水平号12345678
序号A
B
1124782248573363364487155512846636637751428
8
7
5
2
1
y
5.8
6.34.95.444.533.6
SUMMARY OUTPU
回归统计
Multiple R 0.99970596265R Square 0.99941201175Adjusted R Sq 0.99917681646标准误差0.03240370349
190230180220170210
137.5138138138.5139139.5140
底水量(x 1)/g 220230240
4.0,4.5,3.0,3.6。已知试验指标与两因素之间成二元线性关系,试用回归分析法139.5140
吸氨时间(x 2)/min 136.5137吸氨时间(x 2)/min 选用均匀表U 8*(85)安排实验,8个试验结果(吸氨量/g)依次为:5.8,6.3,4.9,5.4,出较好工艺条件,并预测该条件下相应的吸氨量。138.5139170180190200210137240第七章 均匀设计
1、在啤酒生产的某项工艺实验中,选取了底水量(A)和吸氨时间(B)两个因素都取了8个水平,进行试验设计,因素水平如下。试验指标为吸氨量,越大越好。137.5回归方程模型为y =a+b 1x 1+b 2x 2
136.5200底水量(x 1)/g
观测值8
方差分析
df
SS MS F Significance F 回归分析28.9235 4.461754249.285714288.38342726421
残差50.005250000000.00105总计
78.92875
Coefficients 标准误差t Stat P-value Lower 95%
Intercept 96.52583333331.4768020536165.36138888561.5871169308092.7295928008底水量(x1)/-0.69666666660.010********-66.7626010421.42755955001-0.7234906467
吸氨时间(x2)
0.021*********.0005217491941.84641500741.470141026900.020********RESIDUAL OUTP 观测值预测 y 残差1
5.797500000000.002499999992
6.32250000000-0.022******** 4.88250.017499999994 5.4075-0.00750000005 3.967500000000.032499999996 4.49250.007500000007 3.0525-0.05250000008
3.577500000000.022********观测值预测 y
1
5.797500000002
6.322500000003 4.88254 5.4075
5 3.96750000000
6 4.4925
7 3.0525
8
3.57750000000
R=0.99 和Significance F=8.38342726421806E-09<0.01,说明该回归方程非常显y=96.5-0.70X 1+0.02X 2
个因素,
越好。
.8,6.3,4.9,5.4,
分析法找
y
5.8
6.3
4.9
5.4
4
4.5
3
3.6
Upper 95%下限 95.0%上限 95.0% 100.32207386579592.7295928008718100.322073865795 -0.669842686620821-0.723490646712513-0.669842686620821
0.02317453233562560.0204921343310410.0231745323356256
=0.99^0.5
非常显著。预测值在表中