Business Decision Making Part Two: Descriptive Statistics

Business Decision Making Part Twoqnt275descriptive Statistics Are Used

Business Decision Making Part Two QNT275 Descriptive statistics are used in the presentation of data sets in the form of meaningful summaries. This helps make the important patterns of the data observable. Descriptive statistics may not be useful in drawing final conclusions about the data. These statistics are mainly used in describing quantitative data as they involve numerical calculations. The main descriptive statistics are measures of central tendency and measures of variability.

Measures of central tendency express the central position of a data set. Measures of variability, on the other hand, represent the spread of the values in a data set. In the case of American Airline Group, the research involves both quantitative and qualitative data. Operational costs, which are the dependent variable, can be understood by studying operational changes resulting from the merger, including quantitative data on the number of passengers accessing the airline’s services.

The descriptive statistics which could be used to summarize this data include the mean, mode, and median. The mean represents the average number of passengers in a given period, such as one day. The mode indicates the most recurrent number in the data set. For instance, if data were collected over a month, the most frequently recorded passenger number across days would be the mode. The median signifies the middle value when data are ordered ascendingly or descendingly.

Descriptive statistics can also summarize data on the financial capability of the merger, obtained from surveys and audits of the airline’s financial data. Measures of variability like range, variance, standard deviation, quartiles, and absolute deviation describe data consistency and variability. Inferential statistics involve making generalizations about the population based on sample data.

These statistics are particularly useful where the population is large. In this context, a small, representative sample can be used for analysis. Inferential statistics include the estimation of parameters and hypothesis testing. Estimating parameters involves approximating the entire population’s characteristics using sample data, such as estimating the population mean from a sample mean (Bernstein & Bernstein, 2011). Hypothesis testing evaluates the validity of claims about the population based on sample evidence.

Other applicable inferential techniques are correlation testing and regression analysis, which explore relationships between variables. For example, analyzing how operational costs relate to the financial capability of the merger. Inferential statistics can also analyze qualitative data, such as customer opinions on changes post-merger. A positive correlation between operational costs and financial capability would suggest that the merger, with higher financial capacity, may have eased operations through factors like high-capacity planes and fuel efficiency (Bernstein & Bernstein, 2011).

Inferential statistical results involve probability and are expressed with confidence intervals within which true population parameters are expected to lie, with a given confidence level. To improve accuracy, sampling must be representative of the population, which can be achieved through simple random sampling, ensuring each individual has an equal chance of selection (Sahu, 2015). Random sampling applies to both quantitative and qualitative data collection.

Linear regression models the relationship between a dependent variable and an independent variable, which is useful for this study. In the case of the American Airline Group, the dependent variable is operational costs, and the independent variable is financial capability. The model can identify how increases in financial resources due to the merger impact operational efficiency and costs. Regression analysis contributes to trend analysis, helping forecast future changes based on historical data (Weisberg, 2014).

Time series analysis examines data collected over time at multiple points, identifying trends and patterns. For example, the airline can analyze customer satisfaction or customer usage trends over successive periods, enabling preemptive strategic responses like introducing new services or adjusting marketing efforts. The use of time series helps in planning and decision-making based on observed shifts over time (Brockwell & Davis, 2016).

Paper For Above instruction

In the context of business decision-making, understanding and applying statistical tools is crucial for interpreting data accurately and making informed strategic choices. Descriptive and inferential statistics serve as foundational techniques that enable managers and analysts to summarize complex datasets and derive meaningful insights. This essay discusses the significance of these statistical measures in analyzing the operational and financial dynamics of the American Airline Group merger, emphasizing how these tools facilitate better decision-making.

