试验设计与数据处理作业

1、试验设计与数据处理作业姓名： 班级： 学号： 3-7SAS分析结果如下：金球：及置信度为0.9的置信区间分别为： 铂球:及置信度为0.9的置信区间分别为： 3-13取假设H0：u1-u20和假设H1：u1-u20用sas分析结果如下： Sample Statistics Group N Mean Std. Dev. Std. Error - x 8 0.231875 0.0146 0.0051 y 10 0.2097 0.0097 0.0031 Hypothesis Test Null hypothesis: Mean 1 - Mean 2 = 0 Alternative: Mean 1 -

2、Mean 2 = 0 If Variances Are t statistic Df Pr > t - Equal 3.878 16 0.0013 Not Equal 3.704 11.67 0.0032由此可见p值远小于0.05，可认为拒绝原假设，即认为2个作家所写的小品文中由3个字母组成的词的比例均值差异显著。3-14用sas分析如下： Hypothesis Test Null hypothesis: Variance 1 / Variance 2 = 1 Alternative: Variance 1 / Variance 2 = 1 - Degrees of Freedom -

3、F Numer. Denom. Pr > F - 2.27 7 9 0.2501由p值为0.25010.05（显著性水平），所以接受原假设，两方差无显著差异4-1 Sas分析结果如下：Dependent Variable: y Sum of Source DF Squares Mean Square F Value Pr > F Model 4 1480.823000 370.205750 40.88 <.0001 Error 15 135.822500 9.054833 Corrected Total 19 1616.645500 R-Square Coeff Var Ro

4、ot MSE y Mean 0.915985 13.12023 3.009125 22.93500 Source DF Anova SS Mean Square F Value Pr > F c 4 1480.823000 370.205750 40.88 <.0001由结果可知，p值小于0.001，故可认为在水平a=0.05下，这些百分比的均值有显著差异。4-2SAS分析如下： The GLM ProcedureDependent Variable: R Sum of Source DF Squares Mean Square F Value Pr > F Model 11

5、 82.8333333 7.5303030 1.39 0.2895 Error 12 65.0000000 5.4166667 Corrected Total 23 147.8333333 R-Square Coeff Var Root MSE R Mean 0.560316 22.34278 2.327373 10.41667 Source DF Type I SS Mean Square F Value Pr > F m 2 44.33333333 22.16666667 4.09 0.0442 n 3 11.50000000 3.83333333 0.71 0.5657 m*n 6

6、 27.00000000 4.50000000 0.83 0.5684 Source DF Type III SS Mean Square F Value Pr > F m 2 44.33333333 22.16666667 4.09 0.0442 n 3 11.50000000 3.83333333 0.71 0.5657 m*n 6 27.00000000 4.50000000 0.83 0.5684由结果可知，在不同浓度下得率有显著差异，在不同温度下得率差异不明显，交互作用的效应不显著。5-3用L9（34）确定配比试验方案:试验方案 因素试验号ABCD1234567891（0.1份

7、）112（0.3份）223（0.2份）331（0.3份）2（0.4份）3（0.5份）1231231（0.2份）2（0.1份）3（0.1份）2313121（0.5份）2（0.3份）3（0.1份）312231以1号条件为例，表中四个数值的组成比为：A：B：C：D=0.1：0.3：0.2：0.5配比方案中，要求各行四个比值之和为1。在1号条件中，四种数值分别是 其余实验条件按相同方法可得。6-5（1）作出散点图如下： （2）SAS分析结果如下：Dependent Variable: y Analysis of Variance Sum of Mean Source DF Squares Square

8、 F Value Pr > F Model 2 38.93714 19.46857 9.54 0.0033 Error 12 24.47619 2.03968 Corrected Total 14 63.41333 Root MSE 1.42817 R-Square 0.6140 Dependent Mean 30.03333 Adj R-Sq 0.5497 Coeff Var 4.75530 Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > |t| Intercept 1

9、19.03333 3.27755 5.81 <.0001 t1 1 1.00857 0.35643 2.83 0.0152 t2 1 -0.02038 0.00881 -2.31 0.0393故回归方程为：6-6（1）作线性回归分析结果如下：由分析结果得回归方程为：由p值都小于0.1可知，每项都是显著的，方程也是显著的。（2）由分析结果可知，在a=0.05下，仅有x3和x1应当引入方程。故所求方程为：6-9分析结果如下：Dependent Variable: y Stepwise Selection: Step 1 Variable t9 Entered: R-Square = 0.34

10、73 and C(p) = 175.7517 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 1 76.24389 76.24389 7.45 0.0163 Error 14 143.28371 10.23455 Corrected Total 15 219.52760 Parameter Standard Variable Estimate Error Type II SS F Value Pr > F Intercept 8.22980 1.38618 360.7494

11、9 35.25 <.0001 t9 0.01056 0.00387 76.24389 7.45 0.0163 Bounds on condition number: 1, 1- Stepwise Selection: Step 2 Variable t13 Entered: R-Square = 0.6717 and C(p) = 84.4265 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 2 147.46551 73.73276 13.30 0.0007 Error

12、13 72.06209 5.54324 Corrected Total 15 219.52760 Parameter Standard Variable Estimate Error Type II SS F Value Pr > F Intercept 18.33483 2.99803 207.32264 37.40 <.0001 t9 0.01173 0.00287 92.81733 16.74 0.0013 t13 -1.89938 0.52989 71.22162 12.85 0.0033- Stepwise Selection: Step 3 Variable t5 En

13、tered: R-Square = 0.7627 and C(p) = 60.2727 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 3 167.42492 55.80831 12.85 0.0005 Error 12 52.10268 4.34189 Corrected Total 15 219.52760 Parameter Standard Variable Estimate Error Type II SS F Value Pr > F Intercept 19.

