{"id":420,"date":"2019-09-08T09:42:29","date_gmt":"2019-09-08T00:42:29","guid":{"rendered":"https:\/\/best-biostatistics.com\/toukei-er\/entry\/t-test-linear-regression-anova\/"},"modified":"2024-10-06T16:46:34","modified_gmt":"2024-10-06T07:46:34","slug":"t-test-linear-regression-anova","status":"publish","type":"post","link":"https:\/\/best-biostatistics.com\/toukei-er\/entry\/t-test-linear-regression-anova\/","title":{"rendered":"t \u691c\u5b9a\u3068\u5206\u6563\u5206\u6790\u3068\u56de\u5e30\u5206\u6790\u306f\u540c\u3058\u3053\u3068\u3092\u3057\u3066\u3044\u308b"},"content":{"rendered":"\n

\u5206\u6563\u5206\u6790\u3068\u56de\u5e30\u5206\u6790\u3068\uff54\u691c\u5b9a\u306f\u540c\u3058\u3082\u306e\u3067\u3042\u308b\u3002<\/p>\n\n\n\n

\u3053\u308c\u304c\u308f\u304b\u308c\u3070\u3001\u91cd\u56de\u5e30\u5206\u6790\uff08\u5171\u5206\u6563\u5206\u6790\uff09\u304c\u4ea4\u7d61\u56e0\u5b50\u8abf\u6574\u3057\u305f\u7fa4\u9593\u6bd4\u8f03\u306b\u4f7f\u308f\u308c\u308b\u7406\u7531\u304c\u308f\u304b\u308b\u3060\u308d\u3046\u3002<\/p>\n\n\n\n\n\n\n\n

\uff54\u691c\u5b9a\u306f\u56de\u5e30\u5206\u6790\u3067\u3082\u3067\u304d\u308b\u3057\u3001\u5206\u6563\u5206\u6790\u306f\u4e8c\u7fa4\u3067\u3082\u3067\u304d\u308b<\/h2>\n\n\n\n

\u3053\u3053\u3067\u3001\uff54\u691c\u5b9a\u306f\u3001\u3044\u308f\u3086\u308b t \u691c\u5b9a\u306e\u3053\u3068\u3067\u3042\u308a\u3001\u72ec\u7acb\u3057\u305f\u4e8c\u7fa4\u306e\u5e73\u5747\u5024\u306e\u5dee\u306e\u691c\u5b9a\u306e\u3053\u3068\u3092\u8a00\u3063\u3066\u3044\u308b\u3002<\/p>\n\n\n\n

\uff54\u691c\u5b9a\u306f\u56de\u5e30\u5206\u6790\u3067\u3082\u3067\u304d\u308b\u3002<\/p>\n\n\n\n

\u5206\u6563\u5206\u6790\u3067\u3082\u3067\u304d\u308b\u3002<\/p>\n\n\n\n

R\u306e\u95a2\u6570\u3092\u4f7f\u3063\u3066\u8a08\u7b97\u3057\u3066\u307f\u308b\u3002<\/p>\n\n\n\n

\u4f7f\u3046\u30c7\u30fc\u30bf\u306fsleep\u3068\u3044\u3046\u30c7\u30fc\u30bf\u3067\u3001extra\u3068\u3044\u3046\u5909\u6570\u3092group\u9593\u3067\u6bd4\u8f03\u3059\u308b\u3002<\/p>\n\n\n\n

\uff54\u691c\u5b9a\u306e\u7d50\u679c<\/h3>\n\n\n\n

t.test()\u3092\u4f7f\u3063\u3066\u8a08\u7b97\u3059\u308b\u3002<\/p>\n\n\n\n

\u7b49\u5206\u6563\u6027\u3092\u4eee\u5b9a\u3057\u305f\u65b9\u6cd5\u306b\u3059\u308b\uff08var=T\uff09\u3002<\/p>\n\n\n\n

t.test<\/span>(<\/span>extra~<\/span>group,<\/span> var=<\/span>T<\/span>,<\/span> data=<\/span>sleep)<\/span>\n<\/code><\/pre>\n\n\n\n
\u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3002<\/p>\n\n\n\n
Group1\u306e\u5e73\u5747\u304c0.75\u3001Group2\u306e\u5e73\u5747\u304c2.33\u3067\u3001\u304b\u306a\u308a\u96e2\u308c\u3066\u306f\u3044\u305d\u3046\u3060\u304c\u3001\u30b5\u30f3\u30d7\u30eb\u30b5\u30a4\u30ba\u304c\u5c0f\u3055\u3044\u304b\u3089\u304b\u3001\u5206\u6563\u304c\u5927\u304d\u3044\u305f\u3081\u304b\u7d71\u8a08\u5b66\u7684\u6709\u610f\u3067\u306f\u306a\u3044\uff08p=0.07919\uff09\u3002<\/p>\n\n\n\n
