Statistics in Business

Statistics in Business

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Statistics in Business

The use of statistics is important in our everyday lives. Today I will define statistics and explain what makes quantitative data and qualitative data different from each other. I will also evaluate the different types of tables and charts that are used to represent quantitative and qualitative data. The levels of data measurement will be explained, along with understanding how statistics plays a role in the decision-making process for businesses. Finally, I will provide examples of how statistics could be used in business problem solving.

Statistics is used by every person in both their personal lives, as well as, their business lives. “Statistics is the science of collecting, analyzing, presenting, and interpreting data, as well as, making decisions based on analyses” (Mann, 2016, Chapter 1). We are responsible for making decisions every day, both personally and business related, and with statistics we are able to make decisions that are more intelligent. Statistics can be broken into two categories, descriptive and inferential. Descriptive statistics is when data is organized in a way that can be displayed by using tables and graphs. Inferential statistics, on the other hand, is when sample results are used to make decisions and even predictions about a population (Mann, 2016, Chapter 1). Inferential statistics is used more often than descriptive, as we are almost always making decisions or predictions about populations.

Qualitative and quantitative data are the two types of data that are used to differentiate the type of data being collected. The data that is collected is distinguished as variables, which are the “characteristics that assume different values for different elements” (Mann, 2016, Chapter 1). Quantitative data is the numerical data collected to analyze and help come to a conclusion (Albers, 2017). Some examples of quantitative data would be income, the number of cars someone owns, or the price of a home. Qualitative data, on the other hand, cannot assume a numerical value. Some examples of qualitative data would be the make of a car, the gender of people, and undergraduate students. Qualitative data is seen more as categorical since it does not assume numerical values. Qualitative data is used mainly to build theories on. The reasoning for this is because it is more open-ended than quantitative data, thus allowing researchers to generate theory around things that have not yet been investigated or studied (Graebner, Martin, & Roundy, 2013).

There are different types of tables and charts that can be used to represent qualitative data and quantitative data. Qualitative data is best represented by using either a pie chart or a bar graph. A pie chart is most commonly used to show percentages, but it can also be used to show frequencies. A bar graph is a good way to show frequencies, as it has different heights for each category to represent such frequencies. I find pie charts to be the best for percentages, as they can at times be hard to read quickly compared to a bar graph if you are looking to quickly compare data and not look at percentages. Quantitative data is represented with histograms and polygons. A histogram is similar to a bar graph in the way that the data is represented by marking the classes on the horizontal axes and the frequencies on the vertical axes. Histograms can represent frequency, relative frequency, and percentages. Polygons connect a series of neighboring points where each point represents the midpoint to a particular class. I find polygons to be an easy way to evaluate quantitative data, as it is easy to see what is more or higher with the lines that are connecting to each dot on this chart.

The levels of data measurement include nominal, ordinal, interval, and ratio. The first level is nominal; in this level the numbers in the variable are used merely to classify data. This level does not contain any detailed information, as it just shows the numbers, words, or letters. The second level is ordinal, which is used to show ordered relationship between the variable’s observations. Interval is the third level of measurement. The interval level classifies and orders the measurements, while also identifying the distance between each interval on the scale. In this level, there is much more detail in the information being compared. The fourth and final level of measurement is ratio. In the ratio level, the observations and the equal intervals are able to have a value of zero. The properties of the ratio level are similar to the interval level, but the fact that there can be a value of zero is what really sets it apart from the other three levels of data measurement.

Statistics in business are an important aspect in the decision-making process. The reasoning behind this is because managers have goals and targets that they want to reach within the company and for the organization as a whole. With statistics in business, they are able to gather information that can help everyone involved reach said goals by determining the things that need improvement or to determine what may or may not be working. Knowing how to

conduct research and process and analyze collected data is a key element to a business’ success.

Examples:

A restaurant is a good example of a place that uses statistics. Menu items can be categorized to determine what is or is not selling from the menu items. Collecting data allows the restaurant to see what is selling the most compared to what is not selling at all. By doing this, the restaurant is able to decide if certain items should be removed from the menu based on the research and data being provided.

A call center is another good example of statistical use. The manager of the call center requests that each employee track the calls that are coming it so that they are able to determine what calls are being received the most. This tracking also allows for the manager to see when the busiest times of the day are for calls so that they are able to schedule employees efficiently to cover the phones. With this information, the call center is able to lower their waiting time, as well as, find alternative solutions for the people who are calling in for the highest reasoning; this in turn also lowers the call volume.

In conclusion, you can see just how important statistics are in businesses. They help to make decisions based on facts, numbers, and data. With the many business decisions that have to be made on a daily basis, it is crucial that decisions are being made based on statistical data. The use of statistics will continue to grow as it continues to show effective in the way that it helps businesses run better by collecting important data that can truly help to improve the business functions as a whole.

References:

Mann, P. S. (2016). Introductory Statistics (9th ed.). Retrieved from The University of Phoenix eBook Collection database.

Albers, M. J. (2017, April). Quantitative Data Analysis – In the Graduate Curriculum. Sage, 47(2), 215-233.

Retrieved from http://journals.sagepub.com.contentproxy.phoenix.edu/doi/pdf/10.1177/0047281617692067

Graebner, M. E., Martin, J. A., & Roundy, P. T. (2013, August). Qualitative data: Cooking

without a recipe. Sage, 10(3), 276-284.

Retrieved from http://journals.sagepub.com.contentproxy.phoenix.edu/doi/full/10.1177/1476127012452821#articleCitationDownloadContainer

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