# Statistics in Business

STATISTICS IN BUSINESS 5

Statistics inBusiness

Statistics inBusiness

Statistics is the scientific branch that deals with analyzing,collecting, interpretation, and classification of facts involvingnumeric to deduce facts based on their quantity of likelihood(Kriukov, 2013). It interprets accumulated data that is in enormousamounts and cannot be understood by regular observation. This type ofdata forms a pattern that can be statistically identified, thus,enabling calculations.

Statistics can either be quantitative or qualitative. Quantitativedata refers to the type of data that is measurable (Baker, 2012). Afew examples are length, width, weight, age or even the breadth of aparticular object or substance. This type of data does not requireapproximation since the measurements provided can be used to deducethe necessary facts. Qualitative data involves the information aboutqualities, which cannot be measured (de Casterle, Gastmans, Bryon &Denier, 2012). Examples include the degree of softness, the color ofan object or substance or even the roughness of a surface. It isreliant on one’s opinion and not the actual quantity of something.

During the evaluation of qualitative data, the frequency ofoccurrence is mapped to the specific data. This is called a frequencydistribution. A table or chart is created to represent the variables.Sometimes data that is not in this chart may be calculated bydetermining the total and dividing the frequency of all datacategories by the total (de Casterle et al., 2012). This is referredto as the relative frequency. When evaluating quantitative data,measurements are taken for each subject and their informationrecorded in a chart, text or a table. The data usually appears in adistinctive pattern. Tables are more suitable for classification andshowing the structure of information while graphs may be preferredwhen identifying relationships and patterns. Tables and charts inwhich data is recorded should be self-explanatory.

Business statistics involve data measurement. There are four levelsof data analysis, namely, interval, ordinal, nominal and ratio.Norminal measurement level means using numbers to classify thecollected data. Letters, alpha-numeric symbols and even words may beutilized at this level (Marateb, Mansourian, Adibi & Farina,2014). For instance, when organizing short, medium-sized and tallpeople, S may be used in classifying short people, M for medium sizeand T for tall people. The ordinal measurement level shows the orderof relationships among the collected data. An example is whenclassifying the height of individuals, the one with the highest valueof height is grouped as tall, the person with the smallest is groupedas short while the one with the height that is between tall and shortis classified as medium size. Interval measurement level shows theequivalence of the distance between intervals (Marateb et al., 2014).For instance, when calculating student marks, the difference betweenforty marks and fifty marks should be equal to the difference betweenthirty and forty marks. The ratio measurement level allows a zerovalue in the distance between intervals. The difference betweenDivisions of points on the given scale is equal.

Many numerical problems can be solved using statistics. Examples arecalculating the mean of the ages of students in an individualuniversity. Data may be collected from the students about their ages,filled in a table and later used in the creation of graphs from wherethe mean can be predicted (Kriukov, 2013). Another example is whendetermining the percentage variation of voters in the last fiftyyears. The values of the number of electors in each election year forthe past half-century can be filled in a chart or table, and then thepercentage may be obtained from there.

Statistics has proven to be an important aspect of business. It wouldbe impossible to predict and identify market trends without its use.With the current advancements in technology, numerous websites andapplications have been developed to facilitate it. Consequently, thebusiness future seems bright and promising with the increase inapplication of statistics

References

Baker, M. (2012). Quantitative data: Learning to share. *NatureMethods*, 9 (1), 39-41

de Casterle, B., Gastmans, C., Bryon, E., & Denier, Y. (2012).QUAGOL: A guide for

qualitative data analysis. *International Journal of NursingStudies*, 49 (3), 360-371

Kriukov, V.A. (2013). What do statistics know and not know? *Problemsof Economic Transition*,

55 (11), 3-5.

Marateb, H., Mansourian, M., Adibi, P., & Farina, D. (2014).Manipulating measurement scales

in medical statistical analysis and data mining: A review ofmethodologies. *Journal of Respiratory Medical Science*, 19 (1),47-56

No related posts.