Use Common Statistical Tests to Draw Conclusions From DataIP3
TaraD30
ConceptsAndTerminologyOfStatisticsIP1.pptxConcepts and Terminologies of statistics IP1
Colorado Technical University
Tara Dean
8/13/2022
1
Difference between nominal and ordinal data
Nominal data refers to used for labelling variables.
There is no intrinsic ordering in nominal data.
Ordinal data refers to data that is categorical in nature.
Ordinal data variables are listed in an ordered manner (Suparji et al, 2021).
Data that is used for naming or labeling variables but does not have any quantitative value is known as nominal data. Nominal data typically do not have any inherent ordering. When it comes to a nominal variable like Race, there is no specific order in which the categories can be ranked. categorical information with order is ordinal data. In ordinal data, the variables are arranged sequentially. Ordinal variables typically have a number assigned to them to denote the list's order. The numbers, on the other hand, are not based on any kind of mathematical measurement or determination, but rather are assigned to opinions as labels.
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Qualitative attributes
Attribute 1: Price of the outdoor sporting goods.
Attribute 2: Quality of the products.
Attribute 3: Size of the outdoor sporting products such as shoes.
In the first attribute, the data exist in an order in terms of prices and can be measured. In the second attribute, it is not possible to order the products based on quality as it cannot be weighed appropriately. In the third attribute, there is an order in terms of the size of the product.
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Ordinal data
Attribute 1 (price): Price ranges from economical, relative high and most expensive products.
Attribute 3 (premium customer pack): A sport wear package exist in a pack of a pair of shoe, shirt, pair of socks and shorts.
Ordinal data
Price of the outdoor sporting goods.
Size of the outdoor sporting shoes.
| Very low price | Low price | Medium price | High price | Very high price |
| 1 | 2 | 3 | 4 | 5 |
| Very small | Small size | Medium size | Large Size | Very large size |
| 1 | 2 | 3 | 4 | 5 |
The two attributes can be evaluated on a 5-point scale in illustrating the presence of order. In the first attribute, it is possible to order variables based on the prices. In the second attribute, on the size, it is possible to arrange the variables based on the size of the sporting goods.
5
Data, attributes, population and sample
Ratio data refers to a data that has a defined zero point while interval data exist in meaningful differences (Koo et al, 2018).
Attributes that market researchers may be interested: quality of the goods and durability of the goods.
Population refers to the entire number of all inhabitants or participants of an area.
Sample represents a portion of the population.
Ratio data refers to data that has a defined ratio point from which reference on the data is made. Interval data refers to a set of data that exist after a meaningful difference between the data. Market researchers are interested in variables or attributes that appeal to the customers in making purchases. Population refers to the entire number of all inhabitants in an area while a sample is a small section of the entire population.
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Reference
Koo, H., Chun, Y., & Griffith, D. A. (2018). Geovisualizing attribute uncertainty of interval and ratio variables: A framework and an implementation for vector data. Journal of Visual Languages & Computing, 44, 89-96.
Suparji, S., Nugroho, H. S. W., & Martiningsih, W. (2021). Tips for Distinguishing Nominal and Ordinal Scale Data. Aloha International Journal of Multidisciplinary Advancement (AIJMU), 1(6), 133-135.
