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Molto lavoro per negare il modello, ora si inizia con LaTeX perché altrimenti non finiamo

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Jacopo Basso Ricci
2026-04-08 10:11:20 +02:00
parent 84e88fd6e6
commit 1c8f6fb418
13 changed files with 14051 additions and 378 deletions
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46
molla/compatibilita/comp1.ipynb
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@@ -8,7 +8,7 @@
"## Valori statici (calibro)\n",
"N = 10\n",
"\n",
"A = 23.97 +- 0.16 "
"A = 23.98765 ± 0.18498"
]
},
{
@@ -19,7 +19,7 @@
"## Valori statici (sonar)\n",
"N = 6\n",
"\n",
"A = 23.46 +- 0.23\n"
"A = 23.41058 ± 0.22237"
]
},
{
@@ -30,12 +30,12 @@
"## Valori dinamici (sonar)\n",
"N = 4\n",
"\n",
"K nostro: 24.35 +- 0.25\n"
"KC= 24.3546921 ± 0.03539\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 1,
"id": "b349ba73",
"metadata": {},
"outputs": [],
@@ -46,7 +46,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 2,
"id": "a68eb302",
"metadata": {},
"outputs": [
@@ -54,22 +54,22 @@
"name": "stdout",
"output_type": "stream",
"text": [
"t = 1.751\n",
"p-value (two-tailed) = 10.1827 %\n"
"t = 1.951\n",
"p-value (two-tailed) = 7.1395 %\n"
]
}
],
"source": [
"# Valori statici (calibro)\n",
"Nd = 10\n",
"Ad = 23.97\n",
"uAd = 0.16 * 3\n",
"Ad = 24.00\n",
"uAd = 0.18 * 3\n",
"\n",
"\n",
"#Valori statici (sonar)\n",
"Ns = 6\n",
"As = 23.46\n",
"uAs = 0.23 * 3\n",
"As = 23.41\n",
"uAs = 0.22 * 3\n",
"\n",
"#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n",
@@ -88,7 +88,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 6,
"id": "2746d086",
"metadata": {},
"outputs": [
@@ -96,21 +96,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
"t = -1.147\n",
"p-value (two-tailed) = 27.3612 %\n"
"t = -0.972\n",
"p-value (two-tailed) = 35.0452 %\n"
]
}
],
"source": [
"# Valori statici (calibro)\n",
"Nd = 10\n",
"Ad = 23.97\n",
"uAd = 0.16 * 3\n",
"Ad = 24.00\n",
"uAd = 0.18 *3\n",
"\n",
"#Valori dinamici (sonar)\n",
"Ns = 4\n",
"As = 24.35\n",
"uAs = 0.25 * 3\n",
"uAs = 0.26 *3\n",
"\n",
"#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n",
@@ -129,7 +129,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 4,
"id": "be0c6cc6",
"metadata": {},
"outputs": [
@@ -137,21 +137,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
"t = -1.934\n",
"p-value (two-tailed) = 8.9234 %\n"
"t = -2.775\n",
"p-value (two-tailed) = 2.4092 %\n"
]
}
],
"source": [
"# Valori statici (sonar)\n",
"Nd = 6\n",
"Ad = 23.46\n",
"uAd = 0.23 * 3\n",
"Ad = 23.41\n",
"uAd = 0.22 * 3\n",
"\n",
"#Valori dinamici (sonar)\n",
"Ns = 4\n",
"As = 24.35\n",
"uAs = 0.25 * 3\n",
"uAs = 0.03 * 3\n",
"\n",
"#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n",

122
molla/compatibilita/comp2.ipynb
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@@ -2,29 +2,25 @@
"cells": [
{
