{"id":17268,"date":"2023-06-01T06:00:00","date_gmt":"2023-05-31T21:00:00","guid":{"rendered":"https:\/\/www.muscle-hypertrophy.com\/?p=17268"},"modified":"2023-03-03T15:55:50","modified_gmt":"2023-03-03T06:55:50","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%ab%e9%83%bd%e9%81%93%e5%ba%9c%e7%9c%8c%e4%ba%ba%e5%8f%a3%e3%82%92%e3%82%aa%e3%83%95%e3%82%bb%e3%83%83%e3%83%88%e9%a0%85","status":"publish","type":"post","link":"https:\/\/www.muscle-hypertrophy.com\/?p=17268","title":{"rendered":"\u71b1\u4e2d\u75c7\u642c\u9001\u4eba\u54e1\u6570\u306b\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u8ffd\u52a0\u3057\u4e00\u822c\u5316\u7dda\u5f62\u56de\u5e30\u5206\u6790\u3092\u884c\u3046"},"content":{"rendered":"\n

\u3000\u4ee5\u524d\u306e\u8a18\u4e8b\u3067\u306f\u30dd\u30a2\u30bd\u30f3\u56de\u5e30\u30e2\u30c7\u30eb\u304a\u3088\u3073\u8ca0\u306e\u4e8c\u9805\u5206\u5e03\u30e2\u30c7\u30eb\u3092\u7528\u3044\u3066\u71b1\u4e2d\u75c7\u642c\u9001\u4eba\u54e1\u6570\u306b\u5bfe\u3059\u308b\u65e5\u6700\u9ad8\u6c17\u6e29\u3068\u5e73\u5747\u6c34\u84b8\u6c17\u5727\u306e\u56de\u5e30\u4fc2\u6570\u3092\u63a8\u5b9a<\/a>\u3057\u305f\uff0e<\/p>\n

\u3000\u4eba\u53e310\u4e07\u4eba\u3042\u305f\u308a\u4f55\u540d\u306e\u7f79\u60a3\u8005\u6570\uff0c\u3068\u3044\u3046\u306e\u306f\u5272\u308a\u7b97\u5024\u3067\u3042\u308b\uff0e\u7dcf\u52d9\u7701\u6d88\u9632\u5e81\u306e\u516c\u958b\u3057\u3066\u3044\u308b\u71b1\u4e2d\u75c7\u642c\u9001\u4eba\u54e1\u6570<\/a>\u306f\u90fd\u9053\u5e9c\u770c\u3054\u3068\u306e\u642c\u9001\u6570\u3067\u3042\u308a\uff0c\u3082\u3068\u3082\u3068\u90fd\u9053\u5e9c\u770c\u5225\u4eba\u53e3\u304c\u7570\u306a\u308b\u306e\u3060\u304b\u3089\u642c\u9001\u4eba\u54e1\u6570\u3092\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u3067\u5272\u3063\u305f\u5272\u5408\u306e\u307b\u3046\u304c\u6307\u6a19\u3068\u3057\u3066\u9069\u5207\u306a\u306e\u3067\u306f\u306a\u3044\u304b\uff0c\u3068\u3044\u3046\u6307\u6458\u306f\u4e00\u7406\u3042\u308b\uff0e<\/p>\n

