mba統(tǒng)計學(xué)(專業(yè)版)
【正文】 Relationship among variables ? Function ? Correlation (statistical relationship) Y depends on X, but isn’ t merely determined by X. Example: price—demand for product temperature—demand for airconditioning ? Regression—According to observant data,establish regression model and make statistical reference on variables having statistical relationship. Chapter 10 Regression 1 What does regression do? Solve the following problems: ?Determine whether there has statistical relationship among variables, if has, show the formula. ?Forecast the value of another variable according to one variable or a group of variables. 2 Linear Regression Assumptions ? Normality ? Every value of X , Y follows the normal distribution ? The error probability follows the normal distribution ? Homoscedasticity (Constant Variance) ? Independence of Errors ? Linearity 3 Example: Xprice， Ydemand for the product We have data: 1. Scatter plot 2. Regression equation(Ordinary Least Square Estimation) 3. Correlation coefficient r Testing the regression model can be linearitied Simple Linear Regression X(Yuan) 70 80 90 100 110 Y(thousand) 4 Linear Regression Model Variables consist of a linear function. Y X i i i ? ? ? ? ? ? 0 1 Slope YIntercept Independent (Explanatory) Variable Dependent (Response) Variable Random Error 5 Population Linear Regression Model ? i = random error X ? ? ? YX i X ? ? 0 1 Y X i i i ? ? ? ? ? ? 0
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