"cell_type": "markdown",
"id": "ba4e56bc",
"id": "3b66972b",
"metadata": {},
"source": [
"## Valori dinamici\n",
"N = 10\n",
"# Molla statica 1 Calibro\n",
"Ac = 3.20021 ± 0.00923\n",
"\n",
"A = 3.21951 +- 0.00470 "
]
},
{
"cell_type": "markdown",
"id": "aaf30c1f",
"metadata": {},
"source": [
"## Valori statici\n",
"N = 6\n",
"# Molla statica 1 Sonar\n",
"Ac = 3.21962 ± 0.00633\n",
"\n",
"A = 3.2002 +- 0.0092\n"
"# Molla dinamica 1 Sonar\n",
"KdC = 3.2792872 ± 0.00924\n",
"\n",
"# Molla dinamica 1 Cronometro\n",
"KdtC = 3.66092 ± 0.10078\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 2,
"id": "b349ba73",
"metadata": {},
"outputs": [],
@@ -35,7 +31,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 3,
"id": "a68eb302",
"metadata": {},
"outputs": [
@@ -43,21 +39,103 @@
"name": "stdout",
"output_type": "stream",
"text": [
"t = 5.774\n",
"p-value (two-tailed) = 0.0048 %\n"
"t = 3.807\n",
"p-value (two-tailed) = 0.1924 %\n"
]
}
],
"source": [
"# Valori dimanici (sonar)\n",
"# Valori statici (sonar)\n",
"Nd = 10\n",
"Ad = 3.220\n",
"uAd = 0.005\n",
"Ad = 3.220\n",
"uAd = 0.006\n",
"\n",
"#Valori statici (calibro)\n",
"Ns = 6\n",
"As = 3.200\n",
"uAs = 0.009\n",
"uAs = 0.015\n",
"\n",
"#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n",
"\n",
"s2 = ( (Nd - 1) * uAd**2 + (Ns - 1) * uAs**2 ) / GdL\n",
"\n",
"sigma2 = ( s2 / Nd ) + ( s2 / Ns )\n",
"\n",
"t = ( Ad - As ) / np.sqrt( sigma2 )\n",
"\n",
"\n",
"p_value = 2 * (1 - student_t.cdf(abs(t), df=GdL))\n",
"print(f\"t = {t:.3f}\")\n",
"print(f\"p-value (two-tailed) = {p_value * 100:.4f} %\")\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "459b7f56",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t = -5.278\n",
"p-value (two-tailed) = 0.0117 %\n"
]
}
],
"source": [
"# Valori statici (sonar)\n",
"Nd = 10\n",
"Ad = 3.220\n",
"uAd = 0.006 * 3\n",
"\n",
"#Valori dinamici (sonar)\n",
"Ns = 6\n",
"As = 3.279\n",
"uAs = 0.009 * 3\n",
"\n",
"#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n",
"\n",
"s2 = ( (Nd - 1) * uAd**2 + (Ns - 1) * uAs**2 ) / GdL\n",
"\n",
"sigma2 = ( s2 / Nd ) + ( s2 / Ns )\n",
"\n",
"t = ( Ad - As ) / np.sqrt( sigma2 )\n",
"\n",
"\n",
"p_value = 2 * (1 - student_t.cdf(abs(t), df=GdL))\n",
"print(f\"t = {t:.3f}\")\n",
"print(f\"p-value (two-tailed) = {p_value * 100:.4f} %\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5d82905f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t = 0.701\n",
"p-value (two-tailed) = 49.4616 %\n"
]
}
],
"source": [
"# Valori dinamici (sonar)\n",
"Nd = 10\n",
"Ad = 3.279\n",
"uAd = 0.09 *3\n",
"\n",
"#Valori statici (calibro)\n",
"Ns = 6\n",
"As = 3.200\n",
"uAs = 0.015 * 3\n",
"\n",
"#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n",

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molla/statica/mollaStatica1.ipynb
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@@ -18,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 58,