\u3000\u3057\u304b\u3057\uff0c\u5272\u308a\u7b97\u5024\u3067\u306f\u306a\u304f\u5b9f\u6570\u3092\u89e3\u6790\u3059\u3079\u304d\u3067\u3042\u308b\uff0e\u5909\u5f62\u3057\u305f\u89b3\u6e2c\u5024\u3092\u7d71\u8a08\u30e2\u30c7\u30eb\u306e\u5fdc\u7b54\u5909\u6570\u306b\u3059\u308b\u306e\u306f\u4e0d\u5fc5\u8981\u3067\u3042\u308b\u3070\u304b\u308a\u304b\uff0c\u8aa4\u3063\u305f\u7d50\u679c\u3092\u5c0e\u304d\u304b\u306d\u306a\u3044\u304b\u3089\u3067\u3042\u308b\uff0e\u5272\u308a\u7b97\u5024\u304b\u3089\u306f\u78ba\u304b\u3089\u3057\u3055\u306e\u60c5\u5831\u304c\u5931\u308f\u308c\u308b\u3053\u3068\uff0c\u5909\u63db\u3055\u308c\u305f\u5024\u306e\u5206\u5e03\u304c\u4e0d\u660e\u3067\u3042\u308b\u3053\u3068\u304b\u3089\uff0c\u5272\u308a\u7b97\u5024\u306f\u907f\u3051\u308b\u3079\u304d\u3067\u3042\u308b\uff0e\u305d\u306e\u4ee3\u308f\u308a\u306b\u5272\u308a\u7b97\u306e\u5206\u6bcd\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u7dda\u5f62\u4e88\u6e2c\u5b50\u306b\u7d44\u307f\u8fbc\u3080\u624b\u6cd5\u304c\u3042\u308b\uff0e<\/p>\n

\u3000\u71b1\u4e2d\u75c7\u642c\u9001\u4eba\u54e1\u6570\u306f\u30ab\u30a6\u30f3\u30c8\u30c7\u30fc\u30bf\u3067\u3042\u308a\uff0c\u305d\u306e\u671f\u5f85\u5024\u306f\u96c6\u8a08\u30be\u30fc\u30f3\u306e\u96c6\u8a08\u5bfe\u8c61\u4eba\u53e3\u306b\u4f9d\u5b58\u3059\u308b\uff0e\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3059\u308b\u3053\u3068\u3067\uff0c\u90fd\u9053\u5e9c\u770c\u306e\u4eba\u53e3\u898f\u6a21\u306e\u5f71\u97ff\u3092\u8abf\u6574\u3057\u305f\u56de\u5e30\u5206\u6790\u304c\u3067\u304d\u308b\uff0e\u4eca\u56de\u306f\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u7dda\u5f62\u4e88\u6e2c\u5b50\u306b\u7d44\u307f\u8fbc\u307f\uff0c\u4e00\u822c\u5316\u7dda\u5f62\u56de\u5e30\u5206\u6790\u3092\u884c\u3063\u3066\u307f\u305f\uff0e<\/p>\n

<\/p>\n

e-Stat\u304b\u3089\u90fd\u9053\u5e9c\u770c\u5225\u4eba\u53e3\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9<\/h2>\n

\u3000e-Stat\u30c8\u30c3\u30d7\u30da\u30fc\u30b8\u304b\u3089\u300c\u5730\u57df\u300d\u3092\u9078\u3076\uff0e<\/p>\n

\"e-Stat\u30c8\u30c3\u30d7\u30da\u30fc\u30b8\u304b\u3089\u300c\u5730\u57df\u300d\u3092\u9078\u3076\"<\/a>
e-Stat\u30c8\u30c3\u30d7\u30da\u30fc\u30b8\u304b\u3089\u300c\u5730\u57df\u300d\u3092\u9078\u3076<\/figcaption><\/figure>\n

\u3000\u300c\u90fd\u9053\u5e9c\u770c\u30c7\u30fc\u30bf\u300d\u306e\u300c\u30c7\u30fc\u30bf\u8868\u793a\u300d\u3092\u9078\u3076\uff0e<\/p>\n

\"\u300c\u90fd\u9053\u5e9c\u770c\u30c7\u30fc\u30bf\u300d\u306e\u300c\u30c7\u30fc\u30bf\u8868\u793a\u300d\u3092\u9078\u3076\"<\/a>
\u300c\u90fd\u9053\u5e9c\u770c\u30c7\u30fc\u30bf\u300d\u306e\u300c\u30c7\u30fc\u30bf\u8868\u793a\u300d\u3092\u9078\u3076<\/figcaption><\/figure>\n

