英语翻译线性回归分析是一种统计方法,用于确定某个变量(或一组变量)对另一个变量的影响.线性回归分析能就因变量(Y)与一个或多个自变量(X1,X2,.) 之间的关系给出最优线性元偏估计.管理会

英语翻译线性回归分析是一种统计方法,用于确定某个变量(或一组变量)对另一个变量的影响.线性回归分析能就因变量(Y)与一个或多个自变量(X1,X2,.) 之间的关系给出最优线性元偏估计.管理会
英语翻译
线性回归分析是一种统计方法,用于确定某个变量(或一组变量)对另一个变量的影响.线性回归分析能就因变量(Y)与一个或多个自变量(X1,X2,.) 之间的关系给出最优线性元偏估计.管理会计师经常使用线性回归方法分析成本行为(即确定总成本中的固定和变动部分),或预测未来的事件如销售额等.线性回归分析的基本假设前提是:·线性.因变量与自变量之间的关系是线性的.·稳定性.因变量与自变量之间的线性关系具有稳定性.这一假设通常称做"恒定过程假设"(constant process assumption) 0 ·因变量的真实值与估计值之间的差异(称做"误差项"或"残差项")服从均值为零飞标准差为常数的正态分布.也就是说,因变量与它自身并不相关,即因变量并不具备自相关性或序列相关性.·在多元回归分析中,各个自变量(X1,X2,…,)相互独立,不存在多重共线性.回归分析根据因变量与一个或多个自变量之间的关系建立线性方程.因变量(Y)就是需要预测的值,如销售额或总成本.自变量(X)就是假设可以影响或者驱动因变量变化的因素.此外,人们还假设因变量和自变量之间的关系保持不变(即表现为稳定的线性关系).回归分析主要有两种类型:一是简单回归分析,仅使用一个自变量;另一种是多元回归分析,使用两个或两个以上的自变量.回归分析可以系统地降低估计误差,回归分析也称为最小二乘法回归.回归分析是通过一些数据点拟合一条直线(回归直线),该直线可以将直线(估计值)与数据点(实际值)之间的偏离降到最小.回归分析所依据的统计公式使得估计值与实际值之间的误差能达到最小.

英语翻译线性回归分析是一种统计方法,用于确定某个变量(或一组变量)对另一个变量的影响.线性回归分析能就因变量(Y)与一个或多个自变量(X1,X2,.) 之间的关系给出最优线性元偏估计.管理会
Linear regression analysis is a statistical method used to determine the impact oile variable (or a group of variables) has on another variable.It provides the best,linear,unbiased estimate of the relationship between the dependent variable (Y) and one or more independent variables (X or X's).Linear regression Is often used by management accountants to analyze cost behavior (that is,determine the fixed atid variable portions of a total cost),or to forecast future events such as sales levels.
The assumptions underlying linear regresslOn are:
Linearity一Therelationship between the dependent variable and the independent variable(s) is linear.
Stationary -The process underlying the relationship is stationary.This assumption is often called the constant process assumpt1on.
The differences between the actual values of thedependent variable and its predicted values (the error or residual terms) are normally distributed with a mean of zero and a constant standard deviation.In other words,the dependent variable is not correlated with itself; i.e.,it is not auto-correlated or serial-correlated.
The independent variables (X's) in multiple regression analysisare independent of each other.There is no multi-co linearity.
Regression analysis creates a linear equation based oIì the relationship between a dependent variable and one ormdre independent variables.The dependent variable (Y) is the value being forecast,such as sales or total costs.The independent variables (X) are the factors that are assumed to inf1uence or drive the variations seen in the dependent variable.It is assumed that the relationship between the dependent variable and the independent variable remains constant (hence the linear relationship).
There are two main types of regression analysis:simple regression analysis,which uses only one independent variable; and multiple regression analysis,which uses two or more independent variables.
Regression analysis equations systematically reduce estimation errors,and are therefore also called least square regression.Regression analysis fits a line (the regression line) through data points-a line that minimizes the difference between the line (prediction) and the data point (actual).The statistical formula that the regression is based on produces the least amount of error between these two items.