14、49941 2.70837 225.06452 51.84 <.0001 t5 0.00163 0.00076176 19.95941 4.60 0.0532 t9 0.00728 0.00328 21.44626 4.94 0.0462 t13 -2.19305 0.48856 87.48515 20.15 0.0007 Bounds on condition number: 1.8185, 13.825- All variables left in the model are significant at the 0.1500 level. No other variable met

15、 the 0.1500 significance level for entry into the model. Summary of Stepwise Selection Variable Variable Number Partial Model Step Entered Removed Vars In R-Square R-Square C(p) F Value Pr > F 1 t9 1 0.3473 0.3473 175.752 7.45 0.0163 2 t13 2 0.3244 0.6717 84.4265 12.85 0.0033 3 t5 3 0.0909 0.7627

16、 60.2727 4.60 0.0532由结果可知，y=19.49941+0.00163+0.00728-2.193056-10（1）散点图如下：可以采用Logistic拟合此数据。（2）用Logistic模拟结果为： Dependent Variable y Method: Gauss-Newton Sum of Iter b c a Squares 0 3.7180 2.0000 21.0000 1124.1 1 3.6408 1.8493 14.8393 570.9 2 3.5475 1.6684 14.9977 534.7 3 3.4704 1.5208 15.2362 499.4 4

17、 3.4046 1.3963 15.4814 464.3 5 3.3469 1.2887 15.7348 429.2 6 3.2955 1.1943 15.9985 394.1 7 3.2491 1.1107 16.2735 359.1 8 3.2069 1.0360 16.5601 324.4 9 3.1684 0.9691 16.8579 290.5 10 3.1331 0.9091 17.1660 257.6 11 3.1008 0.8551 17.4829 226.3 12 3.0712 0.8067 17.8067 196.8 13 3.0442 0.7632 18.1349 169

18、.7 14 3.0195 0.7242 18.4653 145.1 15 2.9971 0.6892 18.7951 123.1 16 2.9768 0.6580 19.1220 104.0 17 2.9584 0.6300 19.4438 87.4241 18 2.9420 0.6050 19.7586 73.4129 19 2.9272 0.5827 20.0648 61.7148 20 2.9140 0.5628 20.3610 52.0895 21 2.9023 0.5450 20.6464 44.2826 22 2.8920 0.5291 20.9203 38.0422 23 2.8

19、828 0.5149 21.1822 33.1304 24 2.8748 0.5021 21.4319 29.3307 25 2.8679 0.4907 21.6693 26.4509 26 2.8618 0.4804 21.8946 24.3243 27 2.8566 0.4711 22.1078 22.8087 28 2.8522 0.4628 22.3094 21.7843 29 2.8484 0.4553 22.4995 21.1514 30 2.8453 0.4486 22.6787 20.8276 31 2.8428 0.4425 22.8472 20.7456WARNING: S

20、tep size shows no improvement. WARNING: PROC NLIN failed to converge. Estimation Summary (Not Converged) Method Gauss-Newton Iterations 31 Subiterations 29 Average Subiterations 0.935484 R 0.653053 PPC(c) 0.012486 The NLIN Procedure Estimation Summary (Not Converged) RPC . Object 0.00394 Objective 2

21、0.74557 Observations Read 15 Observations Used 15 Observations Missing 0 NOTE: An intercept was not specified for this model. Sum of Mean Approx Source DF Squares Square F Value Pr > F Regression 3 3245.5 1081.8 625.77 <.0001 Residual 12 20.7456 1.7288 Uncorrected Total 15 3266.3 Corrected Tot

22、al 14 949.9 Approx Parameter Estimate Std Error Approximate 95% Confidence Limits b 2.8428 0.00488 2.8321 2.8534 c 0.4425 0.00321 0.4355 0.4494 a 22.8472 0.5749 21.5947 24.0998 Approximate Correlation Matrix b c a b 1.0000000 0.7483922 -0.2191675 c 0.7483922 1.0000000 -0.4085662 a -0.2191675 -0.4085

23、662 1.0000000故得 7-1n=9作变换，则x1=1，x2=2，x3=3，x9=9。并可设=b0+b11(x)+ b22(x)+ b22(x)+ b44(x)对于n=9，查附表6，利用SAS软件进行回归多项式分析，结果如下：根据结果分析b0，b1，b2的P值小于0.05，是显著的，b3，b4的P值大于0.05是不显著的，所以只需要配要二次项就行了。当n=9时，有： 将代入上式得所求多项式回归方程：7-3本题属于一次回归的正交设计采用二水平，做变换：x1=，x2=，x3=，x4= 试验方案试验号x1x2x3x4y12345678111-1-1-1-1-111-1-111-1-11-11-11-11-11-1-11-111-15.43.40.80.3-2.5-1-3.3-3.6程序如下：data E1;input x1-x4 y;cards;1 1 1 1 5.41 1 -1 -1 3.41 -1 1 -1 0.81 -1 -1 1 0.3-1 1 1 -1 -2.5-1 1 -1 1 -1-1 -1 1 1 -3.3-1 -1 -1 -1 -3.6;proc reg data=E1;model y=x1-x4;run;8-1安排方案如下： 因素 试验号Z1Z2Z312.06002423.48002234.85002046.2700189-1（1）反射点E的坐标（5，