Regression Analysis: North-South Airlines Case Study

Introduction

Regression analysis refers to a predictive statistical method that focuses on evaluating the relationship between a dependent variable and the independent variable (s). The dependent variable refers to the target factors that the study is focusing on understanding. On the other hand, the independent variable refers to the element that the study has hypothesized to have a significant impact on the target element (dependent variable) (Foley, 2018). Regression is often used for forecasting, time series modeling, and identifying cause relationship between variables. Therefore, regression analysis is a very significant tool that help organizations to analyze data and identify relevant relationships (Foley, 2018). In this case, the paper focuses on determining the effectiveness of regression analysis in managing data given the case of North-South Airlines. Through the regression model, the management can acquire an understanding of the maintenance costs of the Boeing 727-300 Jets between 2001 and 2007. In understanding this problem, the discussion will cover the various types of regression, its key applications, and the development of an appropriate forecasting model for the company.

Is your time best spent reading someone else’s essay? Get a 100% original essay FROM A CERTIFIED WRITER!

Save 25%

Analysis

In conducting the regression analysis to understand the data on Boeing 727-300 Jets, North-South Airlines can use various models. These techniques depend on three key metrics including the shape of the regression line, the number of independent variables, and the nature of the dependent variables (Ray, 2015).

Linear Regression

Linear regression is the most common and widely known technique that can be preferable in analyzing the data in this case. In this technique, the nature of the dependent variable is continuous while the independent variable (s) can be either discrete or continuous. However, the nature of the regression line is linear. In establishing the relationship between the variables, the linear regression analysis takes the form of a regression equation which occurs in the form of y = a + bx + e (Ray, 2015). In this equation, y stands for the dependent variable, a stands for a constant, b is the slope of the equation, x is the independent variable, and e stands for an error.

The linear regression can either entail a single independent variable or many independent variables. In the case study, the data entails multiple variables, and therefore the best technique that can be applied in making a prediction is the multiple linear regression (Schneider, Hommel & Blettner, 2010). The multiple regression is a combination of the various independent variable, in this case, it can be the airframe cots per aircraft, the engine cost per aircraft, and the average age in hours (Render, Stair, & Hanna, 2012). In this case, the dependent variable is the maintenance cost. The multiple regression equation is given as follows (Boston University School of Public Health, 2019):

Y = v0 + v1X1 + v2X2 + v3X3 + e

Where:

• Y is the dependent variable
• v0 is the intercept (constant value)
• X1, X2, and X3 are the independent variables
• v1, v2, and v3 are the 1st, 2nd, and 3rd are the coefficients of the respective independent variables.

In using the multiple linear regression, it is worth noting that it is highly sensitive to outliers and therefore can have a great impact on the forecasting equation and the predicted values as well (Ge, 2009).

Solution

Regression analysis is beneficial in helping the company to understand the degree to which independent variable influence the dependent variable. In doing so, an organization can use this technique to understand various trends within the operational activities by utilizing the historical data therein (Schneider, Hommel & Blettner, 2010). It is evident that the model accrues a wide range of benefits to the company and more importantly, upon understanding the trends, it helps to devise action strategies that are beneficial to the company. For example, after understanding the correlation or rather the relationship between the dependent and the independent variables, the management can identify areas that need improvement. This s that the model is highly useful is forecasting, planning, and making decisions for the future (NewGenApps, 2017). For instance, the model can help North-South Airlines to assess the data between 2001 and 2007 and in doing so, it can determine how the engine cost, the cost of the airframe, and the age of the aircraft influence the total maintenance costs of the B727-300. This may help to identify the specific variable that contributes greatly to the total costs and hence devise a strategy to modify or enhance the variable such that it minimizes the total expense therein. Furthermore, the goal of each company is maximizing profits by minimizing costs (Render, Stair, & Hanna, 2012).

The multiple linear regression analysis is therefore majorly used for predictive analysis. By analyzing the present trend on the available data, the company can use the equation to forecast future values. This helps to determine the future benefits, risks, and opportunities that may influence organizational effectiveness (NewGenApps, 2017). It is also worth noting that in the contemporary world, businesses are getting overloaded with raw data especially in terms of financials, operations, and customer information. Using raw data solely can be burdening and sometimes not reliable. Therefore, regression analysis has become a vital tool in making informed decisions. The workers that usually handle these areas of data can now integrate the scientific angle in managing this data is a beneficial way. It means that by learning the various data analytics, businesses are now use regression analysis to make more actionable, smarter, and accurate decisions (Yu & Chen, 2015). Also, it does not mean that the regression analysis is the only beneficial tool but it requires creativity and analytical thinking from the involved team.

Justification

The multiple linear regression models is beneficial in analyzing the data given for North-South Airlines because the data encompasses multiple independent variables. This method is also less cumbersome and is more accurate in predicting future values (Palmer & OConnell, 2009). It is also evident that the multiple linear equations fits the problem at hand as follows:

Y = v0 + v1X1 + v2X2 + v3X3 + e

Where:

• Y stands for the maintenance costs
• v0 stands for the intercept (constant value)
• X1, X2, and X3 stands for the airframe cots per aircraft, the engine cost per aircraft, and the average age in hours respectively
• v1, v2, and v3 are the 1st, 2nd, and 3rd are the coefficients of the respective independent variables (Render, Stair, & Hanna, 2012).

Summary

In the analysis above, it is evident that the regression analysis is beneficial in making solid decisions within a given company. By analyzing the given data for the Boeing 727-300 Jets between 2001 and 2007, Peg Jones can understand the trend of the maintenance costs and therefore provide an accurate response to Stephen Ruth. Using the data and the multiple linear regression model provided above, it can help the top managers to develop a strategy that not only helps to minimize costs but also enhance the effectiveness of the entire organization. It is also beneficial in achieving both the short-term and the long-term value maximization of the firm by forecasting and planning for future trends.

References

Boston University School of Public Health. (2019). Multiple Linear Regression Analysis. Retrieved from http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Multivariable/BS704_Multivariable7.html

Foley, B. (2018). What is Regression Analysis and Why Should I Use It?. Retrieved from https://www.surveygizmo.com/resources/blog/regression-analysis/

Ge, Z. (2009). Effectiveness of the T-test in Multiple Linear Regression Modeling of Environmental Systems. Environmental Engineering Science, 26(2), 377-384. doi: 10.1089/ees.2008.0014

NewGenApps. (2017). 5 Applications of Regression Analysis in Business. Retrieved from https://www.newgenapps.com/blog/business-applications-uses-regression-analysis-advantages

Palmer, P. B., & OConnell, D. G. (2009). Research Corner: Regression Analysis for Prediction: Understanding the Process. Cardiopulmonary Physical Therapy Journal, 20(3), 23-26. doi: 10.1097/01823246-200920030-00004

Ray, S. (2015). 7 Types of Regression Techniques you should know. Retrieved from https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/

Render, B., Stair, R. M., & Hanna, M. E. (2012). Quantitative analysis for management. Upper Saddle River, N.J: Pearson Prentice Hall.

Schneider, A., Hommel, G., & Blettner, M. (2010). Linear Regression Analysis. Deutsches Aerzteblatt Online, 107(44), 776-782. doi: 10.3238/arztebl.2010.0776

Yu, R., & Chen, L. (2015). The need to control for regression to the mean in social psychology studies. Frontiers in Psychology, 5. doi: 10.3389/fpsyg.2014.01574