diff --git a/tools/OhmsLawExperiment.dat b/tools/OhmsLawExperiment.dat
new file mode 100644
index 0000000000000000000000000000000000000000..4e7425f7e300504a665c2ef05ae510e987530a99
--- /dev/null
+++ b/tools/OhmsLawExperiment.dat
@@ -0,0 +1,23 @@
+# Measurements: Ohm's Law
+#  Prec.: U: +\-0.10V, I: +\-0.10A, T: +/-0.10K
+# U[V]   I[A]  T[K]
+  0.50   0.50  293.5
+  1.00   0.89  293.8
+  1.50   1.41  295.4
+  2.00   1.67  296.8
+  2.50   2.30  299.4
+  3.00   2.59  301.0
+  3.50   2.77  303.0
+  4.00   3.57  307.4
+  4.50   3.94  310.9
+  5.00   4.24  315.1
+  5.50   4.73  318.0
+  6.00   4.87  323.2
+  6.50   5.35  327.4
+  7.00   5.74  333.7
+  7.50   5.77  338.2
+  8.00   6.17  343.1
+  8.50   6.32  350.0
+  9.00   6.83  354.7
+  9.50   6.87  358.6
+  10.00  7.17  367.9
\ No newline at end of file
diff --git a/tools/kafe2_example_MultiFit.ipynb b/tools/kafe2_example_MultiFit.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2af3a2c6b07b05853f4ad19f16826bcd98b21ae4
--- /dev/null
+++ b/tools/kafe2_example_MultiFit.ipynb
@@ -0,0 +1,444 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c2162620-c8d2-4bd6-a3ee-0b3212728d1c",
+   "metadata": {},
+   "source": [
+    "# Verwendung der `MultiFit`-Klasse von *kafe2*\n",
+    "\n",
+    "Das folgende, aus der Dokumentation von *kafe2* entnommene und weiter kommentierte, Code-Beispiel demonstriert die Verwendung der Klasse `MultiFit`aus dem *kafe2* Programm-Paket. Sie finden die Original-Dokumentation [hier](https://kafe2.readthedocs.io/en/latest/parts/beginners_guide.html#multifit).\n",
+    "\n",
+    "## Physikalische Problemstellung\n",
+    "\n",
+    "Das Beispiel basiert auf den Messpunkten der Datei `OhmsLawExperiment.dat`. Es geht um die Bestimmung des ohmschen Widerstands $R$ eines Drahts, aus einer Strom-Spannungs ($I(U)$)-Kennlinie. Die Darstellung von $I$ gegen $U$ fördert das überraschende Resultat zutage, dass zwischen $U$ und $I$ kein linearer Zusammenhang, wie er im Rahmen des ohmschen Gesetzes zu erwarten ist, besteht. Der Grund hierfür ist schnell gefunden: Er liegt in der Erwärmung des Drahts und der Temperaturabhängigkeit von $R$. \n",
+    "\n",
+    "**Die Daten bestehen aus drei Messgrößen, die korreliert geloggt wurden: $U$, $I$ und $T$.**\n",
+    "\n",
+    "## Statistische Problemstellung\n",
+    "\n",
+    "Auf Seiten der statistischen Analyse wird das Problem in diesem Beispiel durch eine Erweiterung des anzupassenden Modells gelöst. Dieses Modell hat die Form: \n",
+    "\n",
+    "$$\n",
+    "T(U) = p_{2}\\,U^{2} + p_{1}\\,U + p_{0};\n",
+    "\\qquad\n",
+    "I(U) = \\frac{U}{R_{0}\\bigl(1+\\alpha_{T}\\,T(U)\\bigr)}.\n",
+    "$$\n",
+    "\n",
+    "Bei $T(U)$ handelt es sich um einen rein phänomenologischen Ansatz. Offenbar macht es keinen Sinn $R$ ohne Berücksichtigung von $T$ anzugeben, daher bestimmen wir $R_{0}$ als den Widerstand bei der Temperatur $T=0^{\\circ}$ mit der Temperaturabhängigkeit\n",
+    "\n",
+    "$$\n",
+    "R(T) = R_{0}\\bigl(1+\\alpha_{T}\\,T(U)\\bigr).\n",
+    "$$\n",
+    "\n",
+    "Das gesamte Modell zur Beschreibung der Messreihe $\\{(U_{i},\\,I_{i},\\,T_{i})\\}$ hat mit $p_{0}$, $p_{1}$, $p_{2}$, $\\alpha_{T}$ und $R_{0}$ fünf Parameter. Diesen stehen 20 Messpunkte gegenüber. \n",
+    "\n",
+    "Mit den Mitteln einer einfachen Anpassung können wir das Modell für $T(U)$ oder aber das Modell $I(U)$ unabhängig an die Daten anpassen. **Wir wollen jedoch eine gemeinsame Anpassung beider Modelle an den gesamten korrelierten Datensatz vornehmen.** Daraus wollen wir alle fünf Parameter des Modells und insbesondere den uns v.a. interessierenden Parameter $R_{0}$ bestimmen. Die Antwort auf diese Problemstellung ist die `MultiFit`-Klasse aus dem Programm-Paket *kafe2*. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7a720752-a049-4a6c-960a-a074fd8e4292",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#########\n",
+      "# Fit 0 #\n",
+      "#########\n",
+      "\n",
+      "    ########\n",
+      "    # Data #\n",
+      "    ########\n",
+      "\n",
+      "        X Data  X Data Error  X Data Correlation Matrix                                    \n",
+      "        ======  ============  =============================================================\n",
+      "        0.5     0.2           [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        1.0     0.2           [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        1.5     0.2           [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        2.0     0.2           [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        2.5     0.2           [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        3.0     0.2           [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        3.5     0.2           [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        4.0     0.2           [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        4.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        5.0     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        5.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        6.0     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        6.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        7.0     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
