{"id":17378,"date":"2023-07-06T06:00:00","date_gmt":"2023-07-05T21:00:00","guid":{"rendered":"https:\/\/www.muscle-hypertrophy.com\/?p=17378"},"modified":"2023-02-17T09:33:13","modified_gmt":"2023-02-17T00:33:13","slug":"%e7%86%b1%e4%b8%ad%e7%97%87%e6%90%ac%e9%80%81%e4%ba%ba%e5%93%a1%e6%95%b0%e3%81%ae%e3%82%aa%e3%83%95%e3%82%bb%e3%83%83%e3%83%88%e9%a0%85%e3%82%9265%e6%ad%b3%e4%bb%a5%e4%b8%8a%e4%ba%ba%e5%8f%a3%e3%81%ab","status":"publish","type":"post","link":"https:\/\/www.muscle-hypertrophy.com\/?p=17378","title":{"rendered":"\u71b1\u4e2d\u75c7\u642c\u9001\u4eba\u54e1\u6570\u306e\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u309265\u6b73\u4ee5\u4e0a\u4eba\u53e3\u306b\u5909\u66f4\u3057\u3066\u307f\u308b"},"content":{"rendered":"\n

\u3000\u4ee5\u524d\u306e\u8a18\u4e8b\u3067\u306f\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u306e\u5bfe\u6570\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u4e00\u822c\u5316\u7dda\u5f62\u56de\u5e30\u5206\u6790<\/a>\u3092\u884c\u3063\u305f\uff0e\u5b9f\u969b\u306e\u3068\u3053\u308d\uff0c\u5e74\u4ee3\u5225\u306e\u642c\u9001\u4eba\u54e1\u3068\u3057\u3066\u306f65\u6b73\u4ee5\u4e0a\u306e\u9ad8\u9f62\u8005\u304c\u5727\u5012\u7684\u306b\u591a\u3044\uff0e\u305d\u306e\u305f\u3081\uff0c\u6771\u4eac\u306a\u3069\u52b4\u50cd\u4eba\u53e3\u306e\u591a\u3044\u3068\u3053\u308d\u3067\u306f\u4e88\u6e2c\u6027\u80fd\u304c\u60aa\u5316\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\uff0e\u4eca\u56de\u306f\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u306e\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u30923\u533a\u5206\u306b\u5206\u3051\uff0c65\u6b73\u4ee5\u4e0a\u4eba\u53e3\u306e\u5bfe\u6570\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u63a1\u7528\u3057\u3066\u307f\u305f\u3068\u3053\u308d\u4e88\u6e2c\u6027\u80fd\u304c\u6539\u5584\u3057\u305f\u3068\u601d\u308f\u308c\u305f\u306e\u3067\u8a18\u4e8b\u3068\u3057\u305f\uff0e<\/p>\n

<\/p>\n

\u30c7\u30fc\u30bf\u306e\u6e96\u5099<\/h2>\n

\u30c7\u30fc\u30bf\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/h3>\n

\u3000\u4f8b\u306b\u3088\u3063\u3066e-Stat<\/a>\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3059\u308b\uff0e\u8a73\u7d30\u306f\u3053\u306e\u8a18\u4e8b<\/a>\u306b\u3042\u308b\u901a\u308a\u3067\uff0c15\u6b73\u672a\u6e80\u4eba\u53e3\uff0c15\u6b73\u4ee5\u4e0a65\u6b73\u672a\u6e80\u4eba\u53e3\uff0c65\u6b73\u4ee5\u4e0a\u4eba\u53e3\u3092\u8ffd\u52a0\u3059\u308b\uff0e<\/p>\n

Power Query\u3067\u30c7\u30fc\u30bf\u3092\u6574\u5f62\u3059\u308b<\/h3>\n

\u3000\u5fc5\u8981\u306a\u5217\u306f\u5e74\uff0c\u90fd\u9053\u5e9c\u770c\u30b3\u30fc\u30c9<\/a>\uff0c\u90fd\u9053\u5e9c\u770c\u540d\uff0c\u7dcf\u4eba\u53e3\uff0c15\u6b73\u672a\u6e80\u4eba\u53e3\uff0c15\u6b73\u4ee5\u4e0a64\u6b73\u672a\u6e80\u4eba\u53e3\uff0c65\u6b73\u4ee5\u4e0a\u4eba\u53e3\u3067\u3042\u308b\uff0e<\/p>\n

