Statistics for Managers: Final Paper

BUS 308 Statistics for Managers

**Statistics for Managers: Final Paper**

Although there are many that consider statistics to be an unnecessary or unrealistically difficult concept to perform, it is an extremely important tool to consider. When using statistics, one may just see numbers on a sheet of paper that show unclear or unresolved conclusions. However, when one really studies the impact of what these statistical numbers show and what the results actually mean, businesses and consumer futures are positively impacted by these analyses. Statistics is defined as the a “branch of mathematics concerned with collection, classification, analysis, and interpretation of numerical facts, for drawing inferences on the basis their quantifiable likelihood” or probability (Business Dictionary Statistics, 2018). The data analyses convert raw data from into meaningful information that can be used to benefit the future. Through this statistics course, many studies have been completed using Excel to help further the understanding of the importance of statistical analyses in business. This paper will discuss the following and how each of these topics have played a key role in the development of statistical analyses that will promote real life experience: descriptive statistics, inferential statistics, hypothesis development and testing, selection of appropriate statistical tests, and evaluating statistical results.

**Descriptive Statistics**

Descriptive statistics is easily defined as “brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire” population, or just a given sample (Staff, 2018). To simplify this definition, descriptive statistics is a data analyses and summary of the data provided within a market. Furthermore, this analysis is processed in hopes that there may be patterns that may be identified from the data processed. This analyses and summary help the person that is statistically calculating the results to form a good understanding and identify any patterns in the data and in the work that can be further analyzed. These short summaries of the data include mean, median, mode, and standard deviation which are used in many statistical analyses. This information is imperative to have access to not just because of the overall summary that is provided by the information, but also because it is used in many formulas. Without this information and understanding, further analyses and understanding of a given population would be very difficult to understand, if not impossible.

**Inferential Statistics**

It is important to understand that the population often being measured is in representation of a larger group. For example, in statistics if one wants to measure the test results of all fifth graders within the United States, this would be a huge amount of data to collect. In statistics, one will gather the test result information from fifty fifth graders which will then be a representation of the entire country. Inferential statistics are “techniques that allow us to use these samples” of fifty students, instead of an enormous amount, “to make generalizations about the populations from which the samples were drawn” (Descriptive and Inferential Statistics, 2018). Because of this population size being so much less than that of the number of actual people, it is important that the sampling is done effectively so that it accurately represents the entire population. However, it is also important for statistical analyses and the people processing this analysis to understand that there is a margin of error that can occur due to the random sampling of information. The “sample is not expected to perfectly represent the population” which must be considered when referencing the results of the statistical analysis (Descriptive and Inferential Statistics, 2018).

**Hypothesis Development and Testing**

The hypothesis development stage is extremely important as it ensures that the statistical and mathematical analyses has a standardized decision that can be made. This assures that there is in fact a study to be processed and that the analysis will provide some sort of decision at the end of the testing. In the lectures provided within this course, it outlines a six-step procedure for hypothesis development and testing. Those steps are: state the null and alternate hypothesis, select a level of significance, identify the statistical test to use, state the decision rule, perform the analysis, and then interpret the results. Although this six-step procedure may differ from analysis to analysis, the steps are always similar. In the creation of the hypothesis, it is important to consider that there is a ‘Ho,’ null hypothesis and a ‘Ha,’ alternate hypothesis. Each of these are always the exact opposite of each other, and if the ‘Ho’ is not true, then the ‘Ha’ must always be true.

**Selection of Appropriate Statistical Tests**

This is a very important aspect of the statistical analysis process. If the appropriate tests are not processed, then the results will be inaccurate and lead the test results to be skewed. This will then be problematic if this test is being processed in order to determine necessary steps that a company must take in order to be more profitable. This is considered a confusing task for many, but the easiest way in which to determine what test should be completed is to define the level of measurement for each of the variables that are included in the statistical analyses being processed; examples may include: measuring males and females, rank order, houses or apartments, etc. After this is completed, the analyst must then determine what that person is trying to find out after the completion of the measurements. This is to determine the differences in the relationships. Something to keep in mind is that if the questions being asked are in regards to relationship with interval, ordinal level, or ratio level, the most common analyses that should be used is Spearman and Pearson correlation. Another area to consider is the size of the sample that is being analyzed. “The basic rule of thumb are” that “participants per group for a t-test, chi-square, correlation, or linear regression with two predictors” (How to Select the Appropriate Analysis, 2015).

**Evaluating Statistical Results**

It is extremely important to evaluate the data after the statistical collection and the statistical analysis has been completed. This is so important as it allows for a check of the information that was studied to determine the validity of the information used. To evaluate the data that was used and ensure that it was relevant to the sample, it must be evaluated and calculated within the statistical parameters. The mean and standard deviation must be calculated, and the p-value must be identified (this shows the whether the ‘Ho’ is valid). This is all completed by the analyst selecting the statistics tool that will in fact be the correct tool to use for the appraisal.

**Conclusion**

In statistics, it is imperative for the steps to be followed, for the correct processes to be taken by the analyst, and for the information to be summarized to determine the outcome. This paper has discussed the following topics and how each play a critical role in the development of a successful statistical analyses: descriptive statistics, inferential statistics, hypothesis development and testing, selection of appropriate statistical tests, and evaluating statistical results.

**References**

Business Dictionary – Statistics. (2018). Retrieved from http://www.businessdictionary.com/definition/statistics.html

Descriptive and Inferential Statistics. (2018). Retrieved form https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php

How to Select the Appropriate Analysis. (2015, April). Retrieved from https://www.statisticssolutions.com/how-to-select-the-appropriate-statistical-analysis/

Staff, I. (2018, October). Descriptive Statistics. Retrieved from https://www.investopedia.com/terms/d/descriptive_statistics.asp