><\/span> t.test<\/span>(<\/span>extra~<\/span>group,<\/span> var=<\/span>T<\/span>,<\/span> data=<\/span>sleep)<\/span>\nTwo Sample t-<\/span>test\ndata:<\/span>  extra by group\nt =<\/span> -<\/span>1.8608<\/span>,<\/span> df =<\/span> 18<\/span>,<\/span> p-<\/span>value =<\/span> 0.07919<\/span>\nalternative hypothesis:<\/span> true difference in<\/span> means is not equal to 0<\/span>\n95<\/span> percent confidence interval:<\/span>\n-<\/span>3.363874<\/span>  0.203874<\/span>\nsample estimates:<\/span>\nmean in<\/span> group 1<\/span> mean in<\/span> group 2<\/span>\n0.75<\/span>            2.33<\/span>\n<\/code><\/pre>\n\n\n\n
\u56de\u5e30\u5206\u6790\u306e\u7d50\u679c<\/h3>\n\n\n\n
lm()\u3092\u4f7f\u3063\u3066\u56de\u5e30\u5206\u6790\u3092\u884c\u3046\u3002<\/p>\n\n\n\n
lm()\u306e\u306a\u304b\u306e\u300c~\u300d\u3092\u631f\u3093\u3067\u3001\u5de6\u304c\u9023\u7d9a\u91cf(\u76ee\u7684\u5909\u6570\u3001\u5f93\u5c5e\u5909\u6570\u3001Y)\u3001\u53f3\u304c\u30b0\u30eb\u30fc\u30d7\u5909\u6570\uff08\u8aac\u660e\u5909\u6570\u3001\u72ec\u7acb\u5909\u6570\u3001X\uff09\u3060\u3002<\/p>\n\n\n\n
\u30ab\u30c6\u30b4\u30ea\u5909\u6570\u3092\u8aac\u660e\u5909\u6570\u306b\u3057\u305f\u56de\u5e30\u5206\u6790\u3068\u3044\u3046\u3053\u3068\u3067\u3042\u308b\u3002<\/p>\n\n\n\n
lm.res1 <-<\/span> lm<\/span>(<\/span>extra~<\/span>group,<\/span> data=<\/span>sleep)<\/span>\nsummary<\/span>(<\/span>lm.res1)<\/span>\n<\/code><\/pre>\n\n\n\n
\u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3002<\/p>\n\n\n\n
\uff54\u691c\u5b9a\u306e\u7d50\u679c\u306b\u76f8\u5f53\u3059\u308b\u306e\u304c\u3001Coefficients:\u306egroup2\u884c\u53f3\u7aef\u306e\u300c0.0792 .\u300d<\/p>\n\n\n\n
p=0.07919\u306e\u6700\u5f8c\u306e\u6841\u3092\u56db\u6368\u4e94\u5165\u3059\u308c\u3070\u3001\u540c\u3058\u5024\u3060\u3002<\/p>\n\n\n\n
\u6570\u5b57\u306e\u6700\u5f8c\u306e\u300c.\u300d\u306f0.05\u4ee5\u4e0a\u30010.1\u672a\u6e80\u3092\u610f\u5473\u3057\u3066\u3044\u308b\u3002<\/p>\n\n\n\n
\u305d\u3057\u3066\u5b9f\u306f\u5206\u6563\u5206\u6790\u306e\u7d50\u679c\u3082\u51fa\u3066\u3044\u308b\u3002<\/p>\n\n\n\n
\u6700\u4e0b\u6bb5\u306eF-statistic:\u306e\u884c\u53f3\u7aef\u300cp-value: 0.07919\u300d\u3067\u3042\u308b\u3002<\/p>\n\n\n\n
><\/span> lm.res1 <-<\/span> lm<\/span>(<\/span>extra~<\/span>group,<\/span> data=<\/span>sleep)<\/span>\n><\/span> summary<\/span>(<\/span>lm.res1)<\/span>\nCall:<\/span>\nlm<\/span>(<\/span>formula =<\/span> extra ~<\/span> group,<\/span> data =<\/span> sleep)<\/span>\nResiduals:<\/span>\nMin     1Q Median     3Q    Max\n-<\/span>2.430<\/span> -<\/span>1.305<\/span> -<\/span>0.580<\/span>  1.455<\/span>  3.170<\/span>\nCoefficients:<\/span>\nEstimate Std. Error t value Pr<\/span>(<\/span>>|<\/span>t|<\/span>)<\/span>\n(<\/span>Intercept)<\/span>   0.7500<\/span>     0.6004<\/span>   1.249<\/span>   0.2276<\/span>\ngroup2        1.5800<\/span>     0.8491<\/span>   1.861<\/span>   0.0792<\/span> .