{"id":3229,"date":"2023-07-18T13:59:04","date_gmt":"2023-07-18T13:59:04","guid":{"rendered":"https:\/\/statorials.org\/pt\/teste-de-normalidade-python\/"},"modified":"2023-07-18T13:59:04","modified_gmt":"2023-07-18T13:59:04","slug":"teste-de-normalidade-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/pt\/teste-de-normalidade-python\/","title":{"rendered":"Como testar a normalidade em python (4 m\u00e9todos)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Muitos testes estat\u00edsticos <a href=\"https:\/\/statorials.org\/pt\/hipotese-de-normalidade\/\" target=\"_blank\" rel=\"noopener\">assumem<\/a> que os conjuntos de dados s\u00e3o normalmente distribu\u00eddos.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Existem quatro maneiras comuns de verificar essa hip\u00f3tese em Python:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. (M\u00e9todo visual) Crie um histograma.<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Se o histograma tiver aproximadamente o formato de um \u201csino\u201d, ent\u00e3o os dados ser\u00e3o considerados normalmente distribu\u00eddos.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>2. (M\u00e9todo visual) Crie um gr\u00e1fico QQ.<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Se os pontos no gr\u00e1fico estiverem aproximadamente ao longo de uma linha reta diagonal, ent\u00e3o os dados s\u00e3o considerados normalmente distribu\u00eddos.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>3. (Teste estat\u00edstico formal) Realize um teste de Shapiro-Wilk.<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Se o valor p do teste for maior que \u03b1 = 0,05, ent\u00e3o os dados s\u00e3o considerados normalmente distribu\u00eddos.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>4. (Teste estat\u00edstico formal) Realize um teste de Kolmogorov-Smirnov.<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Se o valor p do teste for maior que \u03b1 = 0,05, ent\u00e3o os dados s\u00e3o considerados normalmente distribu\u00eddos.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Os exemplos a seguir mostram como usar cada um desses m\u00e9todos na pr\u00e1tica.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">M\u00e9todo 1: crie um histograma<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">O c\u00f3digo a seguir mostra como criar um histograma para um conjunto de dados que segue uma <a href=\"https:\/\/statorials.org\/pt\/distribuicao-normal-do-log-python\/\" target=\"_blank\" rel=\"noopener\">distribui\u00e7\u00e3o log-normal<\/a> :<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> math\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> lognorm\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#generate dataset that contains 1000 log-normal distributed values\n<\/span>lognorm_dataset = lognorm. <span style=\"color: #3366ff;\">rvs<\/span> (s=.5, scale= <span style=\"color: #3366ff;\">math.exp<\/span> (1), size=1000)\n\n<span style=\"color: #008080;\">#create histogram to visualize values in dataset\n<\/span>plt. <span style=\"color: #3366ff;\">hist<\/span> (lognorm_dataset, edgecolor=' <span style=\"color: #ff0000;\">black<\/span> ', bins=20)<\/span><\/span><\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-27387 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/normalitepython1.jpg\" alt=\"\" width=\"559\" height=\"364\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">Apenas olhando para este histograma, podemos dizer que o conjunto de dados n\u00e3o apresenta um \u201cformato de sino\u201d e n\u00e3o \u00e9 normalmente distribu\u00eddo.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">M\u00e9todo 2: criar um gr\u00e1fico QQ<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">O c\u00f3digo a seguir mostra como criar um gr\u00e1fico QQ para um conjunto de dados que segue uma distribui\u00e7\u00e3o log-normal:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> math\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> lognorm\n<span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#generate dataset that contains 1000 log-normal distributed values\n<\/span>lognorm_dataset = lognorm. <span style=\"color: #3366ff;\">rvs<\/span> (s=.5, scale= <span style=\"color: #3366ff;\">math.exp<\/span> (1), size=1000)\n\n<span style=\"color: #008080;\">#create QQ plot with 45-degree line added to plot\n<\/span>fig = sm. <span style=\"color: #3366ff;\">qqplot<\/span> (lognorm_dataset, line=' <span style=\"color: #ff0000;\">45<\/span> ')\n\nplt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-27390 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/normalitepython2.jpg\" alt=\"\" width=\"533\" height=\"359\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">Se os pontos do gr\u00e1fico estiverem aproximadamente ao longo de uma linha reta diagonal, geralmente assumimos que um conjunto de dados \u00e9 normalmente distribu\u00eddo.