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五、多元线性回归前面讨论了两个变量之间的回归问题,在生产和科研的实际中,常常遇到影响因变量的自变量不是一个,而是多个的情况,这科回归问题谓多元回归分析。多元回归又分为多元线性回归和多元非线性回归两类。多元线性回归在实际中应用颇多,这是由于因变量与自变量之间线性关系是经常遇见;线性回归计算相对地较简单,工作量也较小.多元非线性回归比多元线性回归复杂,计算工作量也大,故应用较少;另一方面许多非线性回归问题都可化为线性回归加以处理,这样以来使复杂的非线性问题变成线性,问题得到简化。多元线性回归的用途:(1)推导出多个自变量与因变量之间的关系式——多元线性回归方程。
Fifth, multiple linear regression In the previous discussion of the regression between the two variables in the production and scientific research, often encountered independent variables affecting dependent variables is not one, but a number of cases, this section of the regression problem that multiple regression analysis. Multiple regression is divided into multiple linear regression and multiple nonlinear regression. Multivariate linear regression is widely used in practice, which is often encountered due to the linear relationship between dependent variables and independent variables, linear regression calculation is relatively simple, and the workload is also small.Multivariate nonlinear regression is more complex than multiple linear regression, The calculation workload is also large, so the application is less; on the other hand, many of the nonlinear regression problems can be treated as linear regression, so that complex nonlinear problems become linear, the problem is simplified. The use of multiple linear regression: (1) derived a number of independent variables and dependent variables - multiple linear regression equation.