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11.2、SUMMARYOUTPUT回归统计MultipleR0.995651RSquare0.991321AdjustedRSquare0.986982标准误差261.431观测值7方差分析dfSSMSFSignificanceF回归分析23122661515613308228.44457.53E-05残差4273384.768346.19总计631500000Coefficients标准误差tStatP-valueLower95%Upper95%Intercept-0.591505.0042-0.001170.999122-1402.711401.526XVariable122.386469.6005442.3317910.080095-4.2689249.04184XVariable2327.671798.797923.3165850.02947253.3647601.9787(1)回归方程为:120.59122.386327.672yxx(在α=0.05时,常数项和x1的t-检验值没过)(2)b1表示降雨量每增加1mm,早稻的平均收获量将增加22.386kg/hm2;b2表示温度没升高1。C,早稻的平均收获量将增加327.672kg/hm2。(3)模型中存在多重共线性。(R2值很高,F检验通过,常数项和x1的t-检验值没过,也可以通过计算其自变量之间相关系数)11.3、SUMMARYOUTPUT回归统计MultipleR0.947362RSquare0.897496AdjustedRSquare0.878276标准误差791.6823观测值20方差分析dfSSMSFSignificanceF回归分析3878035052926783546.696973.88E-08残差1610028175626760.9总计1997831680Coefficients标准误差tStatP-valueLower95%Upper95%Intercept148.7005574.42130.258870.799036-1069.021366.419XVariable10.8147380.5119891.5913210.131099-0.270631.900105XVariable20.820980.2111773.8876460.0013070.3733051.268654XVariable30.1350410.0658632.0503220.057088-0.004580.274665(1)回归方程为:123148.7010.8150.8210.135yxxx(2)在总变差中,被估计的回归方程所解释的比例:20.897SSRRSSE(3)F检验值为46.6975,线性关系显著;(4)当α=0.05时,X1、X3解释作用均不显著,X2解释作用显著。11.6SUMMARYOUTPUT回归统计MultipleR0.780195RSquare0.608704AdjustedRSquare0.578604标准误差3.042926观测值15方差分析dfSSMSFSignificanceF回归分析1187.2519187.251920.22290.000601残差13120.37219.259396总计14307.624Coefficients标准误差tStatP-valueLower95%Upper95%Intercept4.5428571.1501183.9499060.0016622.0581797.027535XVariable17.0821431.5748644.4969880.0006013.67985710.48443(1)回归方程为:14.5437.082yx(2)回归模型中的系数表明,每件易碎品在运输费用上比非易碎品多出了7.082元。(3)F检验值为20.2235,线性关系显著。
本文标题:多元回归作业答案
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