\u3000\u300c\u5730\u57df\u5019\u88dc\u300d\u3067\u300c\u5168\u3066\u9078\u629e\u300d\u3057\u300c\u5168\u56fd\u300d\u3092\u623b\u3059\uff0e\u78ba\u5b9a\u3057\u3066\u6b21\u753b\u9762\u306b\u9032\u3080\uff0e<\/p>\n

\"\u300c\u5730\u57df\u5019\u88dc\u300d\u3067\u300c\u5168\u3066\u9078\u629e\u300d\u3057\u300c\u5168\u56fd\u300d\u3092\u623b\u3059\"<\/a>
\u300c\u5730\u57df\u5019\u88dc\u300d\u3067\u300c\u5168\u3066\u9078\u629e\u300d\u3057\u300c\u5168\u56fd\u300d\u3092\u623b\u3059<\/figcaption><\/figure>\n

\u3000\u300c\u8868\u793a\u9805\u76ee\u9078\u629e\u300d\u3067\u5206\u91ce\u3092\u300cA\u3000\u4eba\u53e3\u30fb\u4e16\u5e2f\u300d\u3068\u3057\uff0c\u5927\u5206\u985e\u3092\u300cA1\u3000\u4eba\u53e3\u306e\u6982\u8981\u30fb\u69cb\u9020\u300d\u3068\u3057\uff0c\u5c0f\u5206\u985e\u3092\u300cA11\u3000\u4eba\u53e3\u300d\u3068\u3057\uff0c\u300c\u9805\u76ee\u9078\u629e\u300d\u3067\u300cA1101\u3000\u7dcf\u4eba\u53e3\uff08\u4eba\uff09\u300d\u3092\u9078\u3076\uff0e<\/p>\n

\"\u300c\u8868\u793a\u9805\u76ee\u9078\u629e\u300d\u3067\u300c\u7dcf\u4eba\u53e3\uff08\u4eba\uff09\u300d\u3092\u9078\u3076\"<\/a>
\u300c\u8868\u793a\u9805\u76ee\u9078\u629e\u300d\u3067\u300c\u7dcf\u4eba\u53e3\uff08\u4eba\uff09\u300d\u3092\u9078\u3076<\/figcaption><\/figure>\n

\u3000\u300c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u300d\u3092\u30af\u30ea\u30c3\u30af\u3059\u308b\uff0e<\/p>\n

\"\u300c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u300d\u3092\u30af\u30ea\u30c3\u30af\u3059\u308b\"<\/a>
\u300c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u300d\u3092\u30af\u30ea\u30c3\u30af\u3059\u308b<\/figcaption><\/figure>\n

\u3000\u300c\u8868\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u300d\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u8a2d\u5b9a\u3067\u306f\u300c\u30d8\u30c3\u30c0\u306e\u51fa\u529b\u300d\u3092\u300c\u51fa\u529b\u3059\u308b\u300d\u306e\u307e\u307e\u3068\u3057\u3066\u304a\u304f\uff0e\u300c\u6ce8\u91c8\u3092\u8868\u793a\u3059\u308b\u300d\u306e\u30c1\u30a7\u30c3\u30af\u3092\u5916\u3057\uff0c\u300c\u6841\u533a\u5207\u308a\u3092\u4f7f\u7528\u3057\u306a\u3044\u300d\u306b\u30c1\u30a7\u30c3\u30af\u3059\u308b\uff0e\u4e0b\u56f3\u3067\u306f\u300c\u7279\u6b8a\u6587\u5b57\u306e\u9078\u629e\u300d\u3092\u300c0\uff08\u6570\u5b57\u306e\u30bc\u30ed\uff09\u306b\u7f6e\u304d\u63db\u3048\u308b\u300d\u3092\u9078\u3093\u3067\u3044\u308b\u304c\uff0cNULL\u306a\u3044\u3057\u306fNA\u3092\u9078\u3076\u3079\u304d\u3067\u3042\u308a\uff0c\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u5f8c\u306b\u8a72\u5f53\u884c\u3092\u524a\u9664\u3059\u308b\u306e\u304c\u672c\u6765\u3067\u3042\u308b\uff0e<\/p>\n

