英语翻译4 Analysis of varianceAnalysis of variance (ANOVA) is us

英语翻译
4 Analysis of variance
Analysis of variance (ANOVA) is used to judge whether or
not the experimentally found significant factors are statistically
significant.In the present investigation,MINITAB
(statistical software) was used to analyze the significance of
factors.The significance can also be judged by calculating
F or P values.Furthermore,the calculated F values (productof the square of the effect and the degrees of freedom) are
compared with the theoretical extreme values for the F
distribution [24].
InANOVA,themeaning of 5% significance level implies 1
in 20 and 1% means 1 in 100.This indicates that the parameters
falling in 1% significance level are the most dominant
factors and those of 5% significance are the next dominant
factors.
Normal distribution plots (Figs.10 and 11) are used to
identify the outlier points most likely to represent real factor
effects.The points/estimates which are close to the line
fitted to the middle group of points represent estimated
factor effects which do not demonstrate any significant
effects on the response variable [24].
4.1 ANOVA for AISI 1045
From ANOVA (Table 11),it is can be seen that the most
dominating factors among the main factors are exposure
time and nozzle distance,because these parameters have
higher F-statistic values.Among the two-way interactions,
the interaction between shot size and exposure time (P-T)
is more significant and the next interaction effects in
decreasing order are S-T,S-D,P-D,D-T,and P-S.Results fromthe ANOVA test show that all themain factors
except pressure are statistically significant at a significance
level of 1%,since the p-values for these parameters are less
than 0.01 and the significance level for pressure is 5%.These
values agree with the normal probability plot (Fig.10).
4.2 ANOVA for 316L
From the ANOVA results for 316L (Table 12),it can be seen
that the most dominating factor among the main factors is
nozzle distance (D),since it has a higher F-statistic value.
The next dominating parameters are shot size (S),exposure
time (T),and pressure (P).The p-value for each of the main
factors is less than 0.01,which indicates that all the factors
are significant at 1% significance level.
Among the two-way interactions,the interaction between
shot size and the exposure time (S-T) is the most
significant.The next most significant interaction effects in
decreasing order are D-T,P-S,P-D,P-T,and S-D.The pvalue
for the interaction S-D is more than 0.05,which
indicates that it is not significant.This is also observed in
the normal probability plot (Fig.11).
现在悬赏用完了 没办法 如果可以 我一定会补上的

4方差分析
方差分析（ANOVA）来判断是否
实验发现不是重要的因素是统计学
显着.在目前的调查及Minitab
（统计软件）是用来分析的意义
因素.其意义也可以通过计算来判断
F或P值.此外,计算出的F值（productof的效果平方和自由度）的
与此相比较的F理论极值
分布[24].
InANOVA,5％显着水平themeaning implies 1
在20日和1％意味着100 1.这表明,参数
在1％显着水平下降是最优势
因素和5％的显着优势的是未来
因素.
正态分布曲线（图10和11）是用来
找出异常点最有可能代表真实的因素
影响.在/估计是接近线点
安装在中间的点估计集团代表
因素的影响,并不表明任何重大
在响应变量的影响[24].
4.1变异数为1045谢谢了!
从方差分析（表11）,它是可以看到,最
其中的主要因素主导因素是暴露
时间和喷嘴的距离,因为这些参数
较高的F -统计值.在双向互动,
镜头之间的大小和曝光时间（PT交互）
更为显着,未来的互动效应
依次是ST,支持SD,药理学,药物疗法,并PS.Results其外方差分析测试表明,所有themain因素
除了压力,在统计上意义重大
1％的水平,因为在P -为这些参数值少
比0.01和压力显着性水平为5％.这些
价值认同与正常概率图（图10）.
4.2变异数为316
从316的方差分析结果（表12）,可以看出
其中主要的因素是最主导因素
喷嘴距离（D）,因为它具有较高的F -统计值.
接下来的主要参数镜头尺寸（S）和曝光
时间（T）和压力（P）.的p -值的每个主要
因素是小于0.01,这表明所有的因素
显着的1％显着水平.
在双向互动,互动
注射量和曝光时间（S - T）是最
显着.下一个最显着的交互作用效应
依次是氘氚,磷,硫,磷的三维,的P - T,和S - Ð.在0.025
在互动的S - D是超过0.05,
说明它并不显着.这也是观察
正常的概率图