\n---<\/span>\nSignif. codes:<\/span>  0<\/span> \u2018***<\/span>\u2019 0.001<\/span> \u2018**<\/span>\u2019 0.01<\/span> \u2018*<\/span>\u2019 0.05<\/span> \u2018.\u2019 0.1<\/span> \u2018 \u2019 1<\/span>\nResidual standard error:<\/span> 1.899<\/span> on 18<\/span> degrees of freedom\nMultiple R-<\/span>squared:<\/span>  0.1613<\/span>,<\/span>    Adjusted R-<\/span>squared:<\/span>  0.1147<\/span>\nF<\/span>-<\/span>statistic:<\/span> 3.463<\/span> on 1<\/span> and 18<\/span> DF,<\/span>  p-<\/span>value:<\/span> 0.07919<\/span>\n<\/code><\/pre>\n\n\n\n
\u5206\u6563\u5206\u6790\u306e\u7d50\u679c<\/h3>\n\n\n\n
\u5206\u6563\u5206\u6790\u3092\u4f7f\u3046\u65b9\u6cd5\u306f\u3044\u304f\u3064\u304b\u3042\u308b\u3002<\/p>\n\n\n\n
    \n
  1. aov()\u3092\u4f7f\u3046\u65b9\u6cd5<\/li>\n\n\n\n
  2. lm()\u306e\u7d50\u679c\u3092\u4f7f\u3046\u65b9\u6cd5 \u2015 anova()\u3092\u4f7f\u3046<\/li>\n\n\n\n
  3. lm()\u306e\u7d50\u679c\u3092\u4f7f\u3046\u65b9\u6cd5 \u305d\u306e2 \u2015 car \u30d1\u30c3\u30b1\u30fc\u30b8\u306eAnova()\u3092\u4f7f\u3046<\/li>\n<\/ol>\n\n\n\n
    # 1.<\/span>\naov1 <-<\/span> aov<\/span>(<\/span>extra ~<\/span> group,<\/span> data=<\/span>sleep)<\/span>\nsummary<\/span>(<\/span>aov1)<\/span>\n# 2. <\/span>\nanova<\/span>(<\/span>lm.res1)<\/span>\n# 3.<\/span>\nlibrary<\/span>(<\/span>car)<\/span>\nAnova<\/span>(<\/span>lm.res1)<\/span>\n<\/code><\/pre>\n\n\n\n
    aov()\u3092\u4f7f\u3063\u305f # 1. \u306f\u3001group\u884c\u306e\u53f3\u7aef\u300c0.0792 .\u300d\u304c\u7b54\u3048\u3002<\/p>\n\n\n\n
    anova()\u3092\u4f7f\u3063\u305f # 2. \u306f Response: extra\u306egroup\u884c\u306e\u53f3\u7aef\u300c0.07919 .\u300d\u304c\u6c42\u3081\u305f\u5024\u3002<\/p>\n\n\n\n
    Anova()\u3092\u4f7f\u3063\u305f # 3. \u3082Response: extra\u306egroup\u884c\u53f3\u7aef\u300c0.07919 .\u300d\u304c\u5fc5\u8981\u306a\u5024\u3060\u3002<\/p>\n\n\n\n
    \u5171\u5206\u6563\u5206\u6790\u306e\u3068\u304d\u306fAnova()\u3092\u4f7f\u3046\u307b\u3046\u304c\u3088\u3044\u3002<\/p>\n\n\n\n
    \u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306e\u6642\u306fanova()\u3068\u540c\u3058\u7d50\u679c\u306b\u306a\u308b\u306e\u3067\u3069\u3061\u3089\u3092\u4f7f\u3063\u3066\u3082OK\u3002<\/p>\n\n\n\n
