第一节误设定 第二节多重共线性 第三节异方差性

Single equation regression models: -The dependent variable, Y, is expressed as a linear function of one or more explanatory variables, the Xs. Assumption the cause-and-effect relationship, if any, between Y and the Xs is unidirectional: explanatory variables are the cause; the dependent variable is the effect

一、社会指标的概念 社会指标是指反映社会现象的数量,质量,类别,状态,等级,成都等特性的项目。 1.具体性或可感知性 2.计量性或可量度性 3.重要性或代表性 4.时间性

1、机械消耗定额以一台机械工作8小时工作班为计量单位。 -机械连续工作24小时为3个台班停置24小时为停置1个台班 2、定额制定方法 机械一次循环正常延续时间A=循环组成部分正常延续时间之和机械纯工作1小时循环次数B=3600(60)/A

第一章绪论 第二章计量装置 第三章供送机构 第四章灌装机械 第五章袋装机械 第六章包机械 第七章卷封机械 第八章传动系统 第九章总体方案设计 第十章包装生产线 第十一章练习题

14.1 Restricted Least Squares (RLS) 1. OLS and RLS ()Unrestricted least squares(ULS) When using the ordinary least square method(OLS) to estimate the parameters, we do not put any prior constraint() or restriction(s) on the parameters. So we can estimate the parameters without any restrictions. This is ULS

12.1 The Nature of Autocorrelation 1. Definition (1) CLRM assumption: No autocorrelation exist in dishurbances ui; E(iμi)=0 Autocorrelation means: E(μiμ)≠0 (2) Autocorrelation is usually associated with time series data, but it can also occur in cross-sectional data, which is called spatial correlation

One of the CLRM assumptions is: there is no perfect multicollinearity-no exact linear relationships among explanatory variables, Xs, in a multiple regression. In practice, one rarely encounters perfect multicollinearity, but cases of near or very high multicollinearity where explanatory variables are approximately linearly related frequently arise in many applications

The models we discussed are models that are linear in parameters; variables Y and Xs do not necessarily have to be linear The price elasticity of demand~the log-linear models The rate of growth~semilog model Functional forms of regression models which are linear in parameters, but not necessarily linear in variables:

The Object of Hypothesis Testing To answer- How \good\ is the estimated regression line. How can we be sure that the estimated regression function (i.e., the SRF) is in fact a good estimator of the true PRF?