\"\u300c\u8868\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u300d\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u8a2d\u5b9a\"<\/a>
\u300c\u8868\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u300d\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u8a2d\u5b9a<\/figcaption><\/figure>\n

Power Query\u3067\u6574\u5f62<\/h2>\n

\u3000\u5fc5\u8981\u306a\u5217\u306f\u5e74\uff0c\u90fd\u9053\u5e9c\u770c\u30b3\u30fc\u30c9\uff0c\u90fd\u9053\u5e9c\u770c\uff0c\u4eba\u53e3\u3067\u3042\u308b\uff0e\u5217\u540d\u3092\u305d\u308c\u305e\u308cFiscalYear, PrefCode, Prefecture, Population\u3068\u3059\u308b\uff0e\u30c6\u30ad\u30b9\u30c8\u30d5\u30a1\u30a4\u30eb\u3067\u4fdd\u5b58\u3059\u308b\uff0e\u30d5\u30a1\u30a4\u30eb\u540d\u306fT_Population.txt\u3068\u3059\u308b\uff0e<\/p>\n

SQL Server\u306b\u30a4\u30f3\u30dd\u30fc\u30c8<\/h2>\n

\u3000\u300c\u30c7\u30fc\u30bf\u30bd\u30fc\u30b9\u300d\u3092\u300cFlat File Source\u300d\u3068\u3057\u300c\u53c2\u7167…\u300d\u304b\u3089\u30d5\u30a1\u30a4\u30eb\u3092\u6307\u5b9a\u3059\u308b\uff0e<\/p>\n

\"\u300c\u30c7\u30fc\u30bf\u30bd\u30fc\u30b9\u300d\u3092\u300cFlat<\/a>
\u300c\u30c7\u30fc\u30bf\u30bd\u30fc\u30b9\u300d\u3092\u300cFlat File Source\u300d\u3068\u3057\u300c\u53c2\u7167…\u300d\u304b\u3089\u30d5\u30a1\u30a4\u30eb\u3092\u6307\u5b9a<\/figcaption><\/figure>\n

\u3000\u300c\u5909\u63db\u5148\u306e\u9078\u629e\u300d\u3092\u300cSQL Server Native Client 11.0\u300d\u3068\u3059\u308b\uff0e<\/p>\n

\"\u300c\u5909\u63db\u5148\u306e\u9078\u629e\u300d\u3092\u300cSQL<\/a>
\u300c\u5909\u63db\u5148\u306e\u9078\u629e\u300d\u3092\u300cSQL Server Native Client 11.0\u300d\u3068\u3059\u308b<\/figcaption><\/figure>\n

\u3000\u300c\u5217\u30de\u30c3\u30d4\u30f3\u30b0\u300d\u3067\u30c7\u30fc\u30bf\u578b\u3068\u30b5\u30a4\u30ba\u3092\u6307\u5b9a\u3059\u308b\uff0e<\/p>\n

\"\u300c\u5217\u30de\u30c3\u30d4\u30f3\u30b0\u300d\u3067\u30c7\u30fc\u30bf\u578b\u3068\u30b5\u30a4\u30ba\u3092\u6307\u5b9a\"<\/a>
\u300c\u5217\u30de\u30c3\u30d4\u30f3\u30b0\u300d\u3067\u30c7\u30fc\u30bf\u578b\u3068\u30b5\u30a4\u30ba\u3092\u6307\u5b9a<\/figcaption><\/figure>\n

\u30002115\u884c\u304c\u30a4\u30f3\u30dd\u30fc\u30c8\u3055\u308c\u308b\uff0e<\/p>\n

\"2115\u884c\u304c\u30a4\u30f3\u30dd\u30fc\u30c8\u3055\u308c\u308b\"<\/a>
2115\u884c\u304c\u30a4\u30f3\u30dd\u30fc\u30c8\u3055\u308c\u308b<\/figcaption><\/figure>\n