SQL Server\u306b\u30a4\u30f3\u30dd\u30fc\u30c8<\/h3>\n

\u3000\u30a6\u30a3\u30b6\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u71b1\u4e2d\u75c7\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\uff0e<\/p>\n

\u30af\u30a8\u30ea<\/h3>\n

\u3000\u4e0b\u8a18\u30af\u30a8\u30ea\u3092\u767a\u884c\u3057\u3066\u62bd\u51fa\u3055\u308c\u305f\u30c7\u30fc\u30bf\u3092EXCEL\u306b\u30b3\u30d4\u30da\u3059\u308b\uff0e<\/p>\n

USE HeatStrokeDB;\nGO\nSELECT\tYear(H.\u65e5\u4ed8)\tAS Year\n,\tMONTH(H.\u65e5\u4ed8)\tAS Month\n,\tH.\u65e5\u4ed8\tAS Date\n,\tP.\u90fd\u9053\u5e9c\u770c\u30b3\u30fc\u30c9\tAS PrefCode\n,\tP.\u90fd\u9053\u5e9c\u770c\tAS Pref\n,\tT.\u65e5\u6700\u9ad8\u6c17\u6e29\tAS Temp\n,\tV.\u65e5\u5e73\u5747\u84b8\u6c17\u5727\tAS Vapor\n,\tP.[15\u6b73\u672a\u6e80\u4eba\u53e3]\tAS Pop14\n,\tP.[15\uff5e64\u6b73\u4eba\u53e3]\tAS Pop14_65\n,\tP.[65\u6b73\u4ee5\u4e0a\u4eba\u53e3]\tAS Pop65\n,\tH.[\u642c\u9001\u4eba\u54e1\uff08\u8a08\uff09]\tAS Num\nFROM\tdbo.T_HeatStroke\tAS H\nINNER\tJOIN\tdbo.T_MaxTemperature\tAS T\nON\tH.\u90fd\u9053\u5e9c\u770c\u30b3\u30fc\u30c9 = T.\u90fd\u9053\u5e9c\u770c\u30b3\u30fc\u30c9\nAND\tH.\u65e5\u4ed8 = T.\u5e74\u6708\u65e5\nINNER\tJOIN\tdbo.T_VaporPressure\tAS V\nON\tH.\u90fd\u9053\u5e9c\u770c\u30b3\u30fc\u30c9 = V.\u90fd\u9053\u5e9c\u770c\u30b3\u30fc\u30c9\nAND\tH.\u65e5\u4ed8 = V.\u5e74\u6708\u65e5\nINNER\tJOIN\tdbo.T_Population\tAS P\nON\tH.\u90fd\u9053\u5e9c\u770c\u30b3\u30fc\u30c9 = P.\u90fd\u9053\u5e9c\u770c\u30b3\u30fc\u30c9\nAND\tYEAR(H.\u65e5\u4ed8) = P.\u8abf\u67fb\u5e74\nORDER\tBY\tH.\u90fd\u9053\u5e9c\u770c\u30b3\u30fc\u30c9, H.\u65e5\u4ed8<\/pre>\n
R\u3067\u306e\u51e6\u7406<\/h2>\n
\u3000\u4e0b\u8a18\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u308b\uff0e<\/p>\n
> y = HeatStroke5$Num\n> x1 = HeatStroke5$Temp\n> x2 = HeatStroke5$Vapor\n> x5 = HeatStroke5$Month\n> p1 = HeatStroke5$Pop14\n> p2 = HeatStroke5$Pop14_65\n> p3 = HeatStroke5$Pop65<\/pre>\n
> z1 = glm(y ~ x1 + x2 + x5, offset = log(p1), family = poisson(link = \"log\"))\n> z2 = glm(y ~ x1 + x2 + x5, offset = log(p2), family = poisson(link = \"log\"))\n> z3 = glm(y ~ x1 + x2 + x5, offset = log(p3), family = poisson(link = \"log\"))\n> z4 = glm(y ~ x1 + x2 + x5, offset = log(p1 + p2 + p3), family = poisson(link = \"log\"))\n> z5 = glm(y ~ x1 + x2 +x5, offset = log(p1 + p3), family = poisson(link = \"log\"))<\/pre>\n
\u3000\u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u901a\u308a\uff0e5\u901a\u308a\u306e\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3057\u305f\u304c\uff0cAIC\u304c\u6700\u5c0f\u3068\u306a\u3063\u305f\u306e\u306f65\u6b73\u4ee5\u4e0a\u4eba\u53e3\u306e\u307f\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u6295\u5165\u3057\u305f\u30e2\u30c7\u30ebz3\u3067\u3042\u3063\u305f\uff0e<\/p>\n
> summary(z1)\nCall:\nglm(formula = y ~ x1 + x2 + x5, family = poisson(link = \"log\"), \n    offset = log(p1))\nDeviance Residuals: \n     Min        1Q    Median        3Q       Max  \n-11.4213   -1.2344   -0.4067    0.9100   21.9089  \nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)    \n(Intercept) -1.933e+01  1.701e-02 -1136.4   <2e-16 ***\nx1           2.843e-01  5.079e-04   559.8   <2e-16 ***\nx2           7.917e-02  3.964e-04   199.7   <2e-16 ***\nx5          -2.999e-01  1.654e-03  -181.3   <2e-16 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n(Dispersion parameter for poisson family taken to be 1)\n    Null deviance: 982005  on 70107  degrees of freedom\nResidual deviance: 276188  on 70104  degrees of freedom\nAIC: 459136\nNumber of Fisher Scoring iterations: 5<\/pre>\n
