Use Common Statistical Tests to Draw Conclusions From Data

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Deliverable Length: 1,500 – 2,100 words.


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 as to either 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 do 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 hypothesis, 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:

Chi-Square and Hypothesis Testing

Please review this helpful video. The presenter uses the "flip of the coin" and the "role of the die." These are examples and analogies used in the CTU resources.

The following are assumptions you might make in this assignment 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 two 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

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Statistics applied to Business Decision Making BERT ARSENEAULT MGMT 600 INDIVIDUAL PROJECT 1 10/08/2018 Qualitative attributes of outdoor sporting goods Nominal- These are categorical choices such as ➢ Color of outdoor gear (Black, brown, blue) ➢ Brands preferred (Adidas Group, Columbia Sportswear, VF Corp.) Ordinal – this is ranking of an attribute such as ➢ The satisfaction level of the consumer with the goods provide ➢ Quality of the products Names for the endpoints of a 5 point ordinal rating scale Ordinal attributes can be ranked to determine customer preference Attribute 1 2 3 4 5 Satisfaction Dissatisfied Somewhat Somewhat Satisfied Very dissatisfied satisfied Fair Good of consumers Quality of Poor product satisfied Very Good Excellent Difference between nominal and ordinal data. Nominal ➢ Nominal data or scales refers to labels that are used to identify data without corresponding quantitative value ➢ The values used for the labels have no significance numerically Ordinal ➢ Ordinal data is one which can be arranged in a certain order or rank ➢ As a scale it is used measure concepts which do not have a numeric value such as satisfaction to show the level To what extent are you satisfied with our sporting gear? Example 1-dissatisfied 2-somewhat dissatisfied 3-somewhat satisfied 4satiesfied 5-Very satisfied How nominal and ordinal data relate to a rating scale. Nominal ➢ Nominal data relates to a rating scale in that no scale can be attached to it. ➢ It helps to count and number the data but it cannot be measured for example in terms of gender one can either be male of female Ordinal ➢ Ordinal data on the other hand relates to rating scale in that it can attached to one and ranked ➢ The ordinal data can also be counted and ranked, however it cannot be measured as such quantitative attributes of outdoor sporting goods Discrete ➢ What number of shoe do you wear? 1,2,3,4,5,6 ➢ Approximately how many pairs of outdoor sporting shoes do you buy in an year Continuous ➢ How much money do you spend on out door sports products Difference between interval and ratio data. Interval ➢ Interval scales are numerical ➢ It allows one to determine the exact difference in values ➢ This scale however lack a true zero ➢ An example of this scale is the temperatures or time Ratio ➢ Ratios are numerical in form and provide a great deal of information ➢ They provide the differences between units in exact value ➢ An example is the height or weight of something ➢ This scale has an absolute zero Difference between a population and a sample. Population ➢ Population refers to the group of people whom you intend to generalize the findings of your study to ➢ The population is determined by the scope such as in terms of geography Sample ➢ This refers to the people who take part in a study ➢ These could either be those questionnaire or were interviewed ➢ The sample generally represents a portion of the population that filled the Importance of to avoiding bias when conducting research ➢ Bias can be intentional driven by evil intent of persuading others or unintentional which results from errors in the information or misrepresentation ➢ Biasness should be avoided to ensure that the research is objective in nature Possible Population For The Study ➢ The company’s customers ➢ Consumers outdoor sporting goods References: Beacom, B (2018). What Is the Difference Between Nominal & Ordinal Data? Retrieved on October 4, 2018 from GeeksforGeeks (nd). Understanding Data Attribute Types | Qualitative and Quantitative. Retrieved on October, 3rd, 2018 from HR-Survey, LLC (2018). Rating Scales. Retrieved on October 4, 2018 from Market Research Guy (2017). Types of Data & Measurement Scales: Nominal, Ordinal, Interval and Ratio. Retrieved on October 4, 2018 from Statistics Solutions (2018).What is the Difference Between Population and Sample? Retrieved on October 4, 2018 from Importance of a Psychographic and Demographic Analysis BE RT A RS ENEAULT MG MT 6 0 0 1 0 / 17/2018 Importance of conducting the research: Understand the target customers Demographic analysis: income, ethnic groups, education level Psychographic analysis: Uses and behaviors Evaluation of the annual income: Median annual household income National Zip code 60614 $0 $20,000 $40,000 $60,000 $80,000 Evaluation of the demographic distribution: Prevalence of the different ethnic groups White Population Two or More Races Other Population Native Hawaiian and Other Pacific Islander Alone Hispanic Ethnicity Black Population Asian American Indian, Eskimo, Aleut Population 0% 10% 20% National average 30% 40% Zip code 60614 50% 60% 70% 80% 90% 100% Evaluation of the academic level: Education attainment 3% 6% 9% 44% 34% College: Associates Degree College: Bachelor's Degree College: Graduate Degree College: Some College, No Degree School: 9th to 11th grade no diploma School: Grade K - 9 School: High School Graduate Evaluation of the means of transport: Transportation means Worked at home Walked Taxicab Subway or elevated Streetcar or trolley car Railroad Other means Motorcycle Ferryboat Drove alone Carpooled Bus or trolley bus Bicycle 0% 10% 20% 30% National average 40% Zip code 60614 50% 60% 70% 80% References: _v2.xls _v2.xls ...
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Using Common Statistical Tests to Draw Conclusions from Data
Institutional Affiliation




Analysis of the market before entrance is perhaps the most vital part of business
expansion. The entry is made seamless and product acceptance almost natural due to having a
clear knowledge of the market needs, behaviours, and characteristics. In today’s business world,
analysis of this data is becoming as important as collecting it. Many large businesses with the
ability to collect huge amounts of data from the consumers, even prospective ones, invest as
much in the analysis of the data. The ability to conduct a fast and precisely correct analysis of
data in order to come up with a forecast of market reaction and projection of sales and market
change is an ability every business strives to achieve. It gives the organization the ability to
command a niche and to seamlessly expand as it grows and changes to fit the needs of each and
every market it joins (Baker, 2014).
Recently, Big D Incorporated decided to join the Chicago market after a prior analysis
showed great potential for the outdoor sporting goods it seeks to sell. This time round, the
company’s board of directors requires a reaffirmation of the decision to expand into the market
using two product lines. This paper uses the null and alternative hypotheses to conduct Chisquared and nonparametric Wilcoxon signed-rank tests to show how similar, if not better, the
Chicago market is as compared to the national market. It utilizes datasets in demographic
distributions, annual income, academic levels, and transportation means of both the Chicago
market and the national market.
Hypothesis Testing Using Chi-Squared Testing
In the process of studying a large population of the market, that with less directly
accessible data, hypothesis testing is the best method to use. The process entails generating a
hypothesis, or a claim about the one attribute of the population being questioned, and subjecting


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