194 lines
3.9 KiB
Plaintext
194 lines
3.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ba4e56bc",
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"metadata": {},
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"source": [
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"## Valori statici (calibro)\n",
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"N = 10\n",
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"\n",
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"A = 23.98765 ± 0.18498"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aaf30c1f",
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"metadata": {},
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"source": [
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"## Valori statici (sonar)\n",
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"N = 6\n",
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"\n",
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"A = 23.41058 ± 0.22237"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7a851edb",
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"metadata": {},
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"source": [
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"## Valori dinamici (sonar)\n",
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"N = 4\n",
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"\n",
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"KC= 24.3546921 ± 0.03539\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b349ba73",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from scipy.stats import t as student_t"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a68eb302",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"t = 1.951\n",
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"p-value (two-tailed) = 7.1395 %\n"
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]
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}
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],
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"source": [
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"# Valori statici (calibro)\n",
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"Nd = 10\n",
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"Ad = 24.00\n",
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"uAd = 0.18 * 3\n",
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"\n",
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"\n",
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"#Valori statici (sonar)\n",
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"Ns = 6\n",
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"As = 23.41\n",
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"uAs = 0.22 * 3\n",
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"\n",
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"#Nomi coerenti con Cannelli\n",
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"GdL = Nd + Ns - 2\n",
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"\n",
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"s2 = ( (Nd - 1) * uAd**2 + (Ns - 1) * uAs**2 ) / GdL\n",
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"\n",
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"sigma2 = ( s2 / Nd ) + ( s2 / Ns )\n",
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"\n",
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"t = ( Ad - As ) / np.sqrt( sigma2 )\n",
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"\n",
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"\n",
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"p_value = 2 * (1 - student_t.cdf(abs(t), df=GdL))\n",
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"print(f\"t = {t:.3f}\")\n",
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"print(f\"p-value (two-tailed) = {p_value * 100:.4f} %\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "2746d086",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"t = -0.972\n",
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"p-value (two-tailed) = 35.0452 %\n"
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]
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}
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],
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"source": [
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"# Valori statici (calibro)\n",
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"Nd = 10\n",
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"Ad = 24.00\n",
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"uAd = 0.18 *3\n",
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"\n",
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"#Valori dinamici (sonar)\n",
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"Ns = 4\n",
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"As = 24.35\n",
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"uAs = 0.26 *3\n",
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"\n",
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"#Nomi coerenti con Cannelli\n",
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"GdL = Nd + Ns - 2\n",
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"\n",
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"s2 = ( (Nd - 1) * uAd**2 + (Ns - 1) * uAs**2 ) / GdL\n",
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"\n",
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"sigma2 = ( s2 / Nd ) + ( s2 / Ns )\n",
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"\n",
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"t = ( Ad - As ) / np.sqrt( sigma2 )\n",
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"\n",
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"\n",
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"p_value = 2 * (1 - student_t.cdf(abs(t), df=GdL))\n",
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"print(f\"t = {t:.3f}\")\n",
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"print(f\"p-value (two-tailed) = {p_value * 100:.4f} %\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "be0c6cc6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"t = -2.775\n",
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"p-value (two-tailed) = 2.4092 %\n"
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]
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}
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],
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"source": [
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"# Valori statici (sonar)\n",
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"Nd = 6\n",
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"Ad = 23.41\n",
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"uAd = 0.22 * 3\n",
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"\n",
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"#Valori dinamici (sonar)\n",
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"Ns = 4\n",
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"As = 24.35\n",
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"uAs = 0.03 * 3\n",
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"\n",
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"#Nomi coerenti con Cannelli\n",
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"GdL = Nd + Ns - 2\n",
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"\n",
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"s2 = ( (Nd - 1) * uAd**2 + (Ns - 1) * uAs**2 ) / GdL\n",
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"\n",
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"sigma2 = ( s2 / Nd ) + ( s2 / Ns )\n",
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"\n",
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"t = ( Ad - As ) / np.sqrt( sigma2 )\n",
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"\n",
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"\n",
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"p_value = 2 * (1 - student_t.cdf(abs(t), df=GdL))\n",
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"print(f\"t = {t:.3f}\")\n",
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"print(f\"p-value (two-tailed) = {p_value * 100:.4f} %\")\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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