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Statistics in Business

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Statistics in Business
Meg Armen
QNT/275
01/14/2016
James Malachowski

Statistics in Business
Statistics plays a big role in business. Businesses use statistics to improve their different business related problems. Statistics measures probability, hence without profit a business is almost pointless. Because statistics is important in business, we first understand the two branches of statistics, the four levels of data measurement, the role of statistics in decision making, and last, the implementation of statistics is problem solving.
Statistics
According to Jaggia and Kelly, the definition of statistics is a method of the collection, organization, and interpretation of data. In a broad sense, statistics is the communication of numerical information into written form. The authors discuss the breakdown of statistics; first, finding the right data, second, using the right tools to display the data, and finally, to communicate the numerical data into a written language (2014).
Qualitative data Qualitative data such as, labels or names are used to identifying different characteristics of variables on a set of people, objects, or events (Jaggia & Kelly, 2014). Qualitative derives from the word quality. As an example, if we examine a latte, the qualitative description/data would be; the robust aroma, the frothy appearance, the strong taste and the beige ceramic cup it is poured in.
Quantitative data Quantitative variables are described with numerical values (Jaggia & Kelly, 2014). Let's take the latte example and examine it in quantitative data form. The latte is 12 ounces, the serving temperature is 150º Fahrenheit, the serving cup is 5 inches in height and the cost is $4.00.

Four levels of data measurement
To conduct statistics, we must use data measurements. In statistics, there are four types of measurement scales. In order to choose the best method for summarizing and analyzing data, we need to distinguish between the four measurements; nominal, ordinal, interval and ratio (Jaggia & Kelly, 2014).
Nominal and Ordinal scales
According to the text book, nominal and ordinal scales are used to measure qualitative variables (Jaggia & Kelly, 2014). Nominal variables are data which can be grouped or categorized. For example, Dow Jones Industrial Average (DJIA) is a stock market index that shows how 30 large, publicly owned companies in the United States have traded in a standard trading session in the stalk market (Jaggia & Kelly, 2014, p. 9.). The National Association of Securities Dealers Automated Quotation (Nasdaq) and the New York Stalk Exchange (NYSE) are where stalks of these companies are traded. We can group or categories this data, and input the information into a chart. By doing this, we can count how many companies exchanged stalk with the Nasdaq or NYSE. Therefore, these data are classified as nominal (Jaggia & Kelly, 2014). Jaggia and Kelly (2014) explain ordinal scale reflects a stronger level of measurement. With this scale, we are able to not only categorize, but rank the data with some type of characteristic or trait. For example, a survey is conducted for a hotel to rank the service as excellent (4), good (3), fair (2) or poor (1). The ranking between excellent (4) and good (3) are 1. This suggesting that the response to excellent is preferred over good (p.10).

Interval and Ratio scales
According to the text book, interval and ratio scales are used to measure quantitative variables (Jaggia & Kelly, 2014). Interval scales are numerical scales in which intervals have the same action throughout. Let's take the Fahrenheit scale of temperature as an example. The difference between 50 degrees an 60 degrees represents the same temperature difference as the difference between 90 degrees and 80 degrees. The same degrees of measurement, in this case 10 degrees, stays in between 50 degrees and 60 degrees as it does for 90 degrees and 80 degrees. However, since the intervals have the same interpretation throughout the scale, the zero degrees has no meaning in value (Jaggia & Kelly, 2014). Amongst all the different scale levels, the ratio scale is the strongest level of measurement. This scale measurement incorporates all of the characteristics of interval data, in addition, it is true to a zero point. This allows us to interpret the ratio of values. “Measurements such as weight, time, and distance are also measured on a ratio scale since zero is meaningful” (Jaggia & Kelly, 2014, p.13). Sine the zero is meaningful on the ratio scale, values such as weight, time and distance are measurable (Jaggia & Kelly, 2014). The authors state that variables such as sales, profits, and inventory levels are expressed as ratio data (Jaggia & Kelly, 2014, p.13).
The role of statistics in decision making
Statistics help businesses to make the right decisions for their future. The authors give a great example of how statistics help in decision making. A ski resort has given a survey to tweets to see what radio station they listen to on the way up to the resort. 60% of the tweens, listened to KISS108. This particular information can guide the resort to make the right business decision as to where they can direct their marketing spending (Jaggia & Kelly, 2014). “If the resort had to choose one radio station to advertise, it would appear that KISS108 would be the wise choice” (Jaggia & Kelly, 2014, p.11.).
Statistics implied into problem solving
Problem solving is one of the most important aspect of running a business. Statistics is a tool that can help solve real world problems. Statistics helps decision makers solve complex problems and draw conclusions with what the data calculates. Changes in the economy, competition, and new demands of the marketplace all mean that the organization needs to constantly figure out how to stay on afloat. If a business process cannot keep up with the expanding business, management must develop and implement a new process. If the current advertising strategy of the ski resort is not bringing in customers, then management needs to perform a data collection to find out what radio stations their customers listen to. Using the new collected data, can be implemented into solving the resorts decrease in customers.
Conclusion
It is important to understanding and implementing statistics to run a competitive business. The two different branches of statistics differentiate the types of data that can be collected for data measurement. The four levels of data measurement show the strongest to weak types of various data which can be collected to implement into problem solving for a business. We have seen the important role that statistics play in problem solving and decision making.

References
Jaggia, S., & Kelly, A. (2014). Essentials of business statistics: Communicating with numbers. New York: McGraw-Hill Irwin.

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