Big Data Report: Analysis of Data Types and Business Implications

This report analyzes nominal and ordinal data, including their key differences and applications within a business context. It discusses the characteristics of nominal and ordinal data, providing examples like consumer satisfaction and brand preferences. The report further elaborates on the differences between qualitative and quantitative data, and the importance of statistical data analysis. It also explains the differences between interval and ratio data, and how they can be applied in business settings. The report also discusses the distinction between sample and population data, providing examples to illustrate these concepts. The report is designed to provide expert insight and recommendations for Big D Incorporated, aiding in data-driven decision-making for business opportunities.

Statistics

Difference Between Nominal and
Ordinal Data
•Ordinal-certain ranking or position for instance
• Pastry satisfaction of consumer utilizing a ranking scale of 1 to 5 .
Where 5 signifies the greatest satisfaction level.
• Rating the confidence of a consumer in which brands of sporting
goods are most wanted or required
•Nominal-evaluating categorized answers for instance
• Place of residence, gender and preferred color

Five Rating Scale: Ordinal Attributes

The Association between Ordinal
and Nominal Data Utilizing a Scale
Rating
• Nominal Data :
• Nominal data is data whose observations or values can be given a number where the
code are just but For example, the number (code) one could denote female while
the number zero could denote male.
• An individual can tally data that is nominal, nevertheless, but he cannot measure or
put data in order.
• Ordinal data
• Ordinal data is data whose observations or values can be positioned through fixing to
it a rating sale
• This data can be placed and counted similar to what is displayed on the earlier slide
instance satisfaction of the consumer, however ordinal data cannot be quantified.

Qualitative Characteristics
• Qualitative data is statistical
• Utilizing numerical data scientifically ( that is Organization W would
need to contemplate):
• Evaluating food moisture of snacks.
• Value for calories for instance trans fat, fat, vitamin content and
sugar etc.

Ratio and Interval Data
Differences
•Interval:
• Statistical.
• Unvarying understanding all along.
• Zero and not perfect.
•Ratio
• Most informative and numerical.
• It possess a point that is true where the position of zero denotes the lack
measured quantity.

Variance Between Sample and
Population
•Sample: “n” represents the population sample.
• City –n=Fifty
• Nationally-n= Five thousand
• State- n=Five hundred
•Population: “N” signifies the entire population
• City-N=Fifty thousand
• Nationally-N= Six million
• State –N=Five hundred thousand

References