代写文章-残差分析

代写文章-残差分析

残差分析还可以用来评估线性回归的假设是否满足所进行的业务或行业案例的上下文。残差图是回归图,基本表示因变量的观测值与预测值的差值。因此,如果残差图中的点是随机分布的,那么这种情况下的线性回归模型就可以称为适合所进行的情况。

替代图解视图

残差的正态概率图作为潜在的替代方法,即茎叶观,能够揭示可能的非正态性,是避免线性回归模型伦理问题的潜在补救措施。

测试的意义

检验回归系数的显著性是线性回归中另一个潜在的解决问题的方法,以防没有这样的证据表明假设违背。通过构建置信区间和预测区间,可以忽略没有适当知识和技能的线性回归的不恰当应用和低效实现。

结论

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。

代写文章-残差分析

Residual analysis

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.

Significance testing

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.

Conclusion

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.