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Statisticsdeals with data. It is easy for an individual to be confused by rawnumbers(D`Alessio, 2011).A good startpoint to organize and understand data is visualizing itin pictures such as pie charts and bar graphs. This method provides away to contextualize the numbers one may encounter. For instance, fora baby weighing seven pounds and eight ounces, one can use thismethod to estimate the size of the baby from tiny to gigantic.

Varietiesof methods offer ways to visualize raw numbers and tons of raw data(D`Alessio,2011).For instance, the army collected data to help design the right sizeof uniform and gear. Since the civil war, the military has recordeddata on the height of the soldiers and has tracked changes over thedecades. In the twentieth century, Americans grew taller due tobetter nutrition and better health care. In the year 2003, the armyleaders started outfitting soldiers for Operation Lucky freedom andnoticed that they had a gear shortage. There were indications thatthe department faced a shortage of equipment in the correct numbersand sizes. This situation raised an alarm that their database was outof date.

TheAnthropometric team is responsible for conducting surveys to updatethe databases. The team would assist to figure out how big soldiersare and the size of the equipment they require. The team visited manymilitary bases randomly sampling different dimensions of the armypersonnel and aiming to know the average size and frequencies amongthe soldiers. The team confirmed that the soldiers have increased insize. It is crucial for the gear to fit well in the soldier’s bodyto ease performance and maximize protection. The precise sizemeasurement is important for the military equipment designers. Thesurvey was the first to incorporate a 3-D representation of soldiersto design various gears (D`Alessio,2011).This method is cheap and faster than using real human models. Thedimensions of the human image have to reflect those of the users. Asurvey on different dimensions allows the army knows which sizes tokeep well stocked and those to custom order.

Footmeasurement data in centimeters of the soldiers is unorganized, andone cannot tell if the data has any rhyme or reason. This data isorganized into a Stemplot to find out the variations in the data. Useof software or calculator can help sort the data in numerical order.Each measurement is separated into a stem (the whole number or firstpart) and a leaf (the fraction or the last part of the value) in avertical order. It is important to include all the possible stems inthe data range including those that do not have leaves (D`Alessio,2011).All leaves should be arranged in numerical order. This method helpsone to determine the most common and the less shared values based onthe number of leaves per stem.

AStemplot provides a way to establish the median, the distribution andthe range of the data. When the data looks symmetric but has anindividual component falling out of the data range, the figure iscalled an outlier, which might be an actual value or an entry error. are useful for small sets of data. Stemplot is applied tofind out where a number fits in in the entire distribution. Astemplot gives us the estimation of the soldier’s foot as eitherthe small, large or the medium size which is hard to determine byjust analyzing the raw numbers. Other areas where stemplots can beapplied are in the analysis of fuel consumption among the variousvehicles per distance covered. can also be used to comparetwo data sets via constructing a back-to-back stemplot which uses thesame stem for two separate leaves (D`Alessio,2011).


D`Alessio,M. (2011).&nbspAPstatistics.Piscataway, N.J.: Research &amp Education Association.

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