{"id":4760,"date":"2025-06-20T19:39:32","date_gmt":"2025-06-20T10:39:32","guid":{"rendered":"https:\/\/www.ag.kagawa-u.ac.jp\/charlesy\/?p=4760"},"modified":"2025-09-16T09:55:03","modified_gmt":"2025-09-16T00:55:03","slug":"%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90%e3%81%a7%e6%9c%89%e6%84%8f%e3%81%ab%e3%81%aa%e3%82%89%e3%81%aa%e3%81%84%e3%81%8c%e3%80%81%e5%a4%9a%e9%87%8d%e6%af%94%e8%bc%83%e6%a4%9c%e5%ae%9a%e3%81%a7%e6%9c%89","status":"publish","type":"post","link":"https:\/\/www.ag.kagawa-u.ac.jp\/charlesy\/2025\/06\/20\/%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90%e3%81%a7%e6%9c%89%e6%84%8f%e3%81%ab%e3%81%aa%e3%82%89%e3%81%aa%e3%81%84%e3%81%8c%e3%80%81%e5%a4%9a%e9%87%8d%e6%af%94%e8%bc%83%e6%a4%9c%e5%ae%9a%e3%81%a7%e6%9c%89\/","title":{"rendered":"\u5206\u6563\u5206\u6790\u3067\u6709\u610f\u306b\u306a\u3089\u306a\u3044\u304c\u3001\u591a\u91cd\u6bd4\u8f03\u691c\u5b9a\u3067\u6709\u610f\u3068\u306a\u308b\u3088\u3046\u306a\u30c7\u30fc\u30bf\u306e\u4f8b"},"content":{"rendered":"\n

\u6bcd\u96c6\u56e3<\/h2>\n\n\n\n\n\n\n\n

\u4e0b\u306e\u56f3\u3067\u8868\u308f\u3055\u308c\u3066\u3044\u308b5\u3064\u306e\u6b63\u898f\u6bcd\u96c6\u56e3\u304b\u3089\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n\n
\u7fa4<\/td>\u5e73\u5747\u5024<\/td>\u5206\u6563<\/td>\u6a19\u6e96\u504f\u5dee<\/td><\/tr>
A<\/td>5.080<\/td>0.0189<\/td>0.138<\/td><\/tr>
B<\/td>5.1859<\/td>0.0029<\/td>0.054<\/td><\/tr>
C<\/td>5.1861<\/td>0.0026<\/td>0.051<\/td><\/tr>
D<\/td>5.1864<\/td>0.0043<\/td>0.066<\/td><\/tr>
E<\/td>5.238<\/td>0.0062<\/td>0.078<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u7fa4A\u3068\u7fa4E\u306e\u6bcd\u5e73\u5747\u306e\u5dee\u306e\u52b9\u679c\u91cf\u306f\\(\\frac{|5.238295-5.080351|}{\\sqrt{\\frac{0.07842281^2+0.1375497^2}{2}}} \\fallingdotseq 1.41\\)\u3067\u3042\u308a\u3001\u5c11\u306a\u304f\u3068\u3082\u3053\u306e\u52b9\u679c\u3092\u691c\u51fa\u3057\u305f\u3044\u3002\u30e9\u30f3\u30c0\u30e0\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\uff08100,000\u56de\uff09\u3092\u7e70\u308a\u8fd4\u3057\u305f\u6642\u306e5%\u6c34\u6e96\u68c4\u5374\u7387\u3092\u4e0b\u306e\u8868\u306b\u793a\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

<\/td>5%\u6c34\u6e96\u68c4\u5374\u7387<\/td><\/td><\/td><\/td><\/tr>
n<\/em><\/td>\u5206\u6563\u5206\u6790\uff08\u7b49\u5206\u6563\uff09<\/td>Tukey HSD\u691c\u5b9a
\uff08\u7fa4A vs. \u7fa4E\uff09<\/td>		\u5206\u6563\u5206\u6790\uff08\u4e0d\u7b49\u5206\u6563\uff09<\/td>Games\u2013Howell\u6cd5
\uff08\u7fa4A vs. \u7fa4E\uff09<\/td><\/tr>
2<\/td>0.2001<\/td>0.17744<\/td>0.13715<\/td>\u2013a<\/sup><\/em><\/td><\/tr>
3<\/td>0.32522<\/td>0.30633<\/td>0.17187<\/td>0.06232<\/td><\/tr>
4<\/td>0.43086<\/td>0.4194<\/td>0.22275<\/td>0.11431<\/td><\/tr>
