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

.gitignore

@@ -2,3 +2,281 @@
 Funzioni\ matlab.txt
 prova*
 test*
+
+
+## Core latex/pdflatex auxiliary files:
+*.aux
+*.lof
+*.log
+*.lot
+*.fls
+*.out
+*.toc
+*.fmt
+*.fot
+*.cb
+*.cb2
+.*.lb
+
+## Intermediate documents:
+*.dvi
+*.xdv
+*-converted-to.*
+
+## Bibliography auxiliary files (bibtex/biblatex/biber):
+*.bbl
+*.bcf
+*.blg
+*-blx.aux
+*-blx.bib
+*.run.xml
+
+## Build tool auxiliary files:
+*.fdb_latexmk
+*.synctex
+*.synctex(busy)
+*.synctex.gz
+*.synctex.gz(busy)
+*.pdfsync
+
+## Build tool directories for auxiliary files
+# latexrun
+latex.out/
+
+## Auxiliary and intermediate files from other packages:
+# algorithms
+*.alg
+*.loa
+
+# achemso
+acs-*.bib
+
+# amsthm
+*.thm
+
+# beamer
+*.nav
+*.pre
+*.snm
+*.vrb
+
+# changes
+*.soc
+
+# comment
+*.cut
+
+# cprotect
+*.cpt
+
+# elsarticle (documentclass of Elsevier journals)
+*.spl
+
+# endnotes
+*.ent
+
+# fixme
+*.lox
+
+# feynmf/feynmp
+*.mf
+*.mp
+*.t[1-9]
+*.t[1-9][0-9]
+*.tfm
+
+#(r)(e)ledmac/(r)(e)ledpar
+*.end
+*.?end
+*.[1-9]
+*.[1-9][0-9]
+*.[1-9][0-9][0-9]
+*.[1-9]R
+*.[1-9][0-9]R
+*.[1-9][0-9][0-9]R
+*.eledsec[1-9]
+*.eledsec[1-9]R
+*.eledsec[1-9][0-9]
+*.eledsec[1-9][0-9]R
+*.eledsec[1-9][0-9][0-9]
+*.eledsec[1-9][0-9][0-9]R
+
+# glossaries
+*.acn
+*.acr
+*.glg
+*.glo
+*.gls
+*.glsdefs
+*.lzo
+*.lzs
+
+# uncomment this for glossaries-extra (will ignore makeindex's style files!)
+# *.ist
+
+# gnuplottex
+*-gnuplottex-*
+
+# gregoriotex
+*.gaux
+*.glog
+*.gtex
+
+# htlatex
+*.4ct
+*.4tc
+*.idv
+*.lg
+*.trc
+*.xref
+
+# hyperref
+*.brf
+
+# knitr
+*-concordance.tex
+# TODO Uncomment the next line if you use knitr and want to ignore its generated tikz files
+# *.tikz
+*-tikzDictionary
+
+# listings
+*.lol
+
+# luatexja-ruby
+*.ltjruby
+
+# makeidx
+*.idx
+*.ilg
+*.ind
+
+# minitoc
+*.maf
+*.mlf
+*.mlt
+*.mtc[0-9]*
+*.slf[0-9]*
+*.slt[0-9]*
+*.stc[0-9]*
+
+# minted
+_minted*
+*.pyg
+
+# morewrites
+*.mw
+
+# newpax
+*.newpax
+
+# nomencl
+*.nlg
+*.nlo
+*.nls
+
+# pax
+*.pax
+
+# pdfpcnotes
+*.pdfpc
+
+# sagetex
+*.sagetex.sage
+*.sagetex.py
+*.sagetex.scmd
+
+# scrwfile
+*.wrt
+
+# sympy
+*.sout
+*.sympy
+sympy-plots-for-*.tex/
+
+# pdfcomment
+*.upa
+*.upb
+
+# pythontex
+*.pytxcode
+pythontex-files-*/
+
+# tcolorbox
+*.listing
+
+# thmtools
+*.loe
+
+# TikZ & PGF
+*.dpth
+*.md5
+*.auxlock
+
+# todonotes
+*.tdo
+
+# vhistory
+*.hst
+*.ver
+
+# easy-todo
+*.lod
+
+# xcolor
+*.xcp
+
+# xmpincl
+*.xmpi
+
+# xindy
+*.xdy
+
+# xypic precompiled matrices and outlines
+*.xyc
+*.xyd
+
+# endfloat
+*.ttt
+*.fff
+
+# Latexian
+TSWLatexianTemp*
+
+## Editors:
+# WinEdt
+*.bak
+*.sav
+
+# Texpad
+.texpadtmp
+
+# LyX
+*.lyx~
+
+# Kile
+*.backup
+
+# gummi
+.*.swp
+
+# KBibTeX
+*~[0-9]*
+
+# TeXnicCenter
+*.tps
+
+# auto folder when using emacs and auctex
+./auto/*
+*.el
+
+# expex forward references with \gathertags
+*-tags.tex
+
+# standalone packages
+*.sta
+
+# Makeindex log files
+*.lpz
+
+# xwatermark package
+*.xwm

molla/compatibilita/comp1.ipynb

@@ -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",

molla/compatibilita/comp2.ipynb

@@ -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",
+    "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",

molla/statica/mollaStatica1.ipynb

@@ -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)"
    ]

molla/statica/mollaStatica2.ipynb

@@ -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"
      ]
     }
    ],