    # 1.<\/span>\n><\/span> aov1 <-<\/span> aov(<\/span>extra ~<\/span> group,<\/span> data=<\/span>sleep)<\/span>\n><\/span> summary(<\/span>aov1)<\/span>\nDf Sum Sq Mean Sq F<\/span> value Pr(>F)<\/span>\ngroup        1<\/span>  12.48<\/span>  12.482<\/span>   3.463<\/span> 0.0792<\/span> .\nResiduals   18<\/span>  64.89<\/span>   3.605<\/span>\n---<\/span>\nSignif. codes:<\/span>  0<\/span> \u2018***<\/span>\u2019 0.001<\/span> \u2018**<\/span>\u2019 0.01<\/span> \u2018*<\/span>\u2019 0.05<\/span> \u2018.\u2019 0.1<\/span> \u2018 \u2019 1<\/span>\n\n# 2.<\/span>\n><\/span> anova(<\/span>lm.res1)<\/span>\nAnalysis of Variance Table\nResponse:<\/span> extra\nDf Sum Sq Mean Sq F<\/span> value  Pr(>F)<\/span>\ngroup      1<\/span> 12.482<\/span> 12.4820<\/span>  3.4626<\/span> 0.07919<\/span> .\nResiduals 18<\/span> 64.886<\/span>  3.6048<\/span>\n---<\/span>\nSignif. codes:<\/span>  0<\/span> \u2018***<\/span>\u2019 0.001<\/span> \u2018**<\/span>\u2019 0.01<\/span> \u2018*<\/span>\u2019 0.05<\/span> \u2018.\u2019 0.1<\/span> \u2018 \u2019 1<\/span>\n\n# 3.<\/span>\n><\/span> library(<\/span>car)<\/span>\nLoading required package:<\/span> carData\n><\/span> Anova(<\/span>lm.res1)<\/span>\nAnova Table (<\/span>Type II tests)<\/span>\nResponse:<\/span> extra\nSum Sq Df F<\/span> value  Pr(>F)<\/span>\ngroup     12.482<\/span>  1<\/span>  3.4626<\/span> 0.07919<\/span> .\nResiduals 64.886<\/span> 18<\/span>\n---<\/span>\nSignif. codes:<\/span>  0<\/span> \u2018***<\/span>\u2019 0.001<\/span> \u2018**<\/span>\u2019 0.01<\/span> \u2018*<\/span>\u2019 0.05<\/span> \u2018.\u2019 0.1<\/span> \u2018 \u2019 1<\/span>\n<\/code><\/pre>\n\n\n\n
    \u5c0f\u307e\u3068\u3081<\/h3>\n\n\n\n
    \uff54\u691c\u5b9a\u3082\u56de\u5e30\u5206\u6790\u3082\u5206\u6563\u5206\u6790\u3082\u540c\u3058\u300cp=0.07919\u300d\u3067\u3042\u3063\u305f\u3002<\/p>\n\n\n\n
    \u3064\u307e\u308a\u3001\uff54\u691c\u5b9a\u3082\u56de\u5e30\u5206\u6790\u3082\u5206\u6563\u5206\u6790\u3082\u3001\u8981\u306f\u300c\u540c\u3058\u300d\u3002<\/p>\n\n\n\n
    \u300c\uff54\u691c\u5b9a\u300d\u3068\u300c\u56de\u5e30\u5206\u6790\u3001\u5206\u6563\u5206\u6790\u300d\u306e\u9055\u3044\u306f\u3001\u62e1\u5f35\u6027\u3060\u3002<\/p>\n\n\n\n
    \u56de\u5e30\u5206\u6790\u3084\u5206\u6563\u5206\u6790\u306f\u8aac\u660e\u5909\u6570\u3092\u5897\u3084\u3057\u3066\u3001\u91cd\u56de\u5e30\u5206\u6790\u3084\u5171\u5206\u6563\u5206\u6790\uff08ANCOVA, Analysis of covariance\uff09\u306b\u62e1\u5f35\u3067\u304d\u308b\u70b9\u304c\u512a\u308c\u3066\u3044\u308b\u3002<\/p>\n\n\n\n
    \u5206\u6563\u5206\u6790\u3068\u56de\u5e30\u5206\u6790\u3068\uff54\u691c\u5b9a\u304c\u540c\u3058\u3053\u3068\u3092\u793a\u3059\u5225\u306e\u4f8b<\/h2>\n\n\n\n
    MASS \u30d1\u30c3\u30b1\u30fc\u30b8\u306e birthwt \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e smoke\uff08\u6bcd\u89aa\u306e\u55ab\u7159\uff09\u3068 bwt \uff08\u5150\u306e\u51fa\u751f\u4f53\u91cd\uff09\u3092\u4f7f\u3063\u3066\u691c\u5b9a\u3057\u3066\u307f\u308b\u3002<\/p>\n\n\n\n
    \uff54\u691c\u5b9a\u306e\u7d50\u679c<\/h3>\n\n\n\n
    library(MASS)\u3067\u3001MASS \u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u547c\u3073\u51fa\u3059\u3002<\/p>\n\n\n\n