5<\/td>0.52356<\/td>0.51794<\/td>0.28777<\/td>0.17495<\/td><\/tr>
6<\/td>0.60893<\/td>0.60763<\/td>0.35448<\/td>0.24316<\/td><\/tr>
7<\/td>0.67901<\/td>0.68453<\/td>0.42531<\/td>0.31413<\/td><\/tr>
8<\/td>0.74135<\/td>0.74673<\/td>0.49672<\/td>0.38871<\/td><\/tr>
9<\/td>0.79499<\/td>0.80356<\/td>0.5624<\/td>0.46233<\/td><\/tr>
10<\/td>0.83522<\/td>0.84478<\/td>0.62689<\/td>0.53158<\/td><\/tr>
15<\/td>0.95355<\/td>0.95965<\/td>0.84763<\/td>0.8014<\/td><\/tr>
20<\/td>0.98822<\/td>0.99006<\/td>0.94668<\/td>0.92978<\/td><\/tr><\/tbody><\/table>
a<\/sup><\/em> \u81ea\u7531\u5ea6\u304c2\u672a\u6e80\u306a\u306e\u3067p<\/em>\u5024\u3092\u8a08\u7b97\u3067\u304d\u306a\u3044\u3002<\/figcaption><\/figure>\n\n\n\n

\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3059\u308b\u691c\u5b9a\u6cd5\u3067\u306f\\(n = 9\\)\u3001\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u306a\u3044\u691c\u5b9a\u6cd5\u3067\u306f\\(n = 15\\)\u3067\u691c\u51fa\u529b80%\u306b\u9054\u3057\u3066\u3044\u307e\u3059\u3002\u30c7\u30fc\u30bf\u306f\u660e\u3089\u304b\u306b\u4e0d\u7b49\u5206\u6563\u3067\u3059\u306e\u3067\u3001\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u305f\u5206\u6563\u5206\u6790\u3068Tukey\u306eHSD\u6cd5\u3067\u306f\u4e0d\u9069\u5207\u306b\u7518\u304f\u691c\u5b9a\u3092\u3057\u3066\u3044\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

\u6b21\u306b\u3001\u6709\u610f\u6c34\u6e965%\u3067\u691c\u5b9a\u3057\u305f\u6642\u306b\u5206\u6563\u5206\u6790\uff08\u7b49\u5206\u6563\u3092\u4eee\u5b9a\uff09\u3067\u6709\u610f\u5dee\u304c\u51fa\u305a\u3001Tukey\u306eHSD\u691c\u5b9a\u3067A\u7fa4\u3068E\u7fa4\u306e\u9593\u3067\u6709\u610f\u5dee\u304c\u51fa\u308b\u78ba\u7387\u309210,000\u56de\u306e\u30e9\u30f3\u30c0\u30e0\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u304b\u3089\u51fa\u3057\u3066\u307f\u305f\u3068\u3053\u308d\u30010.971%\u3067\u3057\u305f\u3002<\/p>\n\n\n\n

 n<\/em> = 15<\/td>Tukey \\(p < 0.05\\) (\u7fa4A vs. \u7fa4E)<\/td>Tukey \\(p \\ge 0.05\\) (\u7fa4A vs. \u7fa4E)<\/td><\/tr>
\u5206\u6563\u5206\u6790 (\u7b49\u5206\u6563\u3092\u4eee\u5b9a) \\(p < 0.05\\) <\/td>94.975%<\/td>0.468%<\/td><\/tr>
\u5206\u6563\u5206\u6790 (\u7b49\u5206\u6563\u3092\u4eee\u5b9a) \\(p \\ge 0.05\\) <\/td>0.971%<\/td>3.586%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

\u5206\u6563\u5206\u6790\uff08\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u306a\u3044\uff09\u3067\u306f\u6709\u610f\u5dee\u304c\u51fa\u305a\u3001Games\u2013Howell\u6cd5\u3067A\u7fa4\u3068E\u7fa4\u306e\u9593\u3067\u6709\u610f\u5dee\u304c\u51fa\u308b\u78ba\u7387\u306f2.012%\u3067\u3057\u305f\u3002<\/p>\n\n\n

