Business decision making part 3

7

Business Decision Making

James Murray

University of Phoenix

March 20, 2017

Business decision making process revolves around four main operations which do not occur automatically. These operations include problem identification, analysis of possible solutions, evaluation of possibilities that are likely to assist in realizing the goal and finally making an informed decision. As a result, it is only for those people who have mastered the four operations approach that often make an informed decision. According to (Anderson et al., 2016), business decisions often require a great deal of research to establish necessary facts and findings that would guarantee an effective decision. In most occasions, entrepreneurs make use of research finding, past experiences and intuition to aid them make business decisions.

Which is the most appropriate approach in amid to making a viable business decision? Therefore, to help address the question, an individual or a company could employ the following strategies to help make correct and effective business decisions. First, the management needs to gather all facts about the company. It is an important strategy because it prevents chances of missing out valuable information that could make a significant difference concerning how a problem should be amicably resolved (Anderson et al., 2016). Secondly, there is a need to focus on the desired results by pondering on what is required and reviewing possible implication. Additionally, it is necessary for a company to consider feedbacks given by experts and conducting researches.

The last assignment that focused on the problem of sales decline experienced in McDonalds, Inc. The company constitutes one of the biggest chains of fast food eateries throughout the world. For example, it offers diverse items such as cheeseburgers, chicken, French fries, milkshakes and pastries among others. Due to the company’s sales decline, it significantly affected its income which formed the main variable for the study. Therefore, the following article focuses on the following; first, it identifies types of descriptive statistics that might be best for summarizing the data, if a sample was to be collected. Secondly, it provides an analysis of types of inferential statistics that might be most useful for analyzing the data obtained from the sample. Lastly, the article analyzes the role probability or trend analysis might play in helping resolve the problem of sales decline in McDonalds, Inc.

Types of descriptive statistics for summarizing the data from a selected sample

The first step in addressing problems in business and making decisions based on evidence involves collection of accurate data from a sample. The data is summarized and presented in a way that it can best be used to resolve an issue. Descriptive statistics is often applied in social statistics to present quantitative descriptions in a simple and manageable form (Gravetter & Wallnau, 2015). For example, it helps in simplifying large amounts of data in a reasonable way by reducing lots of data into a manageable summary. In the analysis of the issue of sales decline in McDonalds, Inc., the income realized by the company that it influences is chosen as the quantitative variable. It means that the company’s income depends on the volume of sales it makes during a specified sales period.

Therefore, types of descriptive statistics that best for summarizing the data collected from a sample of the company’s income records are illustrated as follows; first, there is the use of mean which a type of descriptive statistics. For mean, it is a measure of central tendency that uses a single value to describe the center of a data set. Therefore, the mean income is calculated to tell the average income that the come has achieved during its sales period faced with sales decline. The second type of descriptive statistics appropriate for summarizing the data from the chosen sample includes the variance. Variance is a measure of the average distance that each set of data deviates from the mean (Anderson et al., 2016). It is useful in observing how spread out the company’s earnings shift from the average income which constitutes a central value of all earnings realized.

By using variance as a measure of dispersion, it possible to tell whether deviation of all incomes from the average income is either significantly or insignificantly different. For example, if it is established that it is significantly different then it forces the company to establish and implement appropriate measures that would remedy the issue of sales decline within the shortest time possible (Anderson et al., 2016). Additionally, it also important to use range to determine the level of change of the organization’s income as impacted by the decrease in sales quantities. Range is the simplest measure of dispersion that tells how the data is spread out. For example, by obtaining the difference between the lowest and the highest income earned. If the difference is a big value then it shows that there is a wide spread out in the company’s earnings.

Types of inferential statistics best for analyzing the data from selected sample

Inferential statistics are typically differentiated from descriptive statistics as follows; with inferential statistics, an individual aims at making conclusions that goes beyond the sample data considered. For example, it is used help decide using the summary obtained with use of descriptive statistics what the entire set of data may imply. Also, inferential statistics could be used to judge of the likelihood that the difference obtained between different groups forms an association between the variables considered (Gravetter & Wallnau, 2015). Therefore, inferential statistics is mostly applied to infer from the selected sample data to make a general conclusion that indicate the actual situation. For example, by studying the trend of various income figures in the sample, it is possible to tell what exactly takes place for the entire income figures realized.

The most appropriate types of inferential statistics include the Analysis of Variance (ANOVA) and regression analysis. In this scenario, variable of concern are the quantity of sales and income all of which are quantitative variables thus the need to use these two inferential statistics. First, according to (Anderson et al., 2016), using the analysis of variance makes it possible to use the descriptive statistics such as mean to test if the sales volume is a significant predictor of the company’s income. However, conducting a regression analysis helps to predict the company’s possible income at predetermined sales volume. The analysis helps the company make business decisions regarding its expected income and thus making proper budgets.

The role probability or trend analysis might play in helping address the sales decline

Probabilities refer to numbers that indicate the possibility that a given situation or event will take place while trend analysis means the process of making comparisons involving business data over a specified period to establish regular outcomes(Gravetter & Wallnau, 2015). As a result, probabilities or trend analysis is useful especially for business improvement and resolving a problem as follows. First, trend analysis helps in developing a strategy to resolve the problem encountered in the business such as the decrease in the sales made over a given period. For example, by conducting trend analysis regarding the company’s income earned, it will help in understanding how performs and estimating where McDonalds, Inc. could be considering its current offerings and practices.

Finally, the best strategy for McDonalds, Inc. involves focusing on the four main operations found in the business decision making process. They include problem identification, analysis of possible solutions, evaluation of possibilities that are likely to assist in realizing the goal and finally making an informed decision. In addition to these operations, analysis involving both descriptive and inferential statistics would help the company determine the actual trends regarding the issue of sales decrease that affects its income earnings.

References

Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2016). Statistics for business & economics. Nelson Education.

Gravetter, F. J., & Wallnau, L. B. (2015). Statistics for the behavioral sciences. Cengage Learning.