Statistics in Business
Created By: Fredrick Jay Harder
Created On: March 21st, 2016
Inspired By: Qnt 275
Taught By: Merry Gallo

If a business has access to reliable data then they can make decisions that will positively impact the business much easier. Without reliable data a business can not thrive because all businesses need data they can rely on to make progress in their prospective field. Luckily, there are statistics that will help to get reliable data to make such important decisions. After all, decisions are not well founded without data or the language thereof. Obtaining that data or language thereof on one's own makes resources spread further as well.

Statistics is known as the language of data. This language is about gathering data, delivering the data, and understanding the data. Statistics and mathematics are very closely related as statistics is the mathematical way to analyze data and observe data. Statistics also equips those who use it with tools to foresee based on the data gathered and is useful in not only business but is also useful in science, health, and government. (Stine &Foster, 2014) After all, science, health, and government need data on a regular basis in order to make informed decisions. This is why being fluent in statistics is very recommended for almost any profession in existence.

Using statistics cuts out the “gray area” or guesswork of life's information in large proportions. With statistics, it is similar to an opinionated conversation. For example, let us assume that John and Mary are discussing the presidential candidates they are most likely to vote for. Mary is discussing the policies of Donald Trump and the John is discussing the policies of Hilary Clinton. The discussion gets heated as they both discuss what is right about their candidate and wrong about the other person's candidate. As John and Mary are just about to get into an angry argument Statis (Incarnation of Statistics) comes in to the conversation. Both John and Mary ask Statis what he thinks about different policies without informing Statis of whether the policy is one of Donald Trump or one of Hilary Clinton. Statis looks at the policies from an objective and unbiased standpoint and chimes in about policies without knowingly supporting either candidate as Statis does not know who has what policy. Statis simply provides information and opinions about the policies based on how concrete they are and how America could benefit from the policies.

There are only two different types of data. One is qualitative (non-numeric) and the other is quantitative (numeric) (Stine & Foster, 2014) The data in both types is useful when categorizing and is also separate and various such as the items that I work with in a retail store. A qualitative data example would be the different types of pants (Levi, Wrangle, and Calvin Klein). Each pair of pants comes with sizes and colors, but are separate from the other brands and can be judged independently. The Wranglers are not Wranglers just because the others are Levi and Calvin Klein. The Wranglers are Wranglers because they are made differently.

A quantitative piece of data is a piece of data that can be measured numerically. An example of quantitative data is the stock price of different shirt brands such as Fruit of the Loom, Tommy Hilfiger and Calvin Klein. If Calvin Klein shirt's stock price is at seven dollars, Tommy Hilfiger shirt's stock price is at eleven dollars, and Fruit of the Loom shirt's stock price is at four dollars then the data provided about the stock prices are quantitative. The stock price does not change simply because of the stock prices of competitor's stocks. The stock price of a shirt producer changes based of the producers variable data about supply and demand among other pieces of data.

There are four different levels of measurement. Some may say that they are different types of scales as I used to say. Levels of measurement is a much better description as each scale offers different sorting features and as one moves to the other scale the features are kept and other features are gained. The four different scales are nominal, ordinal, interval, and ratio. In the nominal scale the level of measurement is limited to categorization only. In the ordinal scale the level of measurement gains rank and still categorizes just as the nominal scale categorizes. The third level of measurement is the interval scale. The interval scale categorizes and ranks just as the ordinal scale does and it gains the meaning in the difference between the different pieces of data. Absolute zero (no number is less than zero) is not meaningful (or does not appear) until the ratio scale level of measurement is used. Absolute zero is used to “construct a ratio or fraction” (Stine & Foster, 2014)

In business development often times pieces of land are acquired for expansion. There are many pieces of data to analyze when considering to acquire a piece of land or not. The business project and development manager will consider acres of land that have been purchased within the last five years. Considerations will be based on list price of this land and others, square footage, environment of land, community of area, and the different potential clients from the closest neighborhoods. The analysis of this data will allow the manager to make informed decisions on whether to buy the land, negotiate the price, or move to the next piece of land being sold. Quantitative data and qualitative data would be considered in making these decisions. The different quantitative data would answer questions such as list price, square footage, and different potential clients from the closest neighborhoods.

There would also be different scales/level of measurement used on this quantitative data. List price would be weighed on the ratio scale, square footage would be weighed on the interval scale (it could almost be weighed on the ratio scale, and different potential clients from the closest neighborhoods would be weighed on the nominal scale. The qualitative data would answer questions such as environment of land and the community of the area. Qualitative data would answer those questions because those questions require simple label based answers.

In governmental positions held by elected officials there is important data gathered that needs presented in the most favorable way during a campaign. It is not only imperative to present the information in a favorable way, but it is also imperative to gather the data from key areas and fields. Governmental officers need to be very fluent in the language of data so that when representing and caring for the people they are aware of the possible outcomes and current situations in the area. Statistics would provide this information in a very clear way and inform the official of the situations that may be faced. Once a situation is consciously made aware of actions can be taken to either promote or dismantle. Statistics would help decipher whether the situation needs promoted or dismantled and how to best do it. This would be weighed on the ratio scale more than the other scales, however; the data may be processed on the different scales before evolving to a more specific analysis such as interval to ratio. The scales can indeed be similar to the scientific method as different steps along the path. Different steps that a governmental official needs to pay close attention to in order to make the best decisions.

The nominal scale could be easily compared to the observational step in the scientific method as the nominal scale categorizes which is something that is almost done automatically when observing something. The ordinal scale requires more focus than observation and so could be compared to constructing a hypothesis and conducting research. The ordinal scale researches by deciding how and where to rank information. A hypothesis is then formed as ranking is put into place. The interval scale is compared in this angle of thought as testing the hypothesis during an experiment. The interval scale gives meaning to the hypothesis as it is tested because meaning is discovered during this step in the scientific method. The ratio scale is compared to the analysis of and conclusion of data in the scientific process as it is the scale where fractions and ratios are created. When the meaning is discovered in result of the interval scale the conclusion is found in the conclusion of the experiment in the form of a ratio or fraction.

The scientific method is useful to elected officials because they need to make informed decisions on how to carry out a plan and issues that are either potential or current. Hypotheses are made on how to resolve an issue, experiments are done to try and resolve the issue and conclusions are drawn as the area experiences the results of the experiment. This is a very risky process for an elected official where they need the best and most accurate data available and where they need to understand the data most efficiently. This is very hard to do without the language of data, also known as, statistics.

The language of data- statistics- will provide businesses, elected officials, and individuals with the information needed to make decisions and to observe the result of those decisions. For this reason, statistics is very important and useful to everyone. Once it is looked at one might wonder how the world ever progressed without a thorough understanding of statistics. In the end, I am just pleased that we have a thorough understanding of the language today. * References

Stine, R., & Foster, D. (2014). Statistics for Business: Decision Making and Analysis (2nd ed.). Upper Saddle River, NJ: Pearson.