+      "        7.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
+      "        8.0     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
+      "        8.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
+      "        9.0     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
+      "        9.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
+      "        10.0    0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+      "\n",
+      "        Y Data  Y Data Error  Y Data Correlation Matrix                                    \n",
+      "        ======  ============  =============================================================\n",
+      "        20.35   0.5           [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        20.65   0.5           [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        22.25   0.5           [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        23.65   0.5           [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        26.25   0.5           [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        27.85   0.5           [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        29.85   0.5           [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        34.25   0.5           [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        37.75   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        41.95   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        44.85   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        50.05   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        54.25   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        60.55   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
+      "        65.05   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
+      "        69.95   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
+      "        76.85   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
+      "        81.55   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
+      "        85.45   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
+      "        94.75   0.5           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+      "\n",
+      "    #########\n",
+      "    # Model #\n",
+      "    #########\n",
+      "\n",
+      "        Model Function\n",
+      "        ==============\n",
+      "\n",
+      "            empirical_T_U_model(U; p_2, p_1, p_0)\n",
+      "\n",
+      "        X Model\n",
+      "        =======\n",
+      "        0.5    \n",
+      "        1.0    \n",
+      "        1.5    \n",
+      "        2.0    \n",
+      "        2.5    \n",
+      "        3.0    \n",
+      "        3.5    \n",
+      "        4.0    \n",
+      "        4.5    \n",
+      "        5.0    \n",
+      "        5.5    \n",
+      "        6.0    \n",
+      "        6.5    \n",
+      "        7.0    \n",
+      "        7.5    \n",
+      "        8.0    \n",
+      "        8.5    \n",
+      "        9.0    \n",
+      "        9.5    \n",
+      "        10.0   \n",
+      "\n",
+      "        Y Model\n",
+      "        =======\n",
+      "        19.9   \n",
+      "        20.93  \n",
+      "        22.29  \n",
+      "        23.98  \n",
+      "        26.0   \n",
+      "        28.34  \n",
+      "        31.01  \n",
+      "        34.0   \n",
+      "        37.32  \n",
+      "        40.97  \n",
+      "        44.94  \n",
+      "        49.24  \n",
+      "        53.87  \n",
+      "        58.82  \n",
+      "        64.1   \n",
+      "        69.71  \n",
+      "        75.64  \n",
+      "        81.9   \n",
+      "        88.49  \n",
+      "        95.4   \n",
+      "\n",
+      "#########\n",
+      "# Fit 1 #\n",
+      "#########\n",
+      "\n",
+      "    ########\n",
+      "    # Data #\n",
+      "    ########\n",
+      "\n",
+      "        X Data  X Data Error  X Data Correlation Matrix                                    \n",
+      "        ======  ============  =============================================================\n",