> summary(z2)\nCall:\nglm(formula = y ~ x1 + x2 + x5, family = poisson(link = \"log\"), \n    offset = log(p2))\nDeviance Residuals: \n     Min        1Q    Median        3Q       Max  \n-12.8365   -1.1833   -0.3580    0.9729   21.3463  \nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)    \n(Intercept) -2.091e+01  1.699e-02 -1230.8   <2e-16 ***\nx1           2.849e-01  5.096e-04   559.1   <2e-16 ***\nx2           8.062e-02  4.008e-04   201.1   <2e-16 ***\nx5          -3.062e-01  1.665e-03  -183.9   <2e-16 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n(Dispersion parameter for poisson family taken to be 1)\n    Null deviance: 997200  on 70107  degrees of freedom\nResidual deviance: 278900  on 70104  degrees of freedom\nAIC: 461848\nNumber of Fisher Scoring iterations: 5<\/pre>\n
> summary(z3)\nCall:\nglm(formula = y ~ x1 + x2 + x5, family = poisson(link = \"log\"), \n    offset = log(p3))\nDeviance Residuals: \n     Min        1Q    Median        3Q       Max  \n-10.3979   -1.2493   -0.4561    0.7605   19.5694  \nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)    \n(Intercept) -2.007e+01  1.679e-02 -1195.6   <2e-16 ***\nx1           2.821e-01  5.052e-04   558.4   <2e-16 ***\nx2           8.262e-02  3.984e-04   207.4   <2e-16 ***\nx5          -2.948e-01  1.667e-03  -176.8   <2e-16 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n(Dispersion parameter for poisson family taken to be 1)\n    Null deviance: 971373  on 70107  degrees of freedom\nResidual deviance: 235828  on 70104  degrees of freedom\nAIC: 418776\nNumber of Fisher Scoring iterations: 5<\/pre>\n
> summary(z4)\nCall:\nglm(formula = y ~ x1 + x2 + x5, family = poisson(link = \"log\"), \n    offset = log(p1 + p2 + p3))\nDeviance Residuals: \n     Min        1Q    Median        3Q       Max  \n-12.0744   -1.2083   -0.3938    0.9011   20.9182  \nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)    \n(Intercept) -2.140e+01  1.694e-02 -1263.6   <2e-16 ***\nx1           2.841e-01  5.083e-04   559.0   <2e-16 ***\nx2           8.096e-02  3.996e-04   202.6   <2e-16 ***\nx5          -3.024e-01  1.664e-03  -181.7   <2e-16 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n(Dispersion parameter for poisson family taken to be 1)\n    Null deviance: 984470  on 70107  degrees of freedom\nResidual deviance: 263358  on 70104  degrees of freedom\nAIC: 446306\nNumber of Fisher Scoring iterations: 5<\/pre>\n
