a73-b3fd5d5680c2


Pak, A. (2013, August 12). Tableau public – Overview and applications [Video file]. Retrieved from http://youtu.be/PnkMiHocqRw

Tableau. (2018). Tableau public. Retrieved from http://www.tableausoftware.com/public/

Concepts and terminology of statistics applied to business decision making

Terrance Avant

Unit 1 – Individual Project


Nominal and ordinal data

Nominal data

Not quantifiable

Can’t be assigned any order

They are only allocated to definite categories

Categories does not have meaningful order

Ordinal data


Can be put into some order

Categorical data where values are ordered

Data can be arranged in meaningful order

Nominal data are data variables without any quantitative values. Nominal data represents items that can be distinguished using a simple naming system. nominal data have no numeric value. Nominal data are only allocated to distinct categories without any meaningful order or hierarchy. Example includes gender, occupation, marital status etc.

On the other hand, ordinal data is usually placed in some form of order by the position on the scale. Ordinal data is categorical data where values are ordered. Ordinal data only show sequence. Examples of ordinal data are test grades A, B, C and D; economic status i.e. high, medium and low etc.


Qualitative attributes of the goods

Color of sporting commodities (nominal)

Satisfaction levels of users (ordinal)

Confidence levels of user to the goods (ordinal)

For ordinal qualitative attributes, a 5 point rating scale indicates high levels of confidence and satisfaction.

The nominal qualitative attributes of outdoor sporting goods is the color of the goods. The ordinal qualitative attributes of the outdoor sporting goods is level of confidence that consumers have in these goods and level of user satisfaction obtained from using them. For the two ordinal attributes, the endpoint of five point rating scale would be 5 to show high confidence levels of consumers in these goods and high satisfaction levels of users of the goods.


Interval and ratio data

Interval data is simply ordinal data whose intervals split equally between values.

Ratio data is ordinal data but the interval between values are not equally split.

Ratio data is simply interval data containing natural zero point. Interval data do not have true zero.

Interval data are values which are below zero. Ratio data is data that cant go below zero.

Interval data is ordinal data in which the intervals between the values are equally split. An example is temperature in degrees. Ratio data is ordinal data which has true and natural zero point. An example is time where a zero is important. Interval data represent values below zero. Ratio data cannot fall below zero.


Quantitative attributes of the goods

The price levels of various brands in the market

The number of all brands in the outdoor sporting goods market to fathom the market

Quantitative attributes are attributes that can be measured in numbers and objectivity. Market researchers might want to measure a number of quantitative attributes of outdoor sporting goods. These includes; number of sporting brands in the market so that they can understand the competition scope in the field. Another attribute is the price levels of different brands of outdoor sporting goods in the market.


Population and sample

A population simply means that entire group which a research is trying to to draw conclusion about.

A sample refers to a specific group from which the population which the research will gather data from.

Sample size is less than the whole population size.

In research, a population means all members of a group that a researcher wants to study and draw conclusion from. On the other hand, a sample is the number of people who are picked from the population to represent the others in the study. A population is the whole group that a research wants to draw conclusion about. A sample is a particular group from the population that the research will collect data from. A sample is a subset of the population that is being studied.



Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2016). Statistics for business & economics. Nelson Education.

Black, K. (2019). Business statistics: for contemporary decision making. John Wiley & Sons.

Ferguson, T. S. (2014). Mathematical statistics: A decision theoretic approach (vol. 1).Academic press.

Summarize and present information in a meaningful way

Terrance Avant

Unit 2 – Individual Project


Educational attainment

Education attainment can be defined as the highest education level which an individual has successfully completed. The successful completion of an education level, on the other hand, is the achievement of learning objectives of that level, usually validated through evaluation of the acquired skills, knowledge and competencies. Based on the graph above, it is clear approximately 44.2 percent (44.19%) of people in Chicago attained their bachelors degree compared to entire United States where approximately 15.5 percent (15.54%) go their bachelors degree (Chen, Chiang & Storey, 2012). Therefore majority of bachelors degrees comes from Chicago. This also apply to graduate degree which is higher in Chicago compared to the entire United States.



Chicago city makes up approximately 25 percent of the population of the wider Chicago- Joliet- Naperville (CJN) Metropolitan areas. Just like Chicago, the CJN Metropolitan area is the third largest metropolitan in the United States after the New York- Northern New Jersey- Long Island Metropolitan areas (20.18 million) and Los Angeles- Long Beach- Santa Ana Metropolitan areas (13.34 million). The population grows every year (Pak, 2013). Something that caught my attention is that the percentage of male and female populations in Chicago is close to those of the entire U.S. In addition, the average household size is similar to the U.S.