    t.test()\u3092\u4f7f\u3046\u3002\u7b49\u5206\u6563\u3092\u4eee\u5b9a\u3059\u308b\uff08var=T\uff09\u3002<\/p>\n\n\n\n
    library<\/span>(<\/span>MASS)<\/span>\nt.test<\/span>(<\/span>bwt~<\/span>smoke,<\/span> var=<\/span>T<\/span>,<\/span> data=<\/span>birthwt)<\/span>\n<\/code><\/pre>\n\n\n\n
    \u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3001smoke=1\uff08\u55ab\u7159\uff09\u30b0\u30eb\u30fc\u30d7\u306e\u307b\u3046\u304c\u5e73\u5747\u5024\u304c300\u30b0\u30e9\u30e0\u7a0b\u5ea6\u4f4e\u304f\u3001\u7d71\u8a08\u5b66\u7684\u6709\u610f\u306b\u7570\u306a\u3063\u3066\u3044\u305f\uff08p = 0.008667\uff09\u3002<\/p>\n\n\n\n
    ><\/span> library<\/span>(<\/span>MASS)<\/span>\n><\/span> t.test<\/span>(<\/span>bwt~<\/span>smoke,<\/span> var=<\/span>T<\/span>,<\/span> data=<\/span>birthwt)<\/span>\nTwo Sample t-<\/span>test\ndata:<\/span>  bwt by smoke\nt =<\/span> 2.6529<\/span>,<\/span> df =<\/span> 187<\/span>,<\/span> p-<\/span>value =<\/span> 0.008667<\/span>\nalternative hypothesis:<\/span> true difference in<\/span> means is not equal to 0<\/span>\n95<\/span> percent confidence interval:<\/span>\n72.75612<\/span> 494.79735<\/span>\nsample estimates:<\/span>\nmean in<\/span> group 0<\/span> mean in<\/span> group 1<\/span>\n3055.696<\/span>        2771.919<\/span>\n<\/code><\/pre>\n\n\n\n
    \u56de\u5e30\u5206\u6790\u306e\u7d50\u679c<\/h3>\n\n\n\n
    lm()\u3092\u4f7f\u3046\u3002<\/p>\n\n\n\n
    bwt \u3092\u300c~\u300d\u306e\u5de6\u306b\u3001smoke \u3092\u300c~\u300d\u306e\u53f3\u306b\u914d\u7f6e\u3059\u308b\u3002<\/p>\n\n\n\n
    summary<\/span>(<\/span>lm.res2 <-<\/span> lm<\/span>(<\/span>bwt~<\/span>smoke,<\/span> data=<\/span>birthwt))<\/span>\n<\/code><\/pre>\n\n\n\n
    smoke \u884c\u306e\u53f3\u7aef\u306ep\u5024\u304c\u300c0.00867 ** \u300d\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308b\u3002\u300c**\u300d\u306f\u3053\u3053\u3067\u306f0.01\u672a\u6e80\u3067\u30010.001\u4ee5\u4e0a\u3067\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u308b\u3002<\/p>\n\n\n\n
    ><\/span> summary<\/span>(<\/span>lm.res2 <-<\/span> lm<\/span>(<\/span>bwt~<\/span>smoke,<\/span> data=<\/span>birthwt))<\/span>\nCall:<\/span>\nlm<\/span>(<\/span>formula =<\/span> bwt ~<\/span> smoke,<\/span> data =<\/span> birthwt)<\/span>\nResiduals:<\/span>\nMin      1Q  Median      3Q     Max\n-<\/span>2062.9<\/span>  -<\/span>475.9<\/span>    34.3<\/span>   545.1<\/span>  1934.3<\/span>\nCoefficients:<\/span>\nEstimate Std. Error t value Pr<\/span>(<\/span>>|<\/span>t|<\/span>)<\/span>\n(<\/span>Intercept)<\/span>  3055.70<\/span>      66.93<\/span>  45.653<\/span>  <<\/span> 2e-16<\/span> ***<\/span>\nsmoke        -<\/span>283.78<\/span>     106.97<\/span>  -<\/span>2.653<\/span>  0.00867<\/span> **<\/span>\n---<\/span>\nSignif. codes:<\/span>  0<\/span> \u2018***<\/span>\u2019 0.001<\/span> \u2018**<\/span>\u2019 0.01<\/span> \u2018*<\/span>\u2019 0.05<\/span> \u2018.