怎樣使用mintab進(jìn)行回歸分析(文件)
【正文】 h Source DF SS MS F P Regression 3 Residual Error 25 Total 28 DurbinWatson statistic = 解釋結(jié)果 MINITAB選擇了以下回歸模型： 其中Y為響應(yīng)，X1, X2, 和X3為預(yù)測(cè)因子，b0, b1, b2, and b3是回歸系數(shù)。連續(xù)平方和只是現(xiàn)有變量平方和，假定所有輸入的變量都存在。下一行即為SS (b3 | b0, b1, b2)，等。The coefficient of the variable East, however, has an ttest pvalue of . If the evidence for the coefficient not being zero appears insufficient and if it adds little to the prediction, you may choose the more parsimonious model with predictors North and South. Make thisdecision only after examining the residuals. In the residual plots example on page 228, you examine the residuals from the model with predictors North and South. (Alternatively, you could have used the graphs available in the Graphs subdialog box.) 。MINITAB不顯示連續(xù)平方和的P值，除了最后連續(xù)平方和，mean square error不能用來檢驗(yàn)這些項(xiàng)目的顯著性。South的值() 是South惟一的平方和假設(shè)North平方和存在以及East的值() 是East惟一的平方和假設(shè)North及South平方和存在 假設(shè)您已得到了b0，連續(xù)平方和表中第一行給出了SS(b1 | b0),，或者由于擬合b1項(xiàng)而導(dǎo)致誤差SS減小(與使用X1作為預(yù)測(cè)因子相同)。連續(xù)平方和與t檢驗(yàn)不一樣。 異常觀測(cè)值 Obs East HeatFlux Fit SE Fit Residual St Resid 4 22 S = RSq = % RSq(adj) = % 您可以用多元回歸分析法來建立三預(yù)測(cè)因子關(guān)系模型。下面的多元回歸例子以及殘差圖程序提供了其它關(guān)系回歸分析的信息。在預(yù)測(cè)區(qū)間為95%情況下的顯示區(qū)間是單個(gè)觀測(cè)值Score1 = 。擬合或被擬合值有時(shí)又叫做被預(yù)測(cè)Y值或 ?？偲椒胶?SS Total)是平均值的修正平方和，使用方差分析來評(píng)定總擬合值，F(xiàn)檢驗(yàn)是對(duì)零假設(shè)H0:所有回歸系數(shù)(除b0)=0進(jìn)行檢驗(yàn)。 The R2 value is the proportion of variability in the Y variable (in this example, Score2) accounted for by the predictors (in this example, Score1). RSq(adj) = %，這是根據(jù)自由度而調(diào)整的R2，如果一個(gè)變量被增加到一個(gè)關(guān)系方程，R2將要變大盡管被增加變量很可能不是實(shí)數(shù)。 系數(shù)表：在輸出窗口中第一個(gè)表給出的是估計(jì)的系數(shù)，b0和b1，以及它們的標(biāo)準(zhǔn)誤差。Values of Predictors for New Observations Unusual Observations Obs Score1 Score2 Fit SE Fit Residual St Resid 9 Predictor Coef SE Coef T P Constant Score1 選擇統(tǒng)計(jì)回歸回歸 ，輸入Score2，在預(yù)測(cè)因子欄中，輸入Score1. “選項(xiàng)”按鈕。 1. Cook’s distance bines leverages and Studentized residuals into one overall measure of how unusual the predictor values and response are for each observation. Large values signify unusual observations. Geometrically, Cook’s distance is a measure of the distance between coefficients ca
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