Chicago Population

1980 Population      Percent Female      Percent Male Total Households Average Household Size Family Population Group Quarters Population 57441 0.51200000000000001 0.48799999999999999 31698 1.75 30152 1952 1990 Population      Percent Female      Percent Male Total Households Average Household Size Family Population Group Quarters Population 61469 0.52 0.48 34838 1.7 26956 2182 1980 to 1990 Population      Percent Female      Percent Male Total Households Average Household Size Family Population Group Quarters Population 7.0000000000000007E-2 8.6999999999999994E-2 5.2999999999999999E-2 9.9000000000000005E-2 -2.8000000000000001E-2 -0.106 0.11799999999999999 2000

Population      Percent Female      Percent Male Total Households Average Household Size Family Population Group Quarters Population 64637 0.51100000000000001 0.48899999999999999 35975 1.7 27975 3363 1990 to 2000 Population      Percent Female      Percent Male Total Households Average Household Size Family Population Group Quarters Population 5.1999999999999998E-2 3.3000000000000002E-2 7.1999999999999995E-2 3.3000000000000002E-2 1E-3 3.7999999999999999E-2 0.54100000000000004

US Population

1980 Population      Percent Female      Percent Male Total Households Average Household Size Family Population Group Quarters Population 224811135 0.51500000000000001 0.48499999999999999 79887387 2.75 193665964 5417823 1990 Population      Percent Female      Percent Male Total Households Average Household Size Family Population Group Quarters Population 248710012 0.51300000000000001 0.48699999999999999 91947641 2.63 203905540 6697345 1980 to 1990 Population      Percent Female      Percent Male Total Households Average Household Size Family Population Group Quarters Population 0.106 0.10199999999999999 0.111 0.151 -4.20000000 00000003E-2 5.2999999999999999E-2 0.23599999999999999 2000

Population      Percent Female      Percent Male Total Households Average Household Size Family Population Group Quarters Population 281421906 0.50900000000000001 0.49099999999999999 105480101 2.59 231374718 7778633 1990 to 2000 Population      Percent Female      Percent Male Total Households Average Household Size Family Population Group Quarters Population 0.13200000000000001 0.125 0.13900000000000001 0.14699999999999999 -1.4E-2 0.13500000000000001 0.161


The metropolitan area of Chicago is comprised of metropolitan divisions- separate employment centers within the bigger metropolitan area. The Chicago- Naperville- Arlington Heights division accounts for about 80 % of the workforce, increased 51,600 jobs over an year ago. In Lake County- Kenosha County, IL- WI division, there was an increment of 5,600 jobs over the year (Pak, 2013). Based on the graphs above, Chicago recorded a higher employment rate compared to year 2000. education is attainable in Chicago because of higher household income rate compared to the United States because the population around the city has established a culture that reduces school dropout rates (Chen, Chiang & Storey, 2012). In majority households with high incomes, there are higher chances that children will get better education in Chicago compared to United States this reduces the dropout rates. In addition, more people in Chicago are employed. This means that they are financially stable to support themselves and get higher education levels.



Employment          1990

Not in Labor Force In Labor Force      Employed      Unemployed      In Armed Forces 66603437 125216716 115716133 7780384 1706113 Employment          2000 Not in Labor Force In Labor Force      Employed      Unemployed      In Armed Forces 0.34699999999999998 0.65300000000000002 0.92400000000000004 6.2E-2 8.9999999999999993E-3 Employment          2000

Not in Labor Force In Labor Force      Employed      Unemployed      In Armed Forces 78319195 138829294 129728865 7947452 1152977 Employment          2000 Not in Labor Force In Labor Force      Employed      Unemployed      In Armed Forces 0.36099999999999999 0.63900000000000001 0.93400000000000005 5.7000000000000002E-2 8.0000000000000002E-3


Employment 1990

Not in Labor Force In Labor Force      Employed      Unemployed      In Armed Forces 10340 45585 43739 1827 21 Employment 2000 Not in Labor Force In Labor Force      Employed      Unemployed      In Armed Forces 0.185 0.81499999999999995 0.96 0.04 0 Employment 2000

Not in Labor Force In Labor Force      Employed      Unemployed      In Armed Forces 11120 47653 45543 2102 8 Employment 2000 Not in Labor Force In Labor Force      Employed      Unemployed      In Armed Forces 0.189 0.81100000000000005 0.95599999999999996 4.3999999999999997E-2 0


When people are highly educate, the opportunities of securing jobs across the whole world are higher. In this regard, the higher income percentage in Chicago is as a result of high number of educated elites who are skilled and competent enough to be in a position to secure most jobs in the city compared to the United States in general. In addition, most of the working people in Chicago earn high incomes compared to the United States in general (Laursen & Thorlund, 2016). This explains why incomes in Chicago are high than in the United States.



Average Household Income Median Household Income Per Capita Household Income 114615 69311 64426


Average Household Income Median Household Income Per Capita Household Income 56643 42257 21587


Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.

Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: taking business intelligence beyond reporting. John Wiley & Sons.

Pak, A. (2013, august 12). Tableau public – overview and applications . Retrieved from http://youtu.Be/pnkmihocqrw

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