Statistics Power Point Presentation

Business Decision Making Part Two

QNT275

Descriptive statistics are used in the presentation of data sets in form of meaningful summaries. This helps for the important patterns of the data to be observable. Descriptive statistics may not be useful in drawing final conclusions about the data. These statistics are majorly used in describing quantitative data as they involve numerical calculations. The main descriptive statistics are the measures of central tendency and the measures of variability. Measures of central tendency express the central position of a data. Measures of variability, on the other hand, represent spread of the values in a data set.

For the case of American Airline Group, the research involves both quantitative and qualitative data. The operational costs, which represent the dependent variable, can be understood by studying the operational changes that result from the merger. This includes quantitative data on the number of passengers that have access to the airline’s services. The descriptive statistics which could be used in summarized this data include the mean, mode, and median. The mean represents the average number of passengers using the airline’s services for a given period of time, for example in one day. The mode represents the most recurrent number in the data set. For example, if the data were to be collected for a period of one month, the particular number of passengers that would be recorded in many days would represent the mode of the data. Median, on the other hand, represents the centrally placed value after the data has been arranged in either an ascending order or descending order. Descriptive statistics could also be used for summarizing data on the financial capability of the merger. This data is obtained from a survey audit of the airline’s financial data. The measures of variability that could be used in this research include the range, variance, standard deviation, quartiles and absolute. These measures describe the consistency of data by presenting the variability (Holcomb, 2017).

Inferential statistics involve making generalizations about the population using facts from the sample. These statistics are useful where the population under study is large. In this case, it is most feasible to select a small group to be a representative of the population. The inferential statistics that can be used in the analysis of the data from the American Airline Group merger include the estimation of parameters and tests of hypothesis. Estimation of parameters involves approximating population parameters using the calculated sample statistics. For example, the population mean may be estimated by the sample mean. It would be economical to study only a small group. Estimation of parameters is thus highly useful for making inferences about the whole population (Bernstein, & Bernstein, 2011).

Test of hypothesis involves testing the accuracy of a claim about the population based on the sample under study. Other inferential statistics applicable to this research are correlation testing and regression analysis. These involve testing the relationship between variables. They would be applicable in this research to identify how the operating costs are related to the financial capability of the merger. Inferential statistics can also be used to analyze qualitative data. The qualitative data collected in this study include customers’ opinions on how the new airline merger has changed their choice of travel. A positive correlation between the operating costs and financial capability would imply that the merger, which has a higher financial capability compared to the individual companies, would have eased airline operations. This can be explained by the introduction of high-capacity planes as well as cost-efficient fuel (Bernstein, & Bernstein, 2011).

Inferential statistics involve estimation of population parameter using the selected sample. This means that the results are not always 100% accurate. Inferential statistics involve probability. The population parameters are estimated to lie within a given confidence interval with the stated confidence level. A confidence is an interval within which the population parameter is expected to lie with a certain confidence level. To increase the probability of getting accurate estimates, the sample should be a true representation of the population. This means that population individuals are well represented in the sample. This can be done through the simple random sampling for selecting the sample for the study. Random sampling ensures that each subject in the population has an equal chance of being selected. When sampling for the customers to be included in the qualitative survey, customers should be selected at random. The size of the sample should be large enough, depending on the size of the population (Sahu, 2015).

Linear regression is a way of identifying the linear relationship between two variables in a study. Linear regression would be useful in the case of the American Airline Group as it would help in the identifying the relationship between operational cost and financial capability. Linear regression involves two variables; the dependent variable and the independent variable. In the case of the American Airline Group, the dependent variable is the operational cost while the independent variable is the financial capability of the merger company. It is expected that as a result of merger formation, the financial capability of the new company is higher than that for the individual companies. This would also increase the operational efficiency of the new business. Linear regression can be used in trend analysis. Trend analysis is the analysis of the change in a data set within different times. After collecting and analyzing data at different times, the results are compared after which the trend can be observed (Weisberg, 2014).

Time series is used to study the variations in data over a given period. Time series requires that data be collected at more than one points and the direction in which the data may be shifting identified. This helps the company to make necessary plans towards the success of the business. The American Airline Group can use time series to analyze customer satisfaction from the services offered by the new merger. This would require that the company collects data on customer satisfaction in different times. If the degree of satisfaction seems to be decreasing, the company may respond through the introduction of new services. The company can also use time series to analyze the trend in the number of customers using its services. To respond to the new trend, the company may initiate new advertisement plans (Brockwell, & Davis, 2016).

ReferencesTop of Form

Top of Form

Bottom of Form

Bernstein, S., & Bernstein, R. (2011). Elements of statistics II: Inferential statistics. New York: McGraw Hill Professional.

Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting. Cham: Springer.

Holcomb, Z. C. (2017). Fundamentals of descriptive statistics. London: Routledge.

Sahu, P. K. (2015). Estimation and inferential statistics. Place of publication not identified: Springer.

Weisberg, S. (2014). Applied linear regression.