\u30af\u30a8\u30ea\u3067\u30c7\u30fc\u30bf\u3092\u62bd\u51fa<\/h2>\n

\u3000\u4e0b\u8a18\u30af\u30a8\u30ea\u3092\u5b9f\u884c\u3057\u3066\u30c7\u30fc\u30bf\u3092\u62bd\u51fa\u3059\u308b\uff0e\u7d50\u679c\u3092Excel\u306b\u30b3\u30d4\u30da\u3057\u3066\u4fdd\u5b58\u3059\u308b\uff0e\u30d5\u30a1\u30a4\u30eb\u540d\u3092HeatStrokeDB.xlsx\u3068\u3059\u308b\uff0e<\/p>\n

USE HeatStrokeDB;\nGO\nSELECT\tP.PrefCode\n,\tP.Prefecture\n,\tP.FiscalYear\n,\tT.\u5e74\u6708\u65e5\tAS Date\n,\tT.\u65e5\u6700\u9ad8\u6c17\u6e29\tAS Temp\n,\tV.\u65e5\u5e73\u5747\u84b8\u6c17\u5727\tAS Vapor\n,\tP.Population\tAS Pop\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.PrefCode\nAND\tYEAR(H.\u65e5\u4ed8) = P.FiscalYear\nORDER\tBY\tP.PrefCode, T.\u5e74\u6708\u65e5<\/pre>\n
(70108 \u884c\u51e6\u7406\u3055\u308c\u307e\u3057\u305f)<\/pre>\n
\u5272\u308a\u7b97\u5024\u306e\u4ee3\u308f\u308a\u306b\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u306e\u5bfe\u6570\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u7dda\u5f62\u4e88\u6e2c\u5b50\u306b\u7d44\u307f\u8fbc\u3080<\/h2>\n
\u3000\u71b1\u4e2d\u75c7\u642c\u9001\u4eba\u54e1\u6570\u3092n, \u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u3092N\u3068\u3057\u3066\u30dd\u30a2\u30bd\u30f3\u5206\u5e03\u3092\u4eee\u5b9a\u3059\u308b\u3068\uff0c\u4e0b\u8a18\u306e\u5f0f\u304c\u6210\u308a\u7acb\u3064\uff0e<\/p>\n
\\frac{n}{N} = Exp(\\alpha + \\beta_1 \\times Temp + \\beta_2 \\times Vapor)<\/p>\n
\u3000\u5f0f\u3092\u5909\u5f62\u3059\u308b\uff0e<\/p>\n
n = N \\times Exp(\\alpha + \\beta_1 \\times Temp + \\beta_2 \\times Vapor)<\/p>\n
n = Exp(Log(N)) \\times Exp(\\alpha + \\beta_1 \\times Temp + \\beta_2 \\times Vapor)<\/p>\n
n = Exp(\\alpha + \\beta_1 \\times Temp + \\beta_2 \\times Vapor + Log(N))<\/p>\n
\u3000Log(N)\u304c\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3067\u3042\u308b\uff0e\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u306e\u5bfe\u6570\u3092\u7dda\u5f62\u4e88\u6e2c\u5b50\u306b\u7d44\u307f\u8fbc\u3093\u3060\u5f62\u3067\u3042\u308b\uff0e<\/p>\n
R\u3067\u4e00\u822c\u5316\u7dda\u5f62\u56de\u5e30\u5206\u6790\u3092\u884c\u3046<\/h2>\n
\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\uff0c\u5909\u6570\u3078\u306e\u4ee3\u5165<\/h3>\n
\u3000\u4e0b\u8a18\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3057\u3066HeatStrokeDB.xlsx\u3092\u8aad\u307f\u8fbc\u307f\uff0c\u5909\u6570y, x1, x2, p\u306b\u305d\u308c\u305e\u308c\u642c\u9001\u4eba\u54e1\u6570\uff0c\u65e5\u6700\u9ad8\u6c17\u6e29\uff0c\u5e73\u5747\u6c34\u84b8\u6c17\u5727\uff0c\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u3092\u4ee3\u5165\u3059\u308b\uff0e<\/p>\n
> library(readxl)\n> HeatStrokeDB <- read_excel(\"C:\/Users\/***\/HeatStrokeDB.xlsx\", \n+     col_types = c(\"numeric\", \"text\", \"numeric\", \n+         \"date\", \"numeric\", \"numeric\", \"numeric\", \n+         \"numeric\"))\n> View(HeatStrokeDB)\n> y = HeatStrokeDB$Num\n> x1 = HeatStrokeDB$Temp\n> x2 = HeatStrokeDB$Vapor\n> p = HeatStrokeDB$Pop<\/pre>\n
