Management Summary
IntimidationT
Business_Analysis.pptxBusiness Analyst
Tenika J Tassin
Applied Managerial Decision-Making
Colorado Technical University
Dr. W. Cousar
03/6/2022
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.
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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.
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Qualitative Attributes of Outdoor Sporting Goods
Trust / Confidence
Satisfaction
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.
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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.
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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
Parents
| 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.
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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.
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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.
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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.
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References
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
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