 n<\/em> = 15<\/td>Games\u2013Howell \\(p < 0.05\\) (\u7fa4A vs. \u7fa4E)<\/td>Games\u2013Howell \\(p \\ge 0.05\\) (\u7fa4A vs. \u7fa4E)<\/td><\/tr>
\u5206\u6563\u5206\u6790 (\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u306a\u3044) \\(p < 0.05\\) <\/td>78.11%<\/td>6.654%<\/td><\/tr>
\u5206\u6563\u5206\u6790 (\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u306a\u3044) \\(p \\ge 0.05\\) <\/td>2.012%<\/td>13.224%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u5177\u4f53\u7684\u306a\u30c7\u30fc\u30bf\u306e\u4f8b (n<\/em> = 15) <\/h2>\n\n\n\n

\u30e9\u30f3\u30c0\u30e0\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u305f\u3042\u308b\u30c7\u30fc\u30bf\uff08\u5404\u7fa4n<\/em> = 15\uff09\u3092\u89e3\u6790\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n\n
\u7fa4<\/td>n<\/em><\/td>\u5e73\u5747\u5024<\/td>\u4e0d\u504f\u5206\u6563<\/td>\u4e0d\u504f\u6a19\u6e96\u504f\u5dee<\/td><\/tr>
A<\/td>15<\/td>5.1120<\/td>0.0193<\/td>0.1390<\/td><\/tr>
B<\/td>15<\/td>5.1831<\/td>0.0031<\/td>0.0560<\/td><\/tr>
C<\/td>15<\/td>5.1965<\/td>0.0026<\/td>0.0508<\/td><\/tr>
D<\/td>15<\/td>5.1984<\/td>0.0059<\/td>0.0771<\/td><\/tr>
E<\/td>15<\/td>5.2409<\/td>0.0073<\/td>0.0856<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
#R\u5185\u3067\u306e\u4e0a\u306e\u30c7\u30fc\u30bf\u306e\u751f\u6210\nN=15\nset.seed(-6152847)\na <- rnorm(N, mean=5.080351, sd=0.1375497)\nset.seed(3448258)\nb <- rnorm(N, mean=5.185921, sd=0.05358547)\nset.seed(-9846648)\nc <- rnorm(N, mean=5.186056, sd=0.05089308)\nset.seed(-1377657)\nd <- rnorm(N, mean=5.186409, sd=0.06550713)\nset.seed(7612155)\ne <- rnorm(N, mean=5.238295, sd=0.07842281)\nset.seed(NULL)\nvalue <- c(a, b, c, d, e)\ngroup <- factor(c(rep("A", N), rep("B", N), rep("C", N), rep("D", N), rep("E", N)))\ndf <- data.frame(group, value)\n<\/pre>\n\n\n
R\u3092\u4f7f\u3063\u305f\u89e3\u6790<\/h2>\n
\u3059\u3079\u3066\u6709\u610f\u6c34\u6e965%\u3067\u691c\u5b9a\u3057\u307e\u3059\u3002<\/p>\n
Welch\u306et<\/em>\u691c\u5b9a\u3092\u4f7f\u3063\u305f\u5bfe\u6bd4\u8f03<\/h3>\n
Welch\u306et<\/em>\u691c\u5b9a<\/p>\n
> pairwise.t.test(value, group, pool.sd=FALSE, p.adj="none")\n\n\tPairwise comparisons using t tests with non-pooled SD \n\ndata:  value and group \n\n  A      B      C      D     \nB 0.0826 -      -      -     \nC 0.0404 0.4960 -      -     \nD 0.0470 0.5377 0.9369 -     \nE 0.0055 0.0383 0.0974 0.1642\n\nP value adjustment method: none \n<\/pre>\n
\u7fa4A vs. \u7fa4E\u304c\\(p = 0.0055\\)\u306a\u306e\u3067\u3001\u6709\u610f\u6c34\u6e965%\u3067\u4f59\u88d5\u3067\u5e30\u7121\u4eee\u8aac\u3092\u68c4\u5374\u3067\u304d\u305d\u3046\u3067\u3059\u304c\u3001\u691c\u5b9a\u306e\u591a\u91cd\u6027\u304c\u8003\u616e\u3055\u308c\u3066\u3044\u307e\u305b\u3093\u3002<\/p>\n\n
