Business Decision Making Pt. 2

Business Decision Making Pt. 2

QNT/275

Business Decision Making Pt. 2

Statistics is when you study the collection, analysis, interpretation, and organization of data. When analyzing data, such as the number and type of calls coming into a call center, you would want to use both descriptive and inferential statistics. When research is being conducted on groups of people, it is common to use descriptive, as well as, inferential statistics to analyze the results and draw conclusions off of.

Descriptive statistics is “the method for organizing, displaying, and describing data by using tables, graphs, and summary measures” (Mann, 2016, Chapter 1). Descriptive statistics is not used to make conclusions beyond the data that is being analyzed, as it is rather just a way to present or describe the data. This is important when it comes to statistics because it allows for a visual representation of data that is being presented, so it makes it easier to see and understand the data being presented. When looking at the types of calls that are being received in the call center, I feel that a pie chart would be a good way to present this data. The reasoning for this is because it would be a good way to show the collected data in a way that is easy to understand, as well as, clearly see the differences between the types of calls being received. A bar graph would also be a good way to represent this data, as you can clearly see the difference in the heights of each bar on the graph. I think that in this scenario, a bar graph would be a good way to represent the numbers of calls that are being received based on the day of the week. This would be a good way to represent which days are receiving the most calls to ensure that the staffing needs are being met during the busier days or times.

Inferential statistics are the “methods that use sample results to help make decisions or predictions about a population” (Mann, 2016, Chapter 1). The point of inferential statistics is to make decisions or draw conclusions about the population that a sample is drawn from. With inferential statistics, you are almost always going to be using estimation, as you are estimating what the population mean and population proportion are for a single population. You can’t really have statistics without having estimating, as you are always estimating what the outcome of the data will be and then testing your hypothesis. In the call center, you would estimate the number of calls that would be received daily or even weekly, as well as, estimate the types of calls that are going to come in. Along with this, you could also estimate what percentage of the calls are going to be in each different category for the reason of the calls. Regression is also important when it comes to inferential statistics, as this is when you are making a prediction of an outcome based on a predictor variable. This would be where we would estimate the relationship among the variables. You would then want to use ANOVA, which is where we analyze the variables and determine how they compare amongst each other.

Trend analysis is when you examine trends to identify major variations that may take place within those trends. Within business, trend analysis is useful in determining the areas that are performing best to allow that business to try and replicate this to create high performance repeatedly. With trend analysis, you are also able to look at the areas that are underperforming, thus allowing the business to either distribute more resources to that area, or determine the type of resources necessary to improve upon. With trend analysis, the director of the call center, along with the vice president, are able to see that calls have gotten significantly higher so that they are able to come up with a solution for this problem. With the new phone system in place, they will then be able to see how the call volume will not seem as high, as the calls will be routed to the correct employees in the call center to assist the members. Linear regression is also used in this scenario by analyzing the relationship between the number of calls and the types of calls coming in. This allows the executives to determine how many employees need to be put into each phone queue category, based on this information.

A time series is going to be used to identify patterns. In the call center, this will be the way that the executives are able to see what days are receiving the most calls, and even what types of calls are coming in the most. When looking at a time series, it is able to show how many more calls have come in within a two-month time frame, or even within a four-month time frame. This allows the executives to see what the trends are among the calls. With this data, we would be able to determine how many employees need to be in each phone queue, as well as, determine the types of phone queues that should be offered.

In conclusion, you are able to see just how important inferential statistics and descriptive statistics are to a business. You are also able to see how important it is to look at trend analysis and the role of a time series. Collecting and analyzing the data of the call center with each of the methods listed will be crucial in determining exactly how the new phone system will work and benefit the members, as well as, the employees and credit union as a whole.

REFERENCES:

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