Statistics in Business
Is statistics just numerical data or is it more than that? According to Investopedia, “Statistics is derives from research of models, representations, and summaries with empirical data or real world observations required for mathematical analysis” (Investopedia, 2013).
The following content will address various aspects of fundamental statistic variables to obtain a better grasp of what statistics really is. This discussion conveys the important role of statistics when practicing business decision-making. All in all, a description of current real life examples along with real life solutions will help understand data summarization of data into numerical form.
Importance of Statistics
To begin with statistics can be considered numerical information. How does this relate to business decision-making? The role of statistics comes into play when a business is in need of data collection, organizing, analyzing, and interpreting of data. It is applied through variables and other statistical measurement tools. Variables are the type of statistics used to perform research. These are used depending on the type of research. Factors like sample, parameter, and population need to be taken into account. Descriptive statistics is self-explanatory, it is research and information consolidation of a certain population; whereas inferential statistics is more about drawing conclusions and inform that data. Other tools used statistical measures are kurtosis, variance and mean.
Differentiation of Qualitative and Quantitative Analysis
Quantitative analysis is gathering of data in a large scale without affecting the evidence. It can be looked as discrete as in possible values of a finite number or continuous data meaning infinite possibilities. On the other side of the coin, qualitative analysis is not number oriented it is rather seen as nonnumeric, examples like group studies and individual interviews are require of more time to consolidate.
Types and Levels of Measurement
In order to understand statistical measurements we must consider all aspects of measurement. The four levels of measurement consist of nominal, ordinal, interval, and ratio-level data. Nominal data tends to have no specific order, it mostly labels or names categories. Ordinal is structured with a sense of order and Interval focuses more on
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