\u5206\u6563\u5206\u6790\uff08\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u306a\u3044\uff09<\/h3>\n
\u5225\u8a18\u4e8b<\/a>\u3002\n
> oneway.test(value ~ group, var.equal=FALSE)\n\n\tOne-way analysis of means (not assuming equal variances)\n\ndata:  value and group\nF = 2.4529, num df = 4.000, denom df = 34.283, p-value = 0.06439\n<\/pre>\n
\\(p = 0.06439 \\ge 0.05\\)\u306a\u306e\u3067\u3001\u300c\u5168\u3066\u306e\u7fa4\u306e\u6bcd\u5e73\u5747\u306b\u5dee\u306f\u306a\u3044\u300d\u3068\u3044\u3046\u5e30\u7121\u4eee\u8aac\u306f\u4fdd\u7559<\/strong>\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n
Games\u2013Howell\u6cd5\uff08\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u306a\u3044\uff09\u3067\u5bfe\u6bd4\u8f03<\/h3>\n
tukey\u95a2\u6570\u306fShigenobu AOKI (2009-08-03) \u30c6\u30e5\u30fc\u30ad\u30fc\u306e\u65b9\u6cd5\u306b\u3088\u308b\u591a\u91cd\u6bd4\u8f03<\/a>\u304b\u3089\u8aad\u307f\u8fbc\u307f\u307e\u3057\u305f\u3002<\/p>\n
> tukey(value, group, method="Games-Howell")\n$result1\n        n     Mean    Variance\nGroup1 15 5.112008 0.019333908\nGroup2 15 5.183060 0.003138345\nGroup3 15 5.196527 0.002576593\nGroup4 15 5.198433 0.005945748\nGroup5 15 5.240933 0.007322983\n\n$Games.Howell\n             t       df          p\n1:2 1.83570268 18.42837 0.38416403\n1:3 2.21144418 17.66639 0.22075686\n1:4 2.10523223 21.86683 0.25336137\n1:5 3.05828117 23.27480 0.03988291\n2:3 0.68992946 27.73205 0.95700608\n2:4 0.62466841 25.55888 0.96971279\n2:5 2.19140328 24.13776 0.21669158\n3:4 0.07995019 24.21543 0.99999001\n3:5 1.72851490 22.76652 0.43735024\n4:5 1.42894943 27.70156 0.61494734\n<\/pre>\n
\u7fa4A vs. \u7fa4E\u3067\\(p = 0.03988291 < 0.05\\)\u306a\u306e\u3067\u3001\u300c\u7fa4A\u3068\u7fa4E\u306e\u6bcd\u5e73\u5747\u306b\u5dee\u306f\u306a\u3044\u300d\u3068\u3044\u3046\u5e30\u7121\u4eee\u8aac\u306f\u68c4\u5374\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n
Welch\u306et<\/em>\u691c\u5b9a\uff08\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u306a\u3044\uff09+ Bonferroni\u88dc\u6b63<\/h3>\n
> pairwise.t.test(value, group, pool.sd=FALSE, p.adj="bonferroni")\n\n\tPairwise comparisons using t tests with non-pooled SD \n\ndata:  value and group \n\n  A     B     C     D    \nB 0.826 -     -     -    \nC 0.404 1.000 -     -    \nD 0.470 1.000 1.000 -    \nE 0.055 0.383 0.974 1.000\n\nP value adjustment method: bonferroni\n<\/pre>\n
Bonferroni\u306e\u88dc\u6b63\u3067\u306f\u691c\u5b9a\u306e\u6570\\(m = 10\\)\u3067\u6709\u610f\u6c34\u6e96\u03b1\u3092\u5272\u308b\u306e\u3067\u3001\u03b1 = 0.05\/10 = 0.005\u3067\u691c\u5b9a\u3057\u307e\u3059\u3002\u4e0a\u306e\u51fa\u529b\u3067\u306f\u8a08\u7b97\u3055\u308c\u305fp\u5024\u304c10\u500d\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u7fa4A vs. \u7fa4E\u3067\\(p = 0.055 \\ge 0.05\\)\u306a\u306e\u3067\u3001\u300c\u7fa4A\u3068\u7fa4E\u306e\u6bcd\u5e73\u5747\u306b\u5dee\u306f\u306a\u3044\u300d\u3068\u3044\u3046\u5e30\u7121\u4eee\u8aac\u306f\u4fdd\u7559<\/strong>\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n