> summary(z5)\n\nCall:\nglm(formula = y ~ x1 + x2 + x5, family = poisson(link = \"log\"), \n    offset = log(p1 + p3))\n\nDeviance Residuals: \n     Min        1Q    Median        3Q       Max  \n-10.7514   -1.2475   -0.4445    0.7999   20.2746  \n\nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)    \n(Intercept) -2.046e+01  1.686e-02 -1213.7   <2e-16 ***\nx1           2.829e-01  5.061e-04   558.9   <2e-16 ***\nx2           8.147e-02  3.977e-04   204.9   <2e-16 ***\nx5          -2.964e-01  1.663e-03  -178.3   <2e-16 ***\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\n(Dispersion parameter for poisson family taken to be 1)\n\n    Null deviance: 970727  on 70107  degrees of freedom\nResidual deviance: 245088  on 70104  degrees of freedom\nAIC: 428036\n\nNumber of Fisher Scoring iterations: 5<\/pre>\n
\u307e\u3068\u3081<\/h2>\n
\u3000\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u306665\u6b73\u4ee5\u4e0a\u4eba\u53e3\u306e\u5bfe\u6570\u3092\u6295\u5165\u3059\u308b\u3068\uff0c\u7dcf\u4eba\u53e3\u3092\u6295\u5165\u3059\u308b\u3088\u308a\u3082\u4e88\u6e2c\u6027\u80fd\u304c\u6539\u5584\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\uff0e<\/p>\n","protected":false},"excerpt":{"rendered":"
\u3000\u4ee5\u524d\u306e\u8a18\u4e8b\u3067\u306f\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u306e\u5bfe\u6570\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u4e00\u822c\u5316\u7dda\u5f62\u56de\u5e30\u5206\u6790\u3092\u884c\u3063\u305f\uff0e\u5b9f\u969b\u306e\u3068\u3053\u308d\uff0c\u5e74\u4ee3\u5225\u306e\u642c\u9001\u4eba\u54e1\u3068\u3057\u3066\u306f65\u6b73\u4ee5\u4e0a\u306e\u9ad8\u9f62\u8005\u304c\u5727\u5012\u7684\u306b\u591a\u3044\uff0e\u305d\u306e\u305f\u3081\uff0c\u6771\u4eac\u306a\u3069\u52b4\u50cd\u4eba\u53e3\u306e\u591a\u3044\u3068\u3053\u308d\u3067\u306f\u4e88\u6e2c\u6027\u80fd\u304c\u60aa\u5316\u3059\u308b\u53ef\u80fd\u6027 … <\/p>\n
“\u71b1\u4e2d\u75c7\u642c\u9001\u4eba\u54e1\u6570\u306e\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u309265\u6b73\u4ee5\u4e0a\u4eba\u53e3\u306b\u5909\u66f4\u3057\u3066\u307f\u308b” \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[12917,12916,13404,12918,13317,4521,11356,13333,4673,13308,5411,13312,5914,3962,8149,5465,13309,4067,677,13316,4672,2740,13322,7154,8148,2871,13359,2783,2684,13363,5878,13403,13300,2893,5141,4460,13355,5162,1206],"class_list":["post-17378","post","type-post","status-publish","format-standard","hentry","category-technology","tag-12917","tag-12916","tag-13404","tag-12918","tag-aic","tag-e-stat","tag-family","tag-formula","tag-from","tag-glm","tag-inner","tag-intercept","tag-join","tag-log","tag-month","tag-offset","tag-poisson","tag-power-query","tag-r","tag-residual-deviance","tag-select","tag-sql-server","tag-summary","tag-use","tag-year","tag-2871","tag-13359","tag-2783","tag-2684","tag-13363","tag-5878","tag-13403","tag-13300","tag-2893","tag-5141","tag-4460","tag-13355","tag-5162","tag-1206"],"_links":{"self":[{"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=\/wp\/v2\/posts\/17378","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=17378"}],"version-history":[{"count":8,"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=\/wp\/v2\/posts\/17378\/revisions"}],"predecessor-version":[{"id":17387,"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=\/wp\/v2\/posts\/17378\/revisions\/17387"}],"wp:attachment":[{"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17378"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17378"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17378"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}