"execution_count": 20,
"id": "f34c5b88",
"metadata": {},
"outputs": [],
@@ -51,7 +51,7 @@
},
{
"cell_type": "code",
"execution_count": 59,
"execution_count": 21,
"id": "08efb2be",
"metadata": {},
"outputs": [],
@@ -86,7 +86,7 @@
},
{
"cell_type": "code",
"execution_count": 60,
"execution_count": 22,
"id": "5494409f",
"metadata": {},
"outputs": [
@@ -215,7 +215,7 @@
"4 168.53 0.002887 "
]
},
"execution_count": 60,
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -226,7 +226,7 @@
},
{
"cell_type": "code",
"execution_count": 61,
"execution_count": 23,
"id": "976d5531",
"metadata": {},
"outputs": [
@@ -282,7 +282,7 @@
},
{
"cell_type": "code",
"execution_count": 62,
"execution_count": 24,
"id": "2ad19283",
"metadata": {},
"outputs": [
@@ -290,10 +290,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
"[23.12238655 23.79957321 23.89802584 23.89480155 24.50322786 24.30058505\n",
" 24.15933014 24.09836266 23.99026349 23.88530016]\n",
"[0.22419984 0.16285763 0.10115386 0.15188559 0.31275403 0.14045171\n",
" 0.2012065 0.36234381 0.31901124 0.61345112]\n"
"[0.04459466 0.05636189 0.07164148 0.08906597 0.04476779 0.05643232\n",
" 0.07206497 0.04463879 0.05651695 0.04482161]\n",
"[0.08074652 0.11183321 0.10315038 0.2075251 0.10230673 0.09273618\n",
" 0.20255041 0.12077527 0.21682558 0.21247745]\n"
]
}
],
@@ -305,13 +305,13 @@
"uK = np.sqrt((1/este)**2 * uF**2 + (F / este**2)**2 * ueste**2 )\n",
"\n",
"\n",
"print(K)\n",
"print(uK)"
"print(uF)\n",
"print(ueste)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"execution_count": 25,
"id": "5f59d6c9",
"metadata": {},
"outputs": [
@@ -360,7 +360,7 @@
},
{
"cell_type": "code",
"execution_count": 64,
"execution_count": 26,
"id": "7e75ec05",
"metadata": {},
"outputs": [],
@@ -387,7 +387,7 @@
},
{
"cell_type": "code",
"execution_count": 65,
"execution_count": 27,
"id": "aefe7756",
"metadata": {},
"outputs": [
@@ -400,8 +400,8 @@
"Dep. Variable: y R-squared: 1.000\n",
"Model: OLS Adj. R-squared: 1.000\n",
"Method: Least Squares F-statistic: 2.238e+04\n",
"Date: Sat, 04 Apr 2026 Prob (F-statistic): 4.46e-15\n",
"Time: 19:37:55 Log-Likelihood: -27.238\n",
"Date: Tue, 07 Apr 2026 Prob (F-statistic): 4.46e-15\n",
"Time: 15:51:49 Log-Likelihood: -27.238\n",
"No. Observations: 10 AIC: 58.48\n",
"Df Residuals: 8 BIC: 59.08\n",
"Df Model: 1 \n",
@@ -449,7 +449,7 @@
},
{
"cell_type": "code",
"execution_count": 66,
"execution_count": 28,
"id": "1d42b009",
"metadata": {},
"outputs": [
@@ -515,7 +515,7 @@
},
{
"cell_type": "code",
"execution_count": 67,
"execution_count": 29,
"id": "986ff4a6",
"metadata": {},
"outputs": [
@@ -577,7 +577,7 @@
},
{
"cell_type": "code",
"execution_count": 68,
"execution_count": 30,
"id": "2d4b7144",
"metadata": {},
"outputs": [
@@ -653,7 +653,7 @@
},
{
"cell_type": "code",
"execution_count": 69,
"execution_count": 31,
"id": "e2407a04",