\u2019 0.1<\/span> \u2018 \u2019 1<\/span>\nResidual standard error:<\/span> 717.8<\/span> on 187<\/span> degrees of freedom\nMultiple R-<\/span>squared:<\/span>  0.03627<\/span>,<\/span>   Adjusted R-<\/span>squared:<\/span>  0.03112<\/span>\nF<\/span>-<\/span>statistic:<\/span> 7.038<\/span> on 1<\/span> and 187<\/span> DF,<\/span>  p-<\/span>value:<\/span> 0.008667<\/span>\n<\/code><\/pre>\n\n\n\n
    \u5206\u6563\u5206\u6790\u306e\u7d50\u679c<\/h3>\n\n\n\n
    \u5206\u6563\u5206\u6790\u306fanova()\u3068Anova()\u306e\u4e8c\u3064\u3067\u8a08\u7b97\u3057\u3066\u307f\u308b\u3002\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306a\u306e\u3067\u7d50\u679c\u306f\u540c\u3058\u306b\u306a\u308b\u3002<\/p>\n\n\n\n
    anova<\/span>(<\/span>lm.res2)<\/span>\nlibrary<\/span>(<\/span>car)<\/span>\nAnova<\/span>(<\/span>lm.res2)<\/span>\n<\/code><\/pre>\n\n\n\n
    \u7d50\u679c\u306f\u300c0.008667 **\u300d\u3067\u3001\u4e8c\u3064\u3068\u3082\u540c\u3058\u3067\u3042\u3063\u305f\u3002<\/p>\n\n\n\n
    ><\/span> anova<\/span>(<\/span>lm.res2)<\/span>\nAnalysis of Variance Table\nResponse:<\/span> bwt\nDf   Sum Sq Mean Sq F<\/span> value   Pr<\/span>(<\/span>><\/span>F<\/span>)<\/span>\nsmoke       1<\/span>  3625946<\/span> 3625946<\/span>  7.0378<\/span> 0.008667<\/span> **<\/span>\nResiduals 187<\/span> 96343710<\/span>  515207<\/span>\n---<\/span>\nSignif. codes:<\/span>  0<\/span> \u2018***<\/span>\u2019 0.001<\/span> \u2018**<\/span>\u2019 0.01<\/span> \u2018*<\/span>\u2019 0.05<\/span> \u2018.\u2019 0.1<\/span> \u2018 \u2019 1<\/span>\n><\/span> library<\/span>(<\/span>car)<\/span>\n><\/span> Anova<\/span>(<\/span>lm.res2)<\/span>\nAnova Table <\/span>(<\/span>Type II tests)<\/span>\nResponse:<\/span> bwt\nSum Sq  Df F<\/span> value   Pr<\/span>(<\/span>><\/span>F<\/span>)<\/span>\nsmoke      3625946<\/span>   1<\/span>  7.0378<\/span> 0.008667<\/span> **<\/span>\nResiduals 96343710<\/span> 187<\/span>\n---<\/span>\nSignif. codes:<\/span>  0<\/span> \u2018***<\/span>\u2019 0.001<\/span> \u2018**<\/span>\u2019 0.01<\/span> \u2018*<\/span>\u2019 0.05<\/span> \u2018.\u2019 0.1<\/span> \u2018 \u2019 1<\/span>\n<\/code><\/pre>\n\n\n\n
    \u3053\u3061\u3089\u306e\u4f8b\u3082\u3001\uff54\u691c\u5b9a\u3001\u56de\u5e30\u5206\u6790\u3001\u5206\u6563\u5206\u6790\u3059\u3079\u3066\u540c\u3058\u7d50\u679c\u306b\u306a\u3063\u305f\u3002<\/p>\n\n\n\n
    \uff1e\uff1e\u3082\u3046\u7d71\u8a08\u3067\u60a9\u3080\u306e\u306f\u7d42\u308f\u308a\u306b\u3057\u307e\u305b\u3093\u304b\uff1f\u00a0<\/a><\/strong><\/span><\/p>\r\n\"\"<\/a>\r\n
    \u21911\u4e07\u4eba\u4ee5\u4e0a\u306e\u533b\u7642\u5f93\u4e8b\u8005\u304c\u8cfc\u8aad\u4e2d<\/span><\/strong><\/span><\/p><\/div>