+      "        0.5     0.2           [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        1.0     0.2           [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        1.5     0.2           [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        2.0     0.2           [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        2.5     0.2           [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        3.0     0.2           [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        3.5     0.2           [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        4.0     0.2           [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        4.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        5.0     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        5.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        6.0     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        6.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        7.0     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
+      "        7.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
+      "        8.0     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
+      "        8.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
+      "        9.0     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
+      "        9.5     0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
+      "        10.0    0.2           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+      "\n",
+      "        Y Data  Y Data Error  Y Data Correlation Matrix                                    \n",
+      "        ======  ============  =============================================================\n",
+      "        0.5     0.1           [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        0.89    0.1           [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        1.41    0.1           [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        1.67    0.1           [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        2.3     0.1           [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        2.59    0.1           [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        2.77    0.1           [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        3.57    0.1           [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        3.94    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        4.24    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        4.73    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        4.87    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        5.35    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "        5.74    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
+      "        5.77    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
+      "        6.17    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
+      "        6.32    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
+      "        6.83    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
+      "        6.87    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
+      "        7.17    0.1           [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
+      "\n",
+      "    #########\n",
+      "    # Model #\n",
+      "    #########\n",
+      "\n",
+      "        Model Function\n",
+      "        ==============\n",
+      "\n",
+      "            I_U_model(U; R_0, alpha, p_2, p_1, p_0)\n",
+      "\n",
+      "        X Model\n",
+      "        =======\n",
+      "        0.5    \n",
+      "        1.0    \n",
+      "        1.5    \n",
+      "        2.0    \n",
+      "        2.5    \n",
+      "        3.0    \n",
+      "        3.5    \n",
+      "        4.0    \n",
+      "        4.5    \n",
+      "        5.0    \n",
+      "        5.5    \n",
+      "        6.0    \n",
+      "        6.5    \n",
+      "        7.0    \n",
+      "        7.5    \n",
+      "        8.0    \n",
+      "        8.5    \n",
+      "        9.0    \n",
+      "        9.5    \n",
+      "        10.0   \n",
+      "\n",
+      "        Y Model\n",
+      "        =======\n",
+      "        0.4524 \n",
+      "        0.9016 \n",
+      "        1.346  \n",
+      "        1.784  \n",
+      "        2.215  \n",
+      "        2.637  \n",
+      "        3.049  \n",
+      "        3.45   \n",
+      "        3.839  \n",
+      "        4.215  \n",
+      "        4.578  \n",
+      "        4.926  \n",
+      "        5.26   \n",
+      "        5.579  \n",
+      "        5.882  \n",
+      "        6.17   \n",
+      "        6.443  \n",
+      "        6.699  \n",
+      "        6.941  \n",
+      "        7.167  \n",
+      "\n",
+      "###############\n",
+      "# Fit Results #\n",
+      "###############\n",
+      "\n",
+      "    Model Parameters\n",
+      "    ================\n",
+      "\n",
+      "        p_2 = 0.653 + 0.044 (up) - 0.043 (down)\n",
+      "        p_1 = 1.09 + 0.37 (up) - 0.37 (down)\n",
+      "        p_0 = 19.19 + 0.52 (up) - 0.52 (down)\n",
+      "        R_0 = 1.029 + 0.037 (up) - 0.037 (down)\n",
+      "        alpha = 0.00373 + 0.00068 (up) - 0.00062 (down)\n",
+      "\n",
+      "    Model Parameter Correlations\n",
+      "    ============================\n",
+      "\n",