"metadata": {},
"outputs": [
@@ -722,7 +722,7 @@
},
{
"cell_type": "code",
"execution_count": 70,
"execution_count": 32,
"id": "32e9948f",
"metadata": {},
"outputs": [
@@ -785,7 +785,7 @@
},
{
"cell_type": "code",
"execution_count": 71,
"execution_count": 33,
"id": "bfb895c6",
"metadata": {},
"outputs": [
@@ -868,7 +868,7 @@
},
{
"cell_type": "code",
"execution_count": 72,
"execution_count": 34,
"id": "202de438",
"metadata": {},
"outputs": [
@@ -942,7 +942,7 @@
},
{
"cell_type": "code",
"execution_count": 73,
"execution_count": 35,
"id": "caf23dbe",
"metadata": {},
"outputs": [
@@ -1000,7 +1000,7 @@
},
{
"cell_type": "code",
"execution_count": 74,
"execution_count": 36,
"id": "8f5c8bb7",
"metadata": {},
"outputs": [
@@ -1010,8 +1010,8 @@
"text": [
"u_strum_m = 0.004082\n",
"u_strum_Dx = 0.020412\n",
"uF_strum = 0.089066\n",
"uK_strum = 0.662828\n"
"uF_strum = 0.058091\n",
"uK_strum = 0.273764\n"
]
}
],
@@ -1028,10 +1028,10 @@
"umasse_strum = np.maximum(umasse, u_strum_m)\n",
"\n",
"# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
"uF_strum = np.max(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
"uDx_strum = np.max(ueste_strum)\n",
"uF_strum = np.average(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
"uDx_strum = np.average(ueste_strum)\n",
"\n",
"uK_strum = np.max(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
"uK_strum = np.average(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
"\n",
"print(f\"u_strum_m = {u_strum_m:.6f}\")\n",
"print(f\"u_strum_Dx = {u_strum_Dx:.6f}\")\n",
@@ -1049,7 +1049,7 @@
},
{
"cell_type": "code",
"execution_count": 75,
"execution_count": 37,
"id": "a1dc24c9",
"metadata": {},
"outputs": [],
@@ -1096,12 +1096,12 @@
"id": "2e57c7d8",
"metadata": {},
"source": [
"## Risutltati della propagazione dell'errore strumentale massimo"
"## Risultati della propagazione dell'errore strumentale massimo"
]
},
{
"cell_type": "code",
"execution_count": 76,
"execution_count": 38,
"id": "c8e264fa",
"metadata": {},
"outputs": [
@@ -1110,22 +1110,22 @@
"output_type": "stream",
"text": [
"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata K = 23.95113 ± 0.66530\n",
"Media pesata K = 23.95113 ± 0.27970\n",
"\n",
"RISULTATI REGRESSIONE OLS:\n",
"Aols = 23.96871 ± 0.68192\n",
"Bols = 0.09993 ± 2.98913\n",
"Chi² OLS = 3.95368 | rid = 0.49421 | P = 0.13872\n",
"Aols = 23.96871 ± 0.31720\n",
"Bols = 0.09993 ± 2.92755\n",
"Chi² OLS = 7.21389 | rid = 0.90174 | P = 0.48626\n",
"\n",
"RISULTATI REGRESSIONE Carpi:\n",
"AC = 24.04738 ± 0.67577\n",
"BC = -1.56983 ± 2.18409\n",
"Chi² Carpi = 4.03420 | rid = 0.50428 | P = 0.14598\n",
"AC = 24.04738 ± 0.30375\n",
"BC = -1.56983 ± 2.09901\n",
"Chi² Carpi = 7.28743 | rid = 0.91093 | P = 0.49404\n",
"\n",
"RISULTATI REGRESSIONE York:\n",
"AY = 24.06480 ± 0.67582\n",
"BY = -1.81758 ± 2.18788\n",
"Chi² York = 4.06701 | rid = 0.50838 | P = 0.14897\n"
"AY = 24.06480 ± 0.30386\n",
