{"id":4506,"date":"2023-07-10T14:11:32","date_gmt":"2023-07-10T14:11:32","guid":{"rendered":"https:\/\/statorials.org\/it\/pitone-di-prova-wald\/"},"modified":"2023-07-10T14:11:32","modified_gmt":"2023-07-10T14:11:32","slug":"pitone-di-prova-wald","status":"publish","type":"post","link":"https:\/\/statorials.org\/it\/pitone-di-prova-wald\/","title":{"rendered":"Come eseguire un wald test in python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Un <strong>test di Wald<\/strong> pu\u00f2 essere utilizzato per verificare se uno o pi\u00f9 parametri di un modello sono uguali a determinati valori.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Questo test viene spesso utilizzato per determinare se una o pi\u00f9 variabili predittive in un modello di regressione sono uguali a zero.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Utilizziamo le seguenti <a href=\"https:\/\/statorials.org\/it\/verifica-delle-ipotesi-1\/\" target=\"_blank\" rel=\"noopener\">ipotesi<\/a> nulle e alternative per questo test:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>H <sub>0<\/sub><\/strong> : alcuni insiemi di variabili predittive sono tutti uguali a zero.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>H <sub>A<\/sub><\/strong> : Non tutte le variabili predittive dell&#8217;insieme sono uguali a zero.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Se non riusciamo a rifiutare l\u2019ipotesi nulla, allora possiamo rimuovere dal modello l\u2019insieme specificato di variabili predittive, poich\u00e9 non forniscono un miglioramento statisticamente significativo nell\u2019adattamento del modello.<\/span><\/p>\n<p> <span style=\"color: #000000;\">L&#8217;esempio seguente mostra come eseguire un test Wald in Python<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Esempio: test Wald in Python<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Per questo esempio, utilizzeremo il famoso set di dati <strong>mtcars<\/strong> per adattare il seguente modello di regressione lineare multipla:<\/span><\/p>\n<p> <span style=\"color: #000000;\">mpg = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> disponibile + \u03b2 <sub>2<\/sub> carboidrati + \u03b2 <sub>3<\/sub> cv + \u03b2 <sub>4<\/sub> cil<\/span><\/p>\n<p> <span style=\"color: #000000;\">Il codice seguente mostra come adattare questo modello di regressione e visualizzare il riepilogo del modello:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">formula<\/span> . <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> smf\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> io\n\n<span style=\"color: #008080;\">#define dataset as string\n<\/span>mtcars_data=\"\"\"model,mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb\nMazda RX4,21,6,160,110,3.9,2.62,16.46,0,1,4,4\nMazda RX4 Wag,21.6,160,110,3.9,2.875,17.02,0,1,4,4\nDatsun 710,22.8,4,108,93,3.85,2.32,18.61,1,1,4,1\nHornet 4 Drive,21.4,6,258,110,3.08,3.215,19.44,1,0,3,1\nHornet Sportabout,18.7,8,360,175,3.15,3.44,17.02,0,0,3,2\nValiant,18.1,6,225,105,2.76,3.46,20.22,1,0,3,1\nDuster 360,14.3,8,360,245,3.21,3.57,15.84,0,0,3,4\nMerc 240D,24.4,4,146.7,62,3.69,3.19,20,1,0,4,2\nMerc 230,22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2\nMerc 280,19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4\nMerc 280C,17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4\nMerc 450SE,16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3\nMerc 450SL,17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3\nMerc 450SLC,15.2,8,275.8,180,3.07,3.78,18,0,0,3,3\nCadillac Fleetwood,10.4,8,472,205,2.93,5.25,17.98,0,0,3,4\nLincoln Continental,10.4,8,460,215,3,5.424,17.82,0,0,3,4\nChrysler Imperial,14.7,8,440,230,3.23,5.345,17.42,0,0,3,4\nFiat 128,32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1\nHonda Civic,30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2\nToyota Corolla,33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1\nToyota Corona,21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1\nDodge Challenger,15.5,8,318,150,2.76,3.52,16.87,0,0,3,2\nAMC Javelin,15.2,8,304,150,3.15,3.435,17.3,0,0,3,2\nCamaro Z28,13.3,8,350,245,3.73,3.84,15.41,0,0,3,4\nPontiac Firebird,19.2,8,400,175,3.08,3.845,17.05,0,0,3,2\nFiat X1-9,27.3,4,79,66,4.08,1.935,18.9,1,1,4,1\nPorsche 914-2,26,4,120.3,91,4.43,2.14,16.7,0,1,5,2\nLotus Europa,30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2\nFord Pantera L,15.8,8,351,264,4.22,3.17,14.5,0,1,5,4\nFerrari Dino,19.7,6,145,175,3.62,2.77,15.5,0,1,5,6\nMaserati Bora,15.8,301,335,3.54,3.57,14.6,0,1,5,8\nVolvo 142E,21.4,4,121,109,4.11,2.78,18.6,1,1,4,2\"\"\"\n\n<span style=\"color: #008080;\">#convert string to DataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">read_csv<\/span> ( <span style=\"color: #3366ff;\">io.StringIO<\/span> (mtcars_data), sep=\" <span style=\"color: #ff0000;\">,<\/span> \")\n\n<span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>results = smf. <span style=\"color: #3366ff;\">ols<\/span> (' <span style=\"color: #ff0000;\">mpg~disp+carb+hp+cyl<\/span> ',df). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view regression model summary\n<\/span>results. <span style=\"color: #3366ff;\">summary<\/span> ()\n\n\tcoef std err t P&gt;|t| [0.025 0.975]\nIntercept34.0216 2.523 13.482 0.000 28.844 39.199\navailable -0.0269 0.011 -2.379 0.025 -0.050 -0.004\ncarb -0.9269 0.579 -1.601 0.121 -2.115 0.261\nhp 0.0093 0.021 0.452 0.655 -0.033 0.052\ncyl -1.0485 0.784 -1.338 0.192 -2.657 0.560\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Successivamente, possiamo utilizzare la funzione <strong>statsmodels<\/strong> <strong>wald_test()<\/strong> per verificare se i coefficienti di regressione per le variabili predittive &#8220;hp&#8221; e &#8220;cyl&#8221; sono entrambi uguali a zero.