\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u306a\u3057\u3067\u30dd\u30a2\u30bd\u30f3\u5206\u5e03\u3092\u4eee\u5b9a\u3059\u308b\u5834\u5408<\/h3>\n
\u3000\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u304c\u5909\u308f\u3063\u305f\u305f\u3081\uff0c\u6bd4\u8f03\u3092\u76ee\u7684\u306b\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u3068\u3057\u3066\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u306a\u3057\u306e\u4e00\u822c\u5316\u7dda\u5f62\u56de\u5e30\u5206\u6790\u3092\u884c\u3046\uff0eAIC\u306f750211\u3067\u3042\u308b\uff0e<\/p>\n
> w = glm(y ~ x1 + x2, family = poisson(link = \"log\"), data = HeatStrokeDB)\n> summary(w)<\/pre>\n
Call:\nglm(formula = y ~ x1 + x2, family = poisson(link = \"log\"), data = HeatStrokeDB)\nDeviance Residuals: \n    Min       1Q   Median       3Q      Max  \n-10.804   -1.965   -0.891    0.596   33.428  \nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)    \n(Intercept) -8.1341526  0.0158004  -514.8   <2e-16 ***\nx1           0.2707915  0.0004953   546.8   <2e-16 ***\nx2           0.0685135  0.0003906   175.4   <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: 1243632  on 70107  degrees of freedom\nResidual deviance:  567265  on 70105  degrees of freedom\nAIC: 750211\nNumber of Fisher Scoring iterations: 5<\/pre>\n
\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3042\u308a\u3067\u30dd\u30a2\u30bd\u30f3\u5206\u5e03\u3092\u4eee\u5b9a\u3059\u308b\u5834\u5408<\/h3>\n
\u3000\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u306e\u5bfe\u6570\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u8ffd\u52a0\u3057\uff0c\u4e00\u822c\u5316\u7dda\u5f62\u56de\u5e30\u5206\u6790\u3092\u884c\u3046\uff0eAIC\u306f480515\u306b\u6539\u5584\u3057\u3066\u3044\u308b\uff0e<\/p>\n
> z = glm(y ~ x1 + x2, offset = log(p), family = poisson(link = \"log\"), data = HeatStrokeDB)\n> summary(z)<\/pre>\n
Call:\nglm(formula = y ~ x1 + x2, family = poisson(link = \"log\"), data = HeatStrokeDB, \n    offset = log(p))\nDeviance Residuals: \n     Min        1Q    Median        3Q       Max  \n-13.0022   -1.2911   -0.4225    0.9607   19.7591  \nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)    \n(Intercept) -2.293e+01  1.570e-02 -1460.9   <2e-16 ***\nx1           2.730e-01  5.034e-04   542.3   <2e-16 ***\nx2           6.671e-02  3.987e-04   167.3   <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: 984714  on 70107  degrees of freedom\nResidual deviance: 297569  on 70105  degrees of freedom\nAIC: 480515\nNumber of Fisher Scoring iterations: 5<\/pre>\n
\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u306a\u3057\u3067\u8ca0\u306e\u4e8c\u9805\u5206\u5e03\u3092\u4eee\u5b9a\u3059\u308b\u5834\u5408<\/h3>\n
\u3000\u3055\u3089\u306b\u6b32\u5f35\u3063\u3066\u8ca0\u306e\u4e8c\u9805\u5206\u5e03\u3092\u4eee\u5b9a\u3057\u3066\u307f\u308b\uff0eAIC\u306f360530\u3068\u30dd\u30a2\u30bd\u30f3\u5206\u5e03\u3088\u308a\u3082\u4e88\u6e2c\u6027\u80fd\u304c\u9ad8\u3044\uff0e<\/p>\n
> y = HeatStrokeDB$Num\n> x1 = HeatStrokeDB$Temp\n> x2 = HeatStrokeDB$Vapor\n> p = HeatStrokeDB$Pop\n> z = glm.nb(y ~ x1 + x2, data = HeatStrokeDB)\n> summary(z)<\/pre>\n
