代写论文：线性回归模型

代写论文：线性回归模型

经过分析数据，各种因素已经演变，这是重要的考虑。第一个因素是PLE生产的产品在今年头两个季度大部分活跃。另外两个季度，产品销售下滑。 PLE应该注意到这一点，并试图找出背后的主要原因，并找到解决这些问题的办法。
预测未来三年，二零一四年和二零一五年的数据将有助公司为未来公司的发展做好准备。
结果是根据预期，三条曲线（商品成本，销售成本和运营成本）是相同的运动模式，曲折增加，但利息成本与其他几乎是直线的稍微不同。因此，预期的结果也是增加之字形（Bio rad，2014）。
线性回归模型与简单的数据是兼容的，因为我们的数据只是以明确的方式波动。这也节省了分析数据的时间和成本。线性回归技术也很容易理解，因为用户只需要处理一条线而不是处理复杂的曲线。
线性回归技术有一定的缺点。主要缺点是精度系数。一般来说，线性回归技术提供的准确数据较少，因此线性回归技术的准确性系数一般小于多项式技术。
线性回归技术也不适合我们长时间预测数据的目的。随着时间的增加，数据的准确性会下降。当数据突然发生变化时，曲线拟合方法将不适用。对于意外的变化，这个过程可能需要从每年的开始。

代写论文：线性回归模型

After analysing the data, various factors have been evolved which is important to consider. First factor is that the product manufactured by PLE is mostly active in the first two quarters of the year. For the other two quarters the sales of the product goes down. PLE should notice this point and try to find out the main reasons behind this and should find the solutions for these problems.
The data is forecasted for the next three years 2013, 2014 and 2015 which will help the company to prepare the plans for the growth of the company in future.
As the result is according to the expectation, the three of the curves – cost of goods, Sales and operating cost – are of the same moving pattern with increasing zigzag but interest cost was slightly different from others which was nearly a straight line. Thus, the expected result was also an increasing zigzag (Bio rad, 2014).
The linear regression model is compatible for the simple data as our data is simply fluctuating in a well- defined manner. This also saves the time and cost for analysing the data. Linear regression technique is also easy to understand as the user needs to work only on a linear line rather than dealing with complicated curves.
The linear regression technique has certain disadvantages. The main disadvantage is the accuracy coefficient. Generally, the linear regression techniques provide less accurate data, thus, the accuracy coefficient of the linear regression techniques is generally less than the polynomial techniques.
Linear regression technique is also not appropriate for the purpose when we need to predict the data for a long time. As the time increases, the accuracy of the data will decrease. The curve fitting method will not be appropriate when a sudden change in the data occurs. For unexpected changes, the process may need to start from the beginning of each year.