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Il codice seguente mostra come utilizzare in pratica questa funzione:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform Wald Test to determine if 'hp' and 'cyl' coefficients are both zero<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">results.wald_test<\/span> (' <span style=\"color: #ff0000;\">(hp=0, cyl=0)<\/span> '))\n\nF test: F=array([[0.91125429]]), p=0.41403001184235005, df_denom=27, df_num=2\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dal risultato possiamo vedere che il <a href=\"https:\/\/statorials.org\/it\/valori-p-significativita-statistica\/\" target=\"_blank\" rel=\"noopener\">valore p<\/a> del test \u00e8 <strong>0,414<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Poich\u00e9 questo valore p non \u00e8 inferiore a 0,05, non riusciamo a rifiutare l&#8217;ipotesi nulla del test di Wald.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ci\u00f2 significa che possiamo assumere che i coefficienti di regressione per le variabili predittive \u201chp\u201d e \u201ccyl\u201d siano entrambi uguali a zero.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Possiamo rimuovere questi termini dal modello perch\u00e9 non migliorano in modo statisticamente significativo l&#8217;adattamento complessivo del modello.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Risorse addizionali<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">I seguenti tutorial spiegano come eseguire altre operazioni comuni in Python:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/it\/regressione-lineare-semplice-in-python\/\" target=\"_blank\" rel=\"noopener\">Come eseguire una regressione lineare semplice<\/a><br \/> <a href=\"https:\/\/statorials.org\/it\/python-di-regressione-polinomiale\/\" target=\"_blank\" rel=\"noopener\">Come eseguire la regressione polinomiale in Python<\/a><br \/> <a href=\"https:\/\/statorials.org\/it\/come-calcolare-vive-in-python\/\" target=\"_blank\" rel=\"noopener\">Come calcolare VIF in Python<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Un test di Wald pu\u00f2 essere utilizzato per verificare se uno o pi\u00f9 parametri di un modello sono uguali a determinati valori. Questo test viene spesso utilizzato per determinare se una o pi\u00f9 variabili predittive in un modello di regressione sono uguali a zero. Utilizziamo le seguenti ipotesi nulle e alternative per questo test: H [&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":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Come eseguire un Wald Test in Python \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"Questo tutorial spiega come eseguire un test Wald in Python, incluso un esempio completo.\" \/>\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\/it\/pitone-di-prova-wald\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Come eseguire un Wald Test in Python \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"Questo tutorial spiega come eseguire un test Wald in Python, incluso un esempio completo.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/it\/pitone-di-prova-wald\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-10T14:11:32+00:00\" \/>\n<meta name=\"author\" content=\"Benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minuti\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/it\/pitone-di-prova-wald\/\",\"url\":\"https:\/\/statorials.org\/it\/pitone-di-prova-wald\/\",\"name\":\"Come eseguire un Wald Test in Python \u2013 Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/it\/#website\"},\"datePublished\":\"2023-07-10T14:11:32+00:00\",\"dateModified\":\"2023-07-10T14:11:32+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/it\/#\/schema\/person\/0896f191fb9fb019f2cd8623112cb3ae\"},\"description\":\"Questo tutorial spiega come eseguire un test Wald in Python, incluso un esempio completo.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/it\/pitone-di-prova-wald\/#breadcrumb\"},\"inLanguage\":\"it-IT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/it\/pitone-di-prova-wald\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/it\/pitone-di-prova-wald\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Casa\",\"item\":\"https:\/\/statorials.org\/it\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Come eseguire un wald test in python\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/it\/#website\",\"url\":\"https:\/\/statorials.org\/it\/\",\"name\":\"Statorials\",\"description\":\"La tua guida all&#039;alfabetizzazione statistica!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/it\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"it-IT\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/it\/#\/schema\/person\/0896f191fb9fb019f2cd8623112cb3ae\",\"name\":\"Benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"it-IT\",\"@id\":\"https:\/\/statorials.org\/it\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/it\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/it\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin anderson\"},\"description\":\"Ciao, sono Benjamin, un professore di statistica in pensione diventato insegnante dedicato di Statorials. 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