Call:\nglm.nb(formula = y ~ x1 + x2, data = HeatStrokeDB, init.theta = 1.057957391, \n    link = log)\nDeviance Residuals: \n    Min       1Q   Median       3Q      Max  \n-2.6241  -1.0533  -0.4774   0.2303   5.9534  \nCoefficients:\n             Estimate Std. Error z value Pr(>|z|)    \n(Intercept) -7.668407   0.036287 -211.33   <2e-16 ***\nx1           0.282649   0.001444  195.76   <2e-16 ***\nx2           0.035372   0.001020   34.69   <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 Negative Binomial(1.058) family taken to be 1)\n    Null deviance: 161258  on 70107  degrees of freedom\nResidual deviance:  75571  on 70105  degrees of freedom\nAIC: 360530\nNumber of Fisher Scoring iterations: 1\n              Theta:  1.05796 \n          Std. Err.:  0.00760 \n 2 x log-likelihood:  -360521.83600<\/pre>\n
\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3042\u308a\u3067\u8ca0\u306e\u4e8c\u9805\u5206\u5e03\u3092\u4eee\u5b9a\u3059\u308b\u5834\u5408<\/h3>\n
\u3000\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u306e\u5bfe\u6570\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u7dda\u5f62\u4e88\u6e2c\u5b50\u306b\u7d44\u307f\u8fbc\u3093\u3060\u5834\u5408\u306f\u3069\u3046\u3060\u308d\u3046\u304b\uff0eAIC\u306f5265246\u3068\u5927\u5e45\u306b\u60aa\u5316\u3057\u305f\uff0e<\/p>\n
> w = glm.nb(y ~ x1 + x2, offset(log(p)), data = HeatStrokeDB)\n> summary(w)<\/pre>\n
Call:\nglm.nb(formula = y ~ x1 + x2, data = HeatStrokeDB, weights = offset(log(p)), \n    init.theta = 1.052690849, link = log)\n\nDeviance Residuals: \n    Min       1Q   Median       3Q      Max  \n-9.8194  -4.0229  -1.8965   0.7493  22.9171  \n\nCoefficients:\n              Estimate Std. Error z value Pr(>|z|)    \n(Intercept) -7.6644546  0.0095107  -805.9   <2e-16 ***\nx1           0.2836617  0.0003792   748.0   <2e-16 ***\nx2           0.0354114  0.0002681   132.1   <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 Negative Binomial(1.0527) family taken to be 1)\n\n    Null deviance: 2344047  on 70107  degrees of freedom\nResidual deviance: 1093700  on 70105  degrees of freedom\nAIC: 5265246\n\nNumber of Fisher Scoring iterations: 1\n\n\n              Theta:  1.05269 \n          Std. Err.:  0.00197 \n\n 2 x log-likelihood:  -5265238.35100<\/pre>\n
\u7d50\u679c<\/h2>\n
\u3000\u30dd\u30a2\u30bd\u30f3\u5206\u5e03\u3092\u4eee\u5b9a\u3057\u305f\u5834\u5408\uff0c\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u306e\u5bfe\u6570\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u8ffd\u52a0\u3057\u305f\u307b\u3046\u304c\u4e88\u6e2c\u6027\u80fd\u304c\u6539\u5584\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\uff0e\u8ca0\u306e\u4e8c\u9805\u5206\u5e03\u3092\u4eee\u5b9a\u3059\u308b\u3068\u3055\u3089\u306b\u4e88\u6e2c\u6027\u80fd\u304c\u6539\u5584\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u304c\uff0c\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u8ffd\u52a0\u3059\u308b\u3068\u9006\u306b\u4e88\u6e2c\u6027\u80fd\u304c\u4f4e\u4e0b\u3057\u305f\uff0e<\/p>\n<\/a>\"\"\n","protected":false},"excerpt":{"rendered":"