"BY = -1.81758 ± 2.10295\n",
"Chi² York = 7.33375 | rid = 0.91672 | P = 0.49891\n"
]
}
],
@@ -1163,8 +1163,8 @@
"\n",
"In generale con il Chi² vale:\n",
"- \\~50%: Errori ottimamente stimati\n",
"- \\<5% : Fit troppo grande\n",
"- \\>95%: Fit troppo piccolo\n",
"- \\<5% : Fit troppo grande (errori sovrastimati)\n",
"- \\>95%: Fit troppo piccolo (errori sottostimati)\n",
"\n",
"In generale mi sembra che nei paper se è presente un Chi² si riporta la probabilità complementare (probabilità di ottenere gli stessi risultati o peggio)"
]

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molla/statica/mollaStatica2.ipynb
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@@ -18,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 82,
"execution_count": 105,
"id": "f34c5b88",
"metadata": {},
"outputs": [],
@@ -51,7 +51,7 @@
},
{
"cell_type": "code",
"execution_count": 83,
"execution_count": 106,
"id": "08efb2be",
"metadata": {},
"outputs": [],
@@ -86,7 +86,7 @@
},
{
"cell_type": "code",
"execution_count": 84,
"execution_count": 107,
"id": "5494409f",
"metadata": {},
"outputs": [
@@ -199,7 +199,7 @@
"3 0.188750 108.610000 0.002887 "
]
},
"execution_count": 84,
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
@@ -210,7 +210,7 @@
},
{
"cell_type": "code",
"execution_count": 85,
"execution_count": 108,
"id": "976d5531",
"metadata": {},
"outputs": [
@@ -265,7 +265,7 @@
},
{
"cell_type": "code",
"execution_count": 86,
"execution_count": 109,
"id": "2ad19283",
"metadata": {},
"outputs": [
@@ -292,7 +292,7 @@
},
{
"cell_type": "code",
"execution_count": 87,
"execution_count": 110,
"id": "5f59d6c9",
"metadata": {},
"outputs": [
@@ -341,7 +341,7 @@
},
{
"cell_type": "code",
"execution_count": 88,
"execution_count": 111,
"id": "7e75ec05",
"metadata": {},
"outputs": [],
@@ -368,7 +368,7 @@
},
{
"cell_type": "code",
"execution_count": 89,
"execution_count": 112,
"id": "aefe7756",
"metadata": {},
"outputs": [
@@ -381,8 +381,8 @@
"Dep. Variable: y R-squared: 1.000\n",
"Model: OLS Adj. R-squared: 1.000\n",
"Method: Least Squares F-statistic: 1.277e+05\n",
"Date: Sat, 04 Apr 2026 Prob (F-statistic): 3.68e-10\n",
"Time: 19:38:07 Log-Likelihood: -7.2422\n",
"Date: Mon, 06 Apr 2026 Prob (F-statistic): 3.68e-10\n",
"Time: 10:04:08 Log-Likelihood: -7.2422\n",
"No. Observations: 6 AIC: 18.48\n",
"Df Residuals: 4 BIC: 18.07\n",
"Df Model: 1 \n",
@@ -438,7 +438,7 @@
},
{
"cell_type": "code",
"execution_count": 90,
"execution_count": 113,
"id": "1d42b009",
"metadata": {},
"outputs": [
@@ -504,7 +504,7 @@
},
{
"cell_type": "code",
"execution_count": 91,
"execution_count": 114,
"id": "986ff4a6",
"metadata": {},
"outputs": [
@@ -562,7 +562,7 @@
},
{
"cell_type": "code",
"execution_count": 92,
"execution_count": 115,
"id": "2d4b7144",
"metadata": {},
"outputs": [
@@ -638,7 +638,7 @@
},
{
"cell_type": "code",
"execution_count": 93,
"execution_count": 116,
