加拿大论文代写：线性回归模型

加拿大论文代写：线性回归模型

两种方法都有各自的优缺点。线性回归模型应该在数据连续不断地增加或减小的情况下使用，在线性基础上形成线性模型，当数据非常复杂且不能以线性模式绘制时，逻辑回归模型是合适的。因此，线性回归模型更适合于不那么复杂的数据，而logistic回归模型更适合于复杂的数据(业务统计，2010)。

或者，如果线性回归模型的精度系数与logistic回归模型的精度系数相差很小，或非常小，则应以精度系数的形式表示。然后使用线性回归模型，但如果存在较大差异，则应使用logistic系数。

给定数据集的定量方法

通过分析给定的数据集,可以说,线性回归模型是适合给定的数据集。因为它可以说精度系数线性回归模型在商品成本是0.9657 0.9939逻辑回归模型,同样,操作成本分别为0.8585和0.8839,利息成本分别为0.8974和0.9006,和销售分别是0.9653和0.9940。因此，线性回归定量模型适用于给定的数据集。

统计分析

对给定数据的统计分析如下:

数据的依赖性问题

在给定的数据中，数据依赖是一个问题，因为数据不是完全依赖的。提供数据的实体不是固定的。数据可能有出入。数据也很波动。我们来看看利息成本。利息成本的曲线不是很波动，但它没有依赖性。

加拿大论文代写：线性回归模型

Linear Regression Model vs. Logistic Regression model
Both methods have their own advantages and disadvantages according to the needs. Linear regression model should be used where the data are continuously increasing or decreasing by forming on a linear basis, which forms a linear look while the logistic regression model is appropriate when data is very complicated and can’t be drawn on a linear pattern. Thus, the linear regression model is more appropriate for the less complicated data and logistic regression model is more appropriate for the much complicated data (Business Statistics, 2010).
Or, it should be expressed in the form of accuracy coefficient if there is negligible or very small difference between the accuracy coefficient of the linear regression model and logistic regression model. Then linear regression model should be used but if there is a large difference then logistic coefficient should be used.
Quantitative method for the given data set
By analysing the given data set, it can be said that the linear regression model is appropriate for the given data set. As it can say that the accuracy coefficient for linear regression model in Cost of goods is 0.9657 while of logistic regression model is 0.9939, same way, for the operating cost it is 0.8585 and 0.8839 respectively, for Interest Cost it is 0.8974 and 0.9006 respectively, and for the sales it is 0.9653 and 0.9940 respectively. As the results say the difference between the accuracy co-efficient is negligible, thus linear regression quantitative model is appropriate for the given data set.
Statistical Analysis
The statistical analysis of the given data is as follows:
Dependency Problem with the data
There is a problem with the data dependency in the given data as the data is not purely dependent. The entities for which the data is provided are not fixed. There may be discrepancy in the data. The data is also very much fluctuating. Let’s look at the interest cost. The curve in the interest cost is not very fluctuating but it has no dependency.