diff --git a/Kennlinien_und_Fitting.ipynb b/Kennlinien_und_Fitting.ipynb
index f29b1006ea64e2794c7ea4aa7233a2921d020b07..7f6082b7bb36ac6295db46ec7778372ef2f2e021 100644
--- a/Kennlinien_und_Fitting.ipynb
+++ b/Kennlinien_und_Fitting.ipynb
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,6 +118,8 @@
     "            array = df.loc[:,['Q','n_rel','H']].to_numpy(float)\n",
     "            array = np.append(array,df_P.loc[:,['P']].to_numpy(float),axis=1)\n",
     "            continue\n",
+    "        #abschneiden der datanpaare, die keine korrespondierenden werte für H oder P haben\n",
+    "        #vielleicht dumm \n",
     "        if len(df.index)<len(df_P.index):\n",
     "            for i in range(len(df_P.index)-len(df.index)):\n",
     "                df_P.drop(len(df.index)+i,inplace=True)\n",
@@ -189,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -214,7 +216,19 @@
     }
    ],
    "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
     "df = pd.DataFrame(combine_csvs(),columns=['Q','n','H','P'])\n",
+    "X = np.empty((0,3),float)\n",
+    "for i in df.index:\n",
+    "    Q_temp=df.loc['Q'].get(i)\n",
+    "    n_temp=df.loc['n'].get(i)\n",
+    "    X = np.append(X,[[Q_temp**2,Q_temp*n_temp,n_temp**2], df.loc],axis=0)\n",
+    "\n",
+    "LR_H = LinearRegression(fit_intercept=False).fit(X , df.loc['H'].to_numpy(float))\n",
+    "\n",
     "print(df)"
    ]
   },