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Unit1IndividualProject-Conceptsandterminologyofstatisticsappliedtobusinessdecisionmaking.pptxConcepts and terminology of statistics applied to business decision making
Terrance Avant
Unit 1 - Individual Project
06/30/2021
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
Quantifiable
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.
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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.
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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.
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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.
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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.
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References
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.