Descriptive statistics provide a way to encapsulate large datasets into understandable summaries. Measures of central tendency—mean, median, and mode—are vital in understanding typical values within data related to passenger numbers and financial metrics. For instance, calculating the mean number of daily passengers can reveal operational capacity utilization, aiding in resource planning. The median offers insights into the central value, especially when data is skewed, such as with occasional surges or drops in passenger numbers. The mode indicates the most common passenger count, highlighting recurrent operational patterns. Together, these metrics enable airline managers to comprehend current performance and identify areas needing operational adjustment.

Measures of variability, such as range, variance, and standard deviation, depict the dispersion of data points around the central measure. In airline operations, understanding variability in passenger counts or costs can help assess stability and predictability. High variability might imply fluctuating demand, necessitating flexible scheduling and resource allocation. Low variability suggests consistent performance, supporting bulk scheduling and cost forecasting. Quartiles and interquartile ranges further facilitate understanding data distribution, helping in identifying anomalies or outliers that could distort operational planning.

Beyond descriptions, inferential statistics extend analysis by enabling generalizations from small samples to entire populations. Estimating parameters like the mean operational cost or passenger volume involves calculating sample statistics and extrapolating to the full airline data. Confidence intervals provide a range within which the real population parameters are likely to reside, giving managers confidence in their strategic decisions. Hypothesis testing further allows assessment of managerial assumptions, such as whether the merger significantly reduced operational costs or increased revenue, based on sample data analysis.

Regression analysis, particularly linear regression, is a powerful inferential tool used to elucidate relationships between variables. In the case of the airline merger, the primary concern is understanding how financial capability impacts operational costs. By modeling operational costs as a function of financial resources, regression analysis quantifies the strength and nature of this relationship. A positive coefficient indicates that increased financial capacity leads to reduced operational costs through economies of scale, fuel-efficient aircraft, and streamlined operations. This information guides strategic decisions on resource allocation and merger investments.

Correlation analysis complements regression by revealing whether a statistically significant relationship exists between operational costs and financial capability, without necessarily implying causation. Recognizing such correlations helps in identifying leverage points that can be targeted to optimize airline performance. For example, a strong negative correlation suggests that as financial capacity grows, operational costs decrease—empowering senior management to prioritize resource enhancements.

Time series analysis further supports strategic planning by examining data trends over time. By analyzing customer satisfaction scores or passenger numbers across successive periods, the airline can identify patterns indicating rising or falling trends. For instance, a declining trend in customer satisfaction might prompt a review of service quality, leading to targeted improvements. Conversely, increasing passenger numbers can motivate capacity expansion or marketing efforts. Trend forecasts derived from time series analysis enable proactive adjustments, ensuring competitive advantage in the dynamic airline industry.

In conclusion, both descriptive and inferential statistical tools are essential in interpreting operational and financial data within airline mergers. Descriptive statistics facilitate understanding current performance levels, while inferential techniques enable predictions and strategic testing hypotheses. Together, they support evidence-based decision-making, optimize resource utilization, and enhance overall organizational effectiveness. As airline companies face increasing competition and fluctuating demand, the mastery and application of these statistical methods are critical for sustained success and growth.

References

• Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting. Springer.
• Bernstein, S., & Bernstein, R. (2011). Elements of statistics II: Inferential statistics. McGraw Hill Professional.
• Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment in social science research. SAGE Publications Ltd. doi:10.4135/
• Holcomb, Z. C. (2017). Fundamentals of descriptive statistics. Routledge.
• Sahu, P. K. (2015). Estimation and inferential statistics. Springer.
• Reed, T., & Reed, D. (2014). American airlines, US airways and the creation of the world's largest airline. Retrieved from [URL]
• Weisberg, S. (2014). Applied linear regression. Springer.
• Domjan, M. (2005). Pavlovian conditioning: A functioning perspective. Annual Review of Psychology, 56(1).
• Terry, W. S. (2009). Learning and memory: Basic principles, processes, and procedures. Allyn and Bacon.
• Additional sources to support the discussion and data analysis techniques may include scholarly articles on airline mergers, statistical analysis in business, and recent industry reports.