Welch\u306et<\/em>\u691c\u5b9a\uff08\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u306a\u3044\uff09+ Holm-Bonferroni\u88dc\u6b63<\/h3>\n
> pairwise.t.test(value, group, pool.sd=FALSE, p.adj="holm")\n\n\tPairwise comparisons using t tests with non-pooled SD \n\ndata:  value and group \n\n  A     B     C     D    \nB 0.496 -     -     -    \nC 0.345 1.000 -     -    \nD 0.345 1.000 1.000 -    \nE 0.055 0.345 0.496 0.657\n\nP value adjustment method: holm \n<\/pre>\n
Holm\u2013Bonferroni\u88dc\u6b63\u306f\u30b9\u30c6\u30c3\u30d7\u30c0\u30a6\u30f3\u6cd5\u3068\u547c\u3070\u308c\u308b\u65b9\u6cd5\u306e\u4e00\u3064\u3067\u3001\u691c\u5b9a\u306e\u6570\\(m = 10\\)\u3068\u3059\u308b\u3068\u4e00\u756a\u5c0f\u3055\u306ap<\/em>\u5024\u3092\u6709\u610f\u6c34\u6e96\\(\\alpha\/m\\)\u3067\u691c\u5b9a\u3057\u3066\u3001\u6b21\u306b\u5c0f\u3055\u306ap<\/em>\u5024\u3092\u6709\u610f\u6c34\u6e96\\(\\alpha\/(m-1)\\)\u3067\u691c\u5b9a\u3057\u3066\u3001\u3092\u7e70\u308a\u8fd4\u3057\u3066\u68c4\u5374\u3055\u308c\u306a\u304b\u3063\u305f\u3068\u3053\u308d\u3067\u6253\u3061\u5207\u308a\u307e\u3059\u3002\u4e0a\u306e\u51fa\u529b\u3067\u306f\u5272\u308b\u306e\u3067\u306f\u306a\u304fp\u5024\u304c\u6574\u6570\u500d\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u4eca\u56de\u306f\u7d50\u679c\u3068\u3057\u3066Bonferroni\u88dc\u6b63\u3068\u540c\u3058\u3067\u3001\u7fa4A vs. \u7fa4E\u3067\\(p = 0.055 \\ge 0.05\\)\u306a\u306e\u3067\u5e30\u7121\u4eee\u8aac\u306f\u4fdd\u7559<\/strong>\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n\n
\u4e0d\u9069\u5207\u306a\u691c\u5b9a\u6cd5: \u5206\u6563\u5206\u6790\uff08\u7b49\u5206\u6563\u3092\u4eee\u5b9a\uff09<\/h3>\n
> oneway.test(value ~ group, var.equal=TRUE)\n\n\tOne-way analysis of means\n\ndata:  value and group\nF = 4.2896, num df = 4, denom df = 70, p-value = 0.00367\n<\/pre>\n
\\(p = 0.00367 < 0.05\\)\u306a\u306e\u3067\u3001\u300c\u5168\u3066\u306e\u7fa4\u306e\u6bcd\u5e73\u5747\u306b\u5dee\u306f\u306a\u3044\u300d\u3068\u3044\u3046\u5e30\u7121\u4eee\u8aac\u306f\u68c4\u5374\u3055\u308c\u307e\u3057\u305f\u3002\u3057\u304b\u3057\u3001p<\/em>\u5024\u304cWelch\u306et<\/em>\u691c\u5b9a\u306ep<\/em>\u5024\u3088\u308a\u3082\u5c0f\u3055\u3044\u3067\u3059\u3002\u6b21\u306bTukey\u306eHSD\u6cd5\u3067\u591a\u91cd\u6bd4\u8f03\u3092\u884c\u3063\u3066\u307f\u307e\u3059\u3002<\/p>\n