    \u307e\u3068\u3081<\/h2>\n\n\n\n
    R\u306e\u7d44\u307f\u8fbc\u307f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\uff12\u3064\u3092\u4f7f\u3063\u3066\u3001\uff54\u691c\u5b9a\u3001\u56de\u5e30\u5206\u6790\u3001\u5206\u6563\u5206\u6790\u306e\u7d50\u679c\u3092\u6bd4\u8f03\u3057\u3066\u307f\u305f\u3002<\/p>\n\n\n\n
    \u3044\u305a\u308c\u306b\u304a\u3044\u3066\u3082\u3001\uff54\u691c\u5b9a\u3001\u56de\u5e30\u5206\u6790\u3001\u5206\u6563\u5206\u6790\u306e3\u3064\u306f\u3001\u307e\u3063\u305f\u304f\u540c\u3058\u7d50\u679c\u306b\u306a\u3063\u305f\u3002<\/p>\n\n\n\n
    \u3064\u307e\u308a\u3001\u3053\u306e3\u3064\u306f\u540c\u3058\u3053\u3068\u3092\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308b\u3002<\/p>\n\n\n\n
    \u56de\u5e30\u5206\u6790\u3001\u5206\u6563\u5206\u6790\u304c\uff54\u691c\u5b9a\u3068\u9055\u3046\u3068\u3053\u308d\u306f\u3001\u591a\u5909\u91cf\u89e3\u6790\u306b\u62e1\u5f35\u3067\u304d\u308b\u3068\u3053\u308d\u3002<\/p>\n\n\n\n
    \u3053\u306e\u3053\u3068\u304c\u308f\u304b\u308c\u3070\u3001\u5358\u5909\u91cf\u3067\uff54\u691c\u5b9a\u3092\u884c\u3044\u3001\u591a\u5909\u91cf\u3067\u306f\u91cd\u56de\u5e30\u5206\u6790\u3084\u5171\u5206\u6563\u5206\u6790\u3092\u884c\u3046\u7406\u7531\u3082\u7406\u89e3\u3067\u304d\u308b\u3060\u308d\u3046\u3002<\/p>\n\n\n\n
    \u4f55\u3089\u304b\u5f79\u306b\u7acb\u3066\u3070\u5e78\u3044\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
    \u5206\u6563\u5206\u6790\u3068\u56de\u5e30\u5206\u6790\u3068\uff54\u691c\u5b9a\u306f\u540c\u3058\u3082\u306e\u3067\u3042\u308b\u3002 \u3053\u308c\u304c\u308f\u304b\u308c\u3070\u3001\u91cd\u56de\u5e30\u5206\u6790\uff08\u5171\u5206\u6563\u5206\u6790\uff09\u304c\u4ea4\u7d61\u56e0\u5b50\u8abf\u6574\u3057\u305f\u7fa4\u9593\u6bd4\u8f03\u306b\u4f7f\u308f\u308c\u308b\u7406\u7531\u304c\u308f\u304b\u308b\u3060\u308d\u3046\u3002<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"swell_btn_cv_data":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[5,35,34,23],"tags":[],"class_list":["post-420","post","type-post","status-publish","format-standard","hentry","category-r","category-t-","category-34","category-23"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/420","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/comments?post=420"}],"version-history":[{"count":3,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/420\/revisions"}],"predecessor-version":[{"id":2441,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/420\/revisions\/2441"}],"wp:attachment":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/media?parent=420"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/categories?post=420"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/tags?post=420"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}