diff --git a/regresja liniowa/.ipynb_checkpoints/regresja_liniowa_NMI-checkpoint.ipynb b/regresja liniowa/.ipynb_checkpoints/regresja_liniowa_NMI-checkpoint.ipynb
index 85d259c..1d334d9 100644
--- a/regresja liniowa/.ipynb_checkpoints/regresja_liniowa_NMI-checkpoint.ipynb	
+++ b/regresja liniowa/.ipynb_checkpoints/regresja_liniowa_NMI-checkpoint.ipynb	
@@ -215,10 +215,340 @@
     "## Przykład kodu na regresję liniową"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1c1dd0af-74e4-4f7a-93bf-64a9d2be14c3",
+   "metadata": {},
+   "source": [
+    "Zaczynamy od importu pewnych bibliotek, które ułatwiają pracę z danymi i uruchamianie algorytmów uczenia maszynowego:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "74d886ec-b819-48fa-aac2-e69c6160287b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    " \n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e1ee83-3b9d-439f-9aee-5ac095ded83a",
+   "metadata": {},
+   "source": [
+    "Wczytajmy dane i podejrzyjmy nasz zbiór danych:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f1111528-20ce-4ed5-9c5a-511f2c1041d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cwiczenia</th>\n",
+       "      <th>czas_min</th>\n",
+       "      <th>wejscia</th>\n",
+       "      <th>nieodwiedzone</th>\n",
+       "      <th>czas_do_testu_godziny</th>\n",
+       "      <th>test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>20.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>36</td>\n",
+       "      <td>60.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>14</td>\n",
+       "      <td>48</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33</td>\n",
+       "      <td>92.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>37</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42</td>\n",
+       "      <td>65.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>16</td>\n",
+       "      <td>36</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>85.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>18</td>\n",
+       "      <td>55</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>98.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>20</td>\n",
+       "      <td>48</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>42</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>37</td>\n",
+       "      <td>70.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cwiczenia  czas_min  wejscia  nieodwiedzone  czas_do_testu_godziny    test\n",
+       "0          4        12        1              1                     20   20.00\n",
+       "1          8        25        3              3                     36   60.00\n",
+       "2         14        48        3              1                     33   92.86\n",
+       "3         11        37        5              0                     42   65.34\n",
+       "4          5        29        2              0                     22   42.35\n",
+       "5          2         5        1              4                      5   13.14\n",
+       "6         16        36        7              1                     47   85.13\n",
+       "7         18        55        5              0                     39   98.33\n",
+       "8         20        48        4              0                     45  100.00\n",
+       "9         10        42        7              1                     37   70.00"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('data.csv', sep=';')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2158a205-2c9e-41b9-9fe0-d56dd7b97213",
+   "metadata": {},
+   "source": [
+    "Teraz obliczamy podstawowy model regresji liniowej i wyliczamy błąd (czyli jak bardzo obliczone wartości funkcji regresji różnią się od tych rzeczywistych) - weźmy miarę RMSE (omówimy ją na późniejszych zajęciach - na razie zapamiętajmy: im niższy błąd tym lepiej):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "f7eade7c-def7-4944-8eea-7cba985e5bf5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Współczynnik a:  4.8869552414605435\n",
+      "Wyraz wolny:  11.935883392226131\n",
+      "Błąd: Root Mean Squared Error (RMSE): 8.27\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LinearRegression()\n",
+    " \n",
+    "X = data[['cwiczenia']]\n",
+    "y = data['test']\n",
+    " \n",
+    "model.fit(X,y, sample_weight=None)\n",
+    "y_pred = model.predict(X)\n",
+    " \n",
+    "print('Współczynnik a: ', model.coef_[0])\n",
+    "print('Wyraz wolny: ', model.intercept_)\n",
+    "print('Błąd: Root Mean Squared Error (RMSE): %.2f'% np.sqrt(mean_squared_error(y, y_pred)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04bf6923-da81-40ca-96bd-33f1ad973ddc",
+   "metadata": {},
+   "source": [
+    "A co by było jakbyśmy wzięli dodatkowo pod uwagę czas spędzony w kursie:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "2b232e81-9d0e-4817-ac0a-5f7b1d293666",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Współczynnik przy argumencie \"cwiczenia\":  2.793448759670846\n",
+      "Współczynnik przy argumencie \"czas_min\":  0.9017691542564438\n",
+      "Wyraz wolny:  4.156132897112705\n",
+      "Błąd: Root Mean Squared Error (RMSE): 5.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = data[['cwiczenia', 'czas_min']]\n",
+    "y = data['test']\n",
+    " \n",
+    "model.fit(X,y, sample_weight=None)\n",
+    "y_pred = model.predict(X)\n",
+    " \n",
+    "print('Współczynnik przy argumencie \"cwiczenia\": ', model.coef_[0])\n",
+    "print('Współczynnik przy argumencie \"czas_min\": ', model.coef_[1])\n",
+    "print('Wyraz wolny: ', model.intercept_)\n",