\u4e0d\u9069\u5207\u306a\u691c\u5b9a\u6cd5: Tukey\u306eHSD\u691c\u5b9a\uff08\u7b49\u5206\u6563\u3092\u4eee\u5b9a\uff09\u3067\u5bfe\u6bd4\u8f03<\/h3>\n
> TukeyHSD(aov(value ~ group))\n  Tukey multiple comparisons of means\n    95% family-wise confidence level\n\nFit: aov(formula = value ~ group)\n\n$group\n           diff          lwr        upr     p adj\nB-A 0.071052607 -0.018456070 0.16056128 0.1834851\nC-A 0.084519419 -0.004989258 0.17402810 0.0732990\nD-A 0.086425114 -0.003083563 0.17593379 0.0634977\nE-A 0.128924820  0.039416143 0.21843350 0.0012702\nC-B 0.013466812 -0.076041866 0.10297549 0.9932698\nD-B 0.015372507 -0.074136171 0.10488118 0.9888662\nE-B 0.057872213 -0.031636465 0.14738089 0.3758897\nD-C 0.001905695 -0.087602983 0.09141437 0.9999971\nE-C 0.044405401 -0.045103276 0.13391408 0.6365763\nE-D 0.042499706 -0.047008971 0.13200838 0.6738995\n<\/pre>\n
\u7fa4A vs. \u7fa4E\u3067\\(p = 0.0012702 < 0.05\\)\u306a\u306e\u3067\u3001\u300c\u7fa4A\u3068\u7fa4E\u306e\u6bcd\u5e73\u5747\u306b\u5dee\u306f\u306a\u3044\u300d\u3068\u3044\u3046\u5e30\u7121\u4eee\u8aac\u306f\u68c4\u5374\u3055\u308c\u307e\u3057\u305f\u3002\u306a\u305cWelch\u306et<\/em>\u691c\u5b9a\u3067\u306eA\u7fa4 vs. E\u7fa4\u306ep<\/em>\u5024 (0.0055) \u306e\u7d045\u5206\u306e1\u3068\u6975\u7aef\u306b\u5c0f\u3055\u3044p<\/em>\u5024\u304c\u51fa\u308b\u304b\u3068\u3044\u3048\u3070\u3001Tukey\u6cd5\u306e\u691c\u5b9a\u7d71\u8a08\u91cfq<\/em>
$$q = \\frac{|\\bar{X_A} – \\bar{X_B}|}{\\sqrt{\\frac{S_R^2}{2}\\left(\\frac{1}{n_A}+\\frac{1}{n_B}\\right)}} $$
\u3092\u8a08\u7b97\u3059\u308b\u969b\u306b\u8aa4\u5dee\u5206\u6563\\(s_R^2\\)\uff08\u5404\u7fa4\u306e\u4e0d\u504f\u5206\u6563\u306e\u5e73\u5747\u5024\uff09\u3092\u4f7f\u7528\u3059\u308b\u305f\u3081\u3067\u3059\u3002A\u7fa4\u3068E\u7fa4\u306e\u5206\u6563\u306f\u5927\u304d\u3044\u306e\u306b\u3001B\u3001C\u3001D\u7fa4\u306e\u5206\u6563\u304c\u5c0f\u3055\u3044\u306e\u3067\u3001\u7d50\u679c\u3068\u3057\u3066p<\/em>\u5024\u304c\u975e\u5e38\u306b\u5c0f\u3055\u304f\u306a\u3063\u3066\u3057\u307e\u3044\u307e\u3057\u305f\u3002\u3053\u306e\u3088\u3046\u306b\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u305f\u5206\u6563\u5206\u6790\u3068Tukey\u306eHSD\u691c\u5b9a\u3092\u3001n<\/em>\u304c\u5c0f\u3055\u304f\u4e0d\u7b49\u5206\u6563\u306e\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u4f7f\u3046\u3079\u304d\u3067\u306a\u3044\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3057\u305f\u3002\u306f\u3058\u3081\u304b\u3089\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3057\u306a\u3044Games\u2013Howell\u6cd5\u3092\u4f7f\u7528\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n\n
\uff08\u4e86\uff09<\/p>\n\n\n\n","protected":false},"excerpt":{"rendered":"
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