+      "               p_2      p_1      p_0      R_0      alpha  \n",
+      "               =======  =======  =======  =======  =======\n",
+      "        p_2    1.0      -0.9579  0.7548   0.6512   -0.7159\n",
+      "        p_1    -0.9579  1.0      -0.8557  -0.6719  0.6836 \n",
+      "        p_0    0.7548   -0.8557  1.0      0.385    -0.3948\n",
+      "        R_0    0.6512   -0.6719  0.385    1.0      -0.9683\n",
+      "        alpha  -0.7159  0.6836   -0.3948  -0.9683  1.0    \n",
+      "\n",
+      "    Cost Function\n",
+      "    =============\n",
+      "\n",
+      "        Cost function: <no description provided>\n",
+      "\n",
+      "        chi2 / ndf = 19.83 / 35 = 0.5667\n",
+      "\n",
+      "        chi2 probability = 0.982\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from kafe2 import XYFit, MultiFit, Plot\n",
+    "\n",
+    "# ------------------------------------------------------------------------\n",
+    "# Daten:\n",
+    "# ------------------------------------------------------------------------\n",
+    "U, I, T = np.loadtxt('OhmsLawExperiment.dat', unpack=True)\n",
+    "# Konvertiere T von Kelvin nach Grad Celsius\n",
+    "T -= 273.15      \n",
+    "# Unsicherheiten\n",
+    "dU, dI, dT = 0.2, 0.1, 0.5\n",
+    "\n",
+    "# ------------------------------------------------------------------------\n",
+    "# Model:\n",
+    "# ------------------------------------------------------------------------\n",
+    "def empirical_T_U_model(U, p_2=1.0, p_1=1.0, p_0=0.0):\n",
+    "    return p_2*U**2+p_1*U+p_0\n",
+    "def I_U_model(U, R_0=1., alpha=0.004, p_2=1.0, p_1=1.0, p_0=0.0):\n",
+    "    _temperature = empirical_T_U_model(U, p_2, p_1, p_0)\n",
+    "    return U/(R_0*(1.0+_temperature*alpha))\n",
+    "\n",
+    "# ------------------------------------------------------------------------\n",
+    "# Setup für die Anpassung des Modells and die Daten:\n",
+    "# ------------------------------------------------------------------------\n",
+    "\n",
+    "# ------------------------------------------------------------------------\n",
+    "# Schritt 1: Definition einzelner XYFit Objekte (fit_0 und fit_1)\n",
+    " \n",
+    "fit_0 = XYFit(\n",
+    "    # Daten: U und T \n",
+    "    xy_data=[U, T],\n",
+    "    # Modell: empirical_T_U_model\n",
+    "    model_function=empirical_T_U_model\n",
+    ")\n",
+    "# Unsicherheiten auf T; Unsicherheiten auf U werden später eingeführt\n",
+    "fit_0.add_error(\n",
+    "    axis='y', \n",
+    "    err_val=dT\n",
+    ")  \n",
+    "# Label für den Datensatz\n",
+    "fit_0.data_container.label = \"Temperature data\"\n",
+    "# Labels für die Spalten des Datensatzes\n",
+    "fit_0.data_container.axis_labels = (\n",
+    "    \"Voltage (V)\", \n",
+    "    \"Temperature (°C)\"\n",
+    ")\n",
+    "# Label für das Modell\n",
+    "fit_0.model_label = \"Temperature parametrization\"\n",
+    "\n",
+    "fit_1 = XYFit(\n",
+    "    # Data: U und I\n",
+    "    xy_data=[U, I],\n",
+    "    # Modell: I_U_model\n",
+    "    model_function=I_U_model\n",
+    ")\n",
+    "# Unsicherheiten auf I; Unsicherheiten auf U werden später eingeführt\n",
+    "fit_1.add_error(\n",
+    "    axis='y', \n",
+    "    err_val=dI\n",
+    ")\n",
+    "# Label für den Datensatz\n",
+    "fit_1.data_container.label = \"Current data\"\n",
+    "# Labels für die Spalten des Datensatzes\n",
+    "fit_1.data_container.axis_labels = (\n",
+    "    \"Voltage (V)\", \n",
+    "    \"Current (A)\"\n",
+    ")\n",
+    "# Label für das Modell\n",
+    "fit_1.model_label = \"Temperature-dependent current\"\n",
+    "\n",
+    "# ------------------------------------------------------------------------\n",
+    "# Schritt 2: Definition des MultiFit Objekts aus fit_0 und fit_1\n",
+    "multi_fit = MultiFit(\n",
+    "    fit_list=[fit_0, fit_1], \n",
+    "    minimizer='iminuit'\n",
+    ")\n",
+    "# Unsicherheiten auf U, als gemeinsame Unsicherheiten für fit_0 und fit_1\n",
+    "multi_fit.add_error(\n",
+    "    axis='x', \n",
+    "    err_val=dU, \n",
+    "    fits='all'\n",
+    ")\n",
+    "# Setze Startwert für Parameter alpha (über multi_fit)\n",
+    "#multi_fit.set_parameter_values(alpha=0.004)\n",
+    "# Latex Name für einen Modellparameter (über multi_fit)\n",
+    "#multi_fit.assign_parameter_latex_names(alpha=r'\\alpha_\\mathrm{T}')\n",
+    "\n",
+    "# ------------------------------------------------------------------------\n",
+    "# Schritt 3: Anpassung und Diagnostik\n",
+    "multi_fit.do_fit()\n",
+    "multi_fit.report(asymmetric_parameter_errors=True)\n",
+    "\n",
+    "plot = Plot(multi_fit, separate_figures=True)\n",
+    "plot.plot(asymmetric_parameter_errors=True)\n",
+    "#plot.save()  \n",
+    "plot.show()"
+   ]
+  }
+ ],
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+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
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