回归分析在交通物流行业的应用。回归分析用于评估对业务的影响。这是通过讨论各种回归模型实现的。讨论了回归模型之间的差异。然后，对所选企业Clipper Logistics Plc进行多元回归和多项式回归。最后，对线性回归中的伦理问题进行了识别和分析，并提出了相应的对策。
The residual analysis could also be conducted to evaluate whether the assumptions of the linear regression is met in context of the undertaken business or industry case. The residual plot is the regression graph which basically shows the difference between the observed value and the predicted value of the dependent variable. Therefore, if the points in the residual plot are dispersed randomly, the linear regression model in that case could be termed as appropriate for the case undertaken.
Alternative diagrammatic view
As the potential alternatives, the stem and leaf view, normal probability plot of the residual could be the potential remedy to avoid the ethical issue on linear model of regression as these tools are capable to uncover the probable non-normality.
Testing the significance of the regression coefficients is another potential option to address the issue in linear regression in case there is no such evidence of assumption breach. Through construction of the confidence interval and prediction interval, the inappropriate application and inefficient implementation of the linear regression without proper knowledge and skills could be neglected.
The application of regression is carried out in the industry of transport and logistics. The regression analysis is done in gauging the impact on the business. This is done with the discussion of various regression models. The difference of the regression models among each other is discussed. Then, with respect to the chosen firm, Clipper Logistics Plc, the multiple regressions and the polynomial regression are applied. In the last part, ethical issues with respect to the linear regression has been identified and analysed along with remedy of the same.