178 lines
3.8 KiB
Plaintext
178 lines
3.8 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3b66972b",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Molla statica 1 Calibro\n",
|
|
"Ac = 3.20021 ± 0.00923\n",
|
|
"\n",
|
|
"# Molla statica 1 Sonar\n",
|
|
"Ac = 3.21962 ± 0.00633\n",
|
|
"\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": 2,
|
|
"id": "b349ba73",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"from scipy.stats import t as student_t"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "a68eb302",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"t = 3.807\n",
|
|
"p-value (two-tailed) = 0.1924 %\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Valori statici (sonar)\n",
|
|
"Nd = 10\n",
|
|
"Ad = 3.220\n",
|
|
"uAd = 0.006\n",
|
|
"\n",
|
|
"#Valori statici (calibro)\n",
|
|
"Ns = 6\n",
|
|
"As = 3.200\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",
|
|
"\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"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|