"id": "e2407a04",
"metadata": {},
"outputs": [
@@ -707,7 +707,7 @@
},
{
"cell_type": "code",
"execution_count": 94,
"execution_count": 117,
"id": "32e9948f",
"metadata": {},
"outputs": [
@@ -766,7 +766,7 @@
},
{
"cell_type": "code",
"execution_count": 95,
"execution_count": 118,
"id": "bfb895c6",
"metadata": {},
"outputs": [
@@ -849,7 +849,7 @@
},
{
"cell_type": "code",
"execution_count": 96,
"execution_count": 119,
"id": "202de438",
"metadata": {},
"outputs": [
@@ -923,7 +923,7 @@
},
{
"cell_type": "code",
"execution_count": 97,
"execution_count": 120,
"id": "caf23dbe",
"metadata": {},
"outputs": [
@@ -981,7 +981,7 @@
},
{
"cell_type": "code",
"execution_count": 98,
"execution_count": 121,
"id": "8f5c8bb7",
"metadata": {},
"outputs": [
@@ -991,8 +991,8 @@
"text": [
"u_strum_m = 0.004082\n",
"u_strum_Dx = 0.020412\n",
"uF_strum = 0.071603\n",
"uK_strum = 0.022946\n"
"uF_strum = 0.053018\n",
"uK_strum = 0.012599\n"
]
}
],
@@ -1009,10 +1009,10 @@
"umasse_strum = np.maximum(umasse, u_strum_m)\n",
"\n",
"# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
"uF_strum = np.max(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
"uDx_strum = np.max(ueste_strum)\n",
"uF_strum = np.average(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
"uDx_strum = np.average(ueste_strum)\n",
"\n",
"uK_strum = np.max(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
"uK_strum = np.average(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
"\n",
"print(f\"u_strum_m = {u_strum_m:.6f}\")\n",
"print(f\"u_strum_Dx = {u_strum_Dx:.6f}\")\n",
@@ -1030,7 +1030,7 @@
},
{
"cell_type": "code",
"execution_count": 99,
"execution_count": 122,
"id": "a1dc24c9",
"metadata": {},
"outputs": [],
@@ -1082,7 +1082,7 @@
},
{
"cell_type": "code",
"execution_count": 100,
"execution_count": 123,
"id": "c8e264fa",
"metadata": {},
"outputs": [
@@ -1091,22 +1091,22 @@
"output_type": "stream",
"text": [
"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata K = 3.20156 ± 0.02327\n",
"Media pesata K = 3.20156 ± 0.01318\n",
"\n",
"RISULTATI REGRESSIONE OLS:\n",
"Aols = 3.20145 ± 0.02463\n",
"Bols = 0.02655 ± 0.99102\n",
"Chi² OLS = 1.17582 | rid = 0.29396 | P = 0.11794\n",
"Aols = 3.20145 ± 0.01546\n",
"Bols = 0.02655 ± 0.99084\n",
"Chi² OLS = 1.52520 | rid = 0.38130 | P = 0.17783\n",
"\n",
"RISULTATI REGRESSIONE Carpi:\n",
"AC = 3.20054 ± 0.02472\n",
"BC = 0.11948 ± 0.95263\n",
"Chi² Carpi = 1.17417 | rid = 0.29354 | P = 0.11767\n",
"AC = 3.20054 ± 0.01559\n",
"BC = 0.11948 ± 0.95244\n",
"Chi² Carpi = 1.52143 | rid = 0.38036 | P = 0.17716\n",
"\n",
"RISULTATI REGRESSIONE York:\n",
"AY = 3.20061 ± 0.02472\n",
"BY = 0.11263 ± 0.95193\n",
"Chi² York = 1.17400 | rid = 0.29350 | P = 0.11764\n"
"AY = 3.20061 ± 0.01559\n",
"BY = 0.11263 ± 0.95173\n",
"Chi² York = 1.52133 | rid = 0.38033 | P = 0.17714\n"
]
}
],

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