<\/span><\/p>\n<p> <span style=\"color: #000000;\">No entanto, os pontos neste gr\u00e1fico claramente n\u00e3o correspondem \u00e0 linha vermelha, pelo que n\u00e3o podemos assumir que este conjunto de dados seja normalmente distribu\u00eddo.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Isso deve fazer sentido, visto que geramos os dados usando uma fun\u00e7\u00e3o de distribui\u00e7\u00e3o log-normal.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">M\u00e9todo 3: realizar um teste de Shapiro-Wilk<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">O c\u00f3digo a seguir mostra como executar um Shapiro-Wilk para um conjunto de dados que segue uma distribui\u00e7\u00e3o log-normal:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> math\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> scipy.stats <span style=\"color: #008000;\">import<\/span> shapiro \n<span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> lognorm\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#generate dataset that contains 1000 log-normal distributed values\n<\/span>lognorm_dataset = lognorm. <span style=\"color: #3366ff;\">rvs<\/span> (s=.5, scale= <span style=\"color: #3366ff;\">math.exp<\/span> (1), size=1000)\n\n<span style=\"color: #008080;\">#perform Shapiro-Wilk test for normality\n<\/span>shapiro(lognorm_dataset)\n\nShapiroResult(statistic=0.8573324680328369, pvalue=3.880663073872444e-29)\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">A partir do resultado, podemos ver que a estat\u00edstica de teste \u00e9 <strong>0,857<\/strong> e o valor p correspondente \u00e9 <strong>3,88e-29<\/strong> (extremamente pr\u00f3ximo de zero).<\/span><\/p>\n<p> <span style=\"color: #000000;\">Como o valor p \u00e9 inferior a 0,05, rejeitamos a hip\u00f3tese nula do teste de Shapiro-Wilk.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Isto significa que temos evid\u00eancias suficientes para dizer que os dados da amostra n\u00e3o prov\u00eam de uma distribui\u00e7\u00e3o normal.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">M\u00e9todo 4: realizar um teste de Kolmogorov-Smirnov<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">O c\u00f3digo a seguir mostra como realizar um teste de Kolmogorov-Smirnov para um conjunto de dados que segue uma distribui\u00e7\u00e3o log-normal:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> math\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> scipy.stats <span style=\"color: #008000;\">import<\/span> kstest\n<span style=\"color: #008000;\">from<\/span> scipy. <span style=\"color: #3366ff;\">stats<\/span> <span style=\"color: #008000;\">import<\/span> lognorm\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#generate dataset that contains 1000 log-normal distributed values\n<\/span>lognorm_dataset = lognorm. <span style=\"color: #3366ff;\">rvs<\/span> (s=.5, scale= <span style=\"color: #3366ff;\">math.exp<\/span> (1), size=1000)\n\n<span style=\"color: #008080;\">#perform Kolmogorov-Smirnov test for normality\n<\/span>kstest(lognorm_dataset, ' <span style=\"color: #ff0000;\">norm<\/span> ')\n\nKstestResult(statistic=0.84125708308077, pvalue=0.0)\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">A partir do resultado, podemos ver que a estat\u00edstica de teste \u00e9 <strong>0,841<\/strong> e o valor p correspondente \u00e9 <strong>0,0<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Como o valor p \u00e9 inferior a 0,05, rejeitamos a hip\u00f3tese nula do teste Kolmogorov-Smirnov.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Isto significa que temos evid\u00eancias suficientes para dizer que os dados da amostra n\u00e3o prov\u00eam de uma distribui\u00e7\u00e3o normal.