MGMT600 APUS Common Statistical Test Chi-square & Hypothesis Testing Based upon the input from Units 1 and 2, you have just received your next assignment t

MGMT600 APUS Common Statistical Test Chi-square & Hypothesis Testing 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. Concepts and Terminology of Statistics
Applied to Business Decision Making
BIANCA LOPEZ
MGMT600
qualitative attributes of outdoor sporting
goods
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Aesthetic appeal (appearance).
?
Flavor/taste
?
Texture
?
Color
?
Sensation
Ordinal attributes: 5 point rating scale
Subject
Very Poor Quality
Cheese Skewers
1
Poor Quality
Banana sandwich triangles
Patry Cakes
Quality
High Quality
3
Salted pretzels
Energy chews
Neutral
4
2
5
difference between nominal and ordinal
data.
Nominal Data
Ordinal Data
Consists of named categories
Consists of ordered categories
Are categorical data
Are in between categorical and quantitative
variables.
Cannot be assigned to any type of data
Can be assigned to any type of order
The order of their values has meaning.
The order of the values indicate a higher rating.
Examples: Housing tenure, eye color, ethnicity.
Examples: Age categories and frequency of
consuming the outdoor sporting goods.
Nominal, ordinal data , a rating scale, and
quantitative attributes.
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Nominal data
?
?
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Not ranked at all but used for identification. i.e. 360486789, (ID number), 90210, (Beverly Hills Zip
Code).
Ordinal data
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Expressed in several forms, different phasing, and are also placed in order as their values represent
categories with meaningful measures i.e. the Likert Scale; bottom middle, top; beginning, middle, end.
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Can be ranked by attaching a rating scale just as is in the previous slide.
Quantitative attributes
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The quantity in the snack.
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The weight/size in the snack.
difference between interval and ratio data.
Interval Data
Ratio Data
The zero point is arbitrary
Zero point is meaningful
POSSIBLE POPULATIONS FOR THE TESTS
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The types of population that will be studied for this research are college students. Single adults,
teenagers, and parents
Difference between population and sample
Basis of Comparison
Population
Sample
Meaning
Collection of events, items, and
Subset of a population.
people about which one can make
inferences
Includes
All units of the group
A handful of units
Characteristic
Parameter
Statistic
Focus on
Identifying the characteristic
Making inferences about the
population
Data Collection
Census or complete enumeration
Sampling or sample survey
Target market, avoiding bias
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The target market
?
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College students, parents, and single people.
Why avoid bias
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Is potentially misleading
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Causes false business conclusions
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affects results, reliability, and validity of findings.
References
Keller, G. (2017). Statistics for Management and Economics + XLSTAT Bind-in.
Boston: Cengage Learning.
Norman, G. R., & Streiner, D. L. (2008). Biostatistics: The Bare Essentials.
PMPH USA.
Schneider, R. A. (2003). Basic Statistics for Social Workers. Lanham: University
Press of America.
Applied Managerial
Decision-making
Bianca Lopez
MGMT600
Introduction
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The chief aim of the presentation is to execute a statistics
examination which will be deployed in decision making
pertaining a targeted market.
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The data that will be use will refer to U.S. Census
nationally populations and Zip code 60614 Chicago.
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The referred trends will be within, Census Trend 1980 to
2000 Summary, Occupation and Employment Summary,
and Income Summary based on the entire United States
and zip code 60614 areas of Chicago.
Assessments of U.S and 60614 zip code
Data Disclosures
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The comparison of the countrywide data to that
of the zip have attained a commonality in relative
percentages of workers aged 16+ (15% in any
variable)
?
There is a striking variance in high level revenue
between 60614 and countrywide average that
ranges to $200,000.
Statistical Assessment of Education
Achievement U.S and 60614 Zip Code
(2000)
Household Income Analytics in 2000 of U.S
and 60614 Zip Code (Chicago)
Income Comparison Report 2000
16.00%
14.00%
12.00%
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
USA
Chicago
Conclusion
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Considering the historical tendency in data referenced to
demographical elements it enables us to insight and
understand statistically on the current market tendency
pertaining the same situation of market investigated.
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There is a high possibility to attain statistic censor of the
populace of whole country; however, it is quite tough to
attain the whole desired partiality of a nation populace.
This is only done through the approach of the voting
system.
Reference
?
Agha, S., Alrubaiee, L., & Jamhour, M. (2012). Effect of core competence on
competitive advantage and organizational performance. International Journal
of Business and management, 7(1), 192.
?
Garcia, G. A., Huerta, A. H., Ramirez, J. J., & Patrón, O. E. (2017). Contexts
that matter to the leadership development of Latino male college students: A
mixed methods perspective. Journal of College Student Development, 58(1),
1-18.
?
Suhr, D. (2011). Answering your research questions with inferential statistics.
Bolder, CO: University of Northern Colorado.

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