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Organizations look at results in many ways: expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of? Are they descriptive or inferential data, and what is the…

Lecturer: Business Statistics “Numbers and measurements are the language of business.  Organizations look at results in many ways: expenses, quality levels, efficiencies, time, costs, etc. What measures does your department keep track of? Are they descriptive or inferential data, and what is the difference between these?  (Note: If you do not have a job where measures are available to you, ask someone you know for some examples, or conduct outside research on an interest of yours, or use personal measures.)”
The paper will explore the above questions in detail. First measurements and numbers are business language. This is given that Professionals and Managers frequently are attentive to their measures level like mode, sums, and means. This is a way of communicating of the measure level and numbers use.
In comparing of descriptive and inferential data, the two define the data variation in terms of the probability or dispersion distributions/patterns describing the data. This is where both rely on the same set of data. The only difference between the 2 is that inferential data aims to draw general conclusions about a big population (Richard, 69). This way its clear organizations look at results in many ways: quality, time, expenses etc.
The dispersion learnt earlier helps to recognize the data information being tracked/measured. In order to decide of results and outcomes, we ought to understand the data variation or consistency (Richard, 44). This means data variation results to dissimilar results understanding.
Descriptive statistics method of calculating data is for instance, function of Excel Analysis ToolPak function which produce salary data that is descriptive in statistics. The example of calculations in statistics is the Employee Salary Data set.
In research, we categorize the collected data by the GEN1 (G variable) for females and males and then get each gender average and standard deviation for these variables in terms of age or other quantity measure (Richard, 78). It is possible to use for one gender descriptive and the functions of FX for (MEAN and STANDARD DEVIATION) for the other.
The paper has looked at the numbers and measurement as well as descriptive and inferential data as a way of analyzing data collected. This way the paper has discussed how inferential data differs from the descriptive data where as seen, inferential depend on the data to draw general perspective of big populations. Lastly, the paper has viewed some research application of descriptive data in contrast to inferential data.
Work Cited
Richard, Chand. Statistics methods and analysis. : Belmont: C.A.2009. Print Read More
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