移転のお知らせ
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移転の最大の原因は、ブログ上表示できる文章数は設定できないことです。過去の文章を見るときにとても不便になります。
# | Control Chart Rule |
West-gard
|
Nelson- Juran
|
AIAG
|
Mont-gomery
|
Western
Electric
|
IHI
|
---|---|---|---|---|---|---|---|
1 |
n points above UCL or below LCL
|
1
|
1
|
1
|
1
|
1
|
1
|
2 |
Zone A: n of n+1 points above/below 2 sigma
|
2
|
2
|
2
|
2
|
2
|
2
|
3 |
Zone B: n of n+1 points above/below 1 sigma
|
4
|
4
|
4
|
4
| ||
4 |
n points in a row above/ below center line
|
8
|
9
|
7
|
8
|
8
|
8
|
5 |
Trends of n points in a row increasing or decreasing
|
7
|
6
|
6
|
6
|
6
| |
6 |
Zone C: n points in a row inside Zone C (hugging)
|
15
|
15
|
15
|
15
| ||
7 |
n points in a row alternating up and down
|
14
|
14
|
14
| |||
8 |
Zone C:n points in a row outside Zone C
|
8
|
8
|
8
| |||
9 |
Zone B: n points above/ below 1 sigma; 2 points one above, one below 2 sigma
|
4
|
Analysis of means is a graphical alternative to ANOVA that tests the equality of population means. The graph displays each factor level mean, the overall mean, and the decision limits. If a point falls outside the decision limits, then evidence exists that the factor level mean represented by that point is significantly different from the overall mean.For example, you are investigating how temperature and additive settings affect the rating of your product. After your experiment, you use analysis of means to generate the following graph.The top plot shows that the interaction effects are well within the decision limits, signifying no evidence of interaction. The lower two plots show the means for the levels of the two factors, with the main effect being the difference between the mean and the center line. In the lower left plot, the point representing the third mean of the factor Temperature is displayed by a red symbol, indicating that there is evidence that the Temperature 200 mean is significantly different from the overall mean at α = 0.05. The main effects for levels 1 and 3 of the Additive factor are well outside the decision limits of the lower right plot, signifying that there is evidence that these means are different from the overall mean.Comparison of ANOM and ANOVAANOVA tests whether the treatment means differ from each other. ANOM tests whether the treatment means differ from the overall mean (also called the grand mean).Often, both analyses yield similar results. However, there are some scenarios in which the results can differ:If one group of means is above the overall mean and a different group of means is below the overall mean, ANOVA might indicate evidence for differences where ANOM might not.If the mean of one group is separated from the other means, the ANOVA F-test might not indicate evidence for differences whereas ANOM might flag this group as being different from the overall mean.One more important difference is that ANOVA assumes that your data follow a normal distribution, while ANOM can be used with data that follows a normal, binomial, or Poisson distribution.
The Mann-Whitney Test looks for differences in median values between two samples.也就是说,Kruskal-Wallis Test是测试受1个因素影响的,多个样本的median的差异(独立多群)。而Friedman Test则是2个因素(paired多群?)。
Both the Kruskal-Wallis and Friedman Tests look for differences in median values between more than two samples.
The Kruskal-Wallis Test is used to analyse the effects of more than two levels of just one factor on the experimental result. It is the non-parametric equivalent of the One Way ANOVA.
The Friedman Test analyses the effect of two factors, and is the nonparametric
equivalent of the Two Way ANOVA.
内科 | 50 | 80 | 58 | 81 |
外科 | 67 | 69 | 72 | 88 |
眼科 | 54 | 62 | 75 | 77 |
科室 | 内 | 眼 | 内 | 眼 | 外 | 外 | 外 | 眼 | 眼 | 内 | 内 | 外 |
得分 | 50 | 54 | 58 | 62 | 67 | 69 | 72 | 75 | 77 | 80 | 81 | 88 |
名次 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
和 | 平均 | |||||
内科 | 1 | 10 | 3 | 11 | 25 | 6.25 |
外科 | 5 | 6 | 7 | 12 | 30 | 7.5 |
眼科 | 2 | 4 | 8 | 9 | 23 | 5.75 |
0(h)
|
1(h)
|
2(h)
|
3(h)
|
…
|
|
濃度a(%)
|
100
|
90
|
80
|
73
|
…
|
濃度b(%)
|
100
|
80
|
70
|
60
|
…
|
濃度c(%)
|
100
|
60
|
45
|
34
|
…
|
0(h)
|
1(h)
|
2(h)
|
3(h)
|
…
|
|
濃度a(%)
|
100
|
90
|
80
|
73
|
…
|
2
| 3 | 5.5 | 6 | ||
濃度b(%)
|
100
|
80
|
70
|
60
|
…
|
2
| 5.5 | 7 | 9.5 | ||
濃度c(%)
|
100
|
60
|
45
|
34
|
…
|
2
| 9.5 | 10 | 11 |