\u3000\u4ee5\u524d\u306e\u8a18\u4e8b\u3067\u306f\u30dd\u30a2\u30bd\u30f3\u56de\u5e30\u30e2\u30c7\u30eb\u304a\u3088\u3073\u8ca0\u306e\u4e8c\u9805\u5206\u5e03\u30e2\u30c7\u30eb\u3092\u7528\u3044\u3066\u71b1\u4e2d\u75c7\u642c\u9001\u4eba\u54e1\u6570\u306b\u5bfe\u3059\u308b\u65e5\u6700\u9ad8\u6c17\u6e29\u3068\u5e73\u5747\u6c34\u84b8\u6c17\u5727\u306e\u56de\u5e30\u4fc2\u6570\u3092\u63a8\u5b9a\u3057\u305f\uff0e \u3000\u4eba\u53e310\u4e07\u4eba\u3042\u305f\u308a\u4f55\u540d\u306e\u7f79\u60a3\u8005\u6570\uff0c\u3068\u3044\u3046\u306e\u306f\u5272\u308a\u7b97\u5024\u3067\u3042\u308b\uff0e\u7dcf\u52d9\u7701\u6d88\u9632\u5e81\u306e\u516c\u958b\u3057 … <\/p>\n
“\u71b1\u4e2d\u75c7\u642c\u9001\u4eba\u54e1\u6570\u306b\u90fd\u9053\u5e9c\u770c\u4eba\u53e3\u3092\u30aa\u30d5\u30bb\u30c3\u30c8\u9805\u3068\u3057\u3066\u8ffd\u52a0\u3057\u4e00\u822c\u5316\u7dda\u5f62\u56de\u5e30\u5206\u6790\u3092\u884c\u3046” \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":[13317,3559,6391,13311,13318,4521,13313,11356,2878,13333,4673,13308,5411,13312,5914,13310,3962,5465,7096,4644,13309,4067,677,13316,4672,2740,4165,13314,7154,13315,2861,13359,2783,4848,2684,646,1026,2499,5134,5249,11311,13356,13363,4282,13358,2759,5178,5136,5370,11240,1027,5852,11919,5153,11181,13325,13361,821,13360,13357,5148,5139,13362,3680,5133,13355],"class_list":["post-17268","post","type-post","status-publish","format-standard","hentry","category-technology","tag-aic","tag-and","tag-by","tag-coefficients","tag-degrees-of-freedom","tag-e-stat","tag-estimate","tag-family","tag-flat-file-source","tag-formula","tag-from","tag-glm","tag-inner","tag-intercept","tag-join","tag-link","tag-log","tag-offset","tag-on","tag-order","tag-poisson","tag-power-query","tag-r","tag-residual-deviance","tag-select","tag-sql-server","tag-sql-server-native-client-11-0","tag-std-error","tag-use","tag-z-value","tag-2861","tag-13359","tag-2783","tag-4848","tag-2684","tag-646","tag-1026","tag-2499","tag-5134","tag-5249","tag-11311","tag-13356","tag-13363","tag-4282","tag-13358","tag-2759","tag-5178","tag-5136","tag-5370","tag-11240","tag-1027","tag-5852","tag-11919","tag-5153","tag-11181","tag-13325","tag-13361","tag-821","tag-13360","tag-13357","tag-5148","tag-5139","tag-13362","tag-3680","tag-5133","tag-13355"],"_links":{"self":[{"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=\/wp\/v2\/posts\/17268","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=17268"}],"version-history":[{"count":32,"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=\/wp\/v2\/posts\/17268\/revisions"}],"predecessor-version":[{"id":17554,"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=\/wp\/v2\/posts\/17268\/revisions\/17554"}],"wp:attachment":[{"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17268"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17268"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.muscle-hypertrophy.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17268"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}