<\/span><\/p>\n<h3> <strong>Como lidar com dados n\u00e3o normais<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Se um determinado conjunto de dados <em>n\u00e3o for<\/em> normalmente distribu\u00eddo, muitas vezes podemos realizar uma das seguintes transforma\u00e7\u00f5es para torn\u00e1-lo mais normalmente distribu\u00eddo:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. Transforma\u00e7\u00e3o de log:<\/strong> transforme valores de x em <strong>log(x)<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. Transforma\u00e7\u00e3o de raiz quadrada:<\/strong> Transforme os valores de x em <strong><span style=\"border-top: 1px solid black;\">\u221ax<\/span><\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3. Transforma\u00e7\u00e3o da raiz c\u00fabica:<\/strong> transforme os valores de x em <strong>x <sup>1\/3<\/sup><\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ao realizar essas transforma\u00e7\u00f5es, o conjunto de dados geralmente se torna distribu\u00eddo de forma mais normal.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Leia <a href=\"https:\/\/statorials.org\/pt\/transformar-dados-em-python\/\" target=\"_blank\" rel=\"noopener noreferrer\">este tutorial<\/a> para ver como realizar essas transforma\u00e7\u00f5es em Python.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Muitos testes estat\u00edsticos assumem que os conjuntos de dados s\u00e3o normalmente distribu\u00eddos. Existem quatro maneiras comuns de verificar essa hip\u00f3tese em Python: 1. (M\u00e9todo visual) Crie um histograma. Se o histograma tiver aproximadamente o formato de um \u201csino\u201d, ent\u00e3o os dados ser\u00e3o considerados normalmente distribu\u00eddos. 2. (M\u00e9todo visual) Crie um gr\u00e1fico QQ. Se os pontos [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-3229","post","type-post","status-publish","format-standard","hentry","category-guia"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Como testar a normalidade em Python (4 m\u00e9todos) - Estatologia<\/title>\n<meta name=\"description\" content=\"Este tutorial explica como testar a normalidade em Python, com v\u00e1rios exemplos.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/pt\/teste-de-normalidade-python\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Como testar a normalidade em Python (4 m\u00e9todos) - Estatologia\" \/>\n<meta property=\"og:description\" content=\"Este tutorial explica como testar a normalidade em Python, com v\u00e1rios exemplos.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pt\/teste-de-normalidade-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-18T13:59:04+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/normalitepython1.jpg\" \/>\n<meta name=\"author\" content=\"Dr. benjamim anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"Dr. benjamim anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tempo estimado de leitura\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pt\/teste-de-normalidade-python\/\",\"url\":\"https:\/\/statorials.org\/pt\/teste-de-normalidade-python\/\",\"name\":\"Como testar a normalidade em Python (4 m\u00e9todos) - Estatologia\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pt\/#website\"},\"datePublished\":\"2023-07-18T13:59:04+00:00\",\"dateModified\":\"2023-07-18T13:59:04+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666\"},\"description\":\"Este tutorial explica como testar a normalidade em Python, com v\u00e1rios exemplos.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pt\/teste-de-normalidade-python\/#breadcrumb\"},\"inLanguage\":\"pt-PT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pt\/teste-de-normalidade-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pt\/teste-de-normalidade-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Lar\",\"item\":\"https:\/\/statorials.org\/pt\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Como testar a normalidade em python (4 m\u00e9todos)\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/pt\/#website\",\"url\":\"https:\/\/statorials.org\/pt\/\",\"name\":\"Statorials\",\"description\":\"O seu guia para a literacia estat\u00edstica!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/pt\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"pt-PT\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/e08f98e8db95e0aa9c310e1b27c9c666\",\"name\":\"Dr. benjamim anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-PT\",\"@id\":\"https:\/\/statorials.org\/pt\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/pt\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/pt\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Dr. benjamim anderson\"},\"description\":\"Ol\u00e1, sou Benjamin, um professor aposentado de estat\u00edstica que se tornou professor dedicado na Statorials. 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