+    "print('Błąd: Root Mean Squared Error (RMSE): %.2f'% np.sqrt(mean_squared_error(y, y_pred)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49fa078-6216-4d83-bbbd-db0b58aa7c8e",
+   "metadata": {},
+   "source": [
+    "Mamy lepszy wynik - błąd jest niższy :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d501bcc0-d3bf-4552-bf2b-c78753b4d5d6",
+   "metadata": {},
+   "source": [
+    "A jak wyglądają nasze wyznaczone wartości funkcji regresji?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e539d645-86c6-42c0-9059-7a3c310cc2a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 26.15115779,  49.04795183,  86.54933494,  68.24952796,\n",
+       "        44.27468217,  14.25187619,  81.31500261, 104.03551406,\n",
+       "       103.31002749,  69.96492497])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_y_pred = data.copy()\n",
+    "data_with_y_pred['y_pred'] = y_pred\n",
+    "data_with_y_pred\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ea9c2cb-b654-4190-9e4f-972e4d3bf92f",
+   "metadata": {},
+   "source": [
+    "## Zadanie 1.\n",
+    "\n",
+    "Dla całego zbioru danych oblicz sumę kwadratów róźnic pomiędzy kolumnami **test** i **y_pred**. Wynik zaokrąglij do dwóch miejsc po przecinku (pamiętaj, że od 5 zaokrąglamy w górę). Podpowiedź:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90d8f306-e823-4552-8f41-6aac1ee37611",
+   "metadata": {},
+   "source": [
+    "- `data_with_y_pred['nazwa_kolumny'].size` - liczba elementów (wierszy) w danej kolumnie\n",
+    "\n",
+    "- `data_with_y_pred['nazwa_kolumny'][0]` - pierwszy (indeksujemy od zera) element (wiersz) kolumny"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d9c213b-5758-409b-a73d-1012a3709032",
+   "metadata": {},
+   "source": [
+    "Konkretne zadanie: napisz funkcję o nazwie `fun1()`, która zwróci oczekiwany wynik. Kod tej funkcji wklej na Moodle'u w aktywności o nazwie **Regresja liniowa - zadanie 1**. Na Moodle'u poza powyższym zbiorem danych kod będzie sprawdzony na innym zbiorze danych, więc nie można napisać prostej funkcji, która zwróci wartość obliczoną np. na kartce - niestety trzeba napisać funkcję obliczającą poszukiwaną wartość ;)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28e2ef8c-dbbf-4c3d-924e-f59318e4f383",
+   "id": "7d1a8edd-0a16-4504-8383-1e9240504306",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/regresja liniowa/regresja_liniowa_NMI.ipynb b/regresja liniowa/regresja_liniowa_NMI.ipynb
index 85d259c..1d334d9 100644
--- a/regresja liniowa/regresja_liniowa_NMI.ipynb	
+++ b/regresja liniowa/regresja_liniowa_NMI.ipynb	
@@ -215,10 +215,340 @@
     "## Przykład kodu na regresję liniową"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1c1dd0af-74e4-4f7a-93bf-64a9d2be14c3",
+   "metadata": {},
+   "source": [
+    "Zaczynamy od importu pewnych bibliotek, które ułatwiają pracę z danymi i uruchamianie algorytmów uczenia maszynowego:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "74d886ec-b819-48fa-aac2-e69c6160287b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    " \n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e1ee83-3b9d-439f-9aee-5ac095ded83a",
+   "metadata": {},
+   "source": [
+    "Wczytajmy dane i podejrzyjmy nasz zbiór danych:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f1111528-20ce-4ed5-9c5a-511f2c1041d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cwiczenia</th>\n",
+       "      <th>czas_min</th>\n",
+       "      <th>wejscia</th>\n",
+       "      <th>nieodwiedzone</th>\n",
+       "      <th>czas_do_testu_godziny</th>\n",
+       "      <th>test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>20.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>8</td>\n",
+       "      <td>25</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>36</td>\n",
+       "      <td>60.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>14</td>\n",
+       "      <td>48</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33</td>\n",
+       "      <td>92.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>37</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42</td>\n",
+       "      <td>65.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>42.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>16</td>\n",
+       "      <td>36</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>85.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>18</td>\n",
+       "      <td>55</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>98.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>20</td>\n",
+       "      <td>48</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>100.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>42</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>37</td>\n",
+       "      <td>70.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cwiczenia  czas_min  wejscia  nieodwiedzone  czas_do_testu_godziny    test\n",
+       "0          4        12        1              1                     20   20.00\n",
+       "1          8        25        3              3                     36   60.00\n",
+       "2         14        48        3              1                     33   92.86\n",
+       "3         11        37        5              0                     42   65.34\n",
+       "4          5        29        2              0                     22   42.35\n",
+       "5          2         5        1              4                      5   13.14\n",
+       "6         16        36        7              1                     47   85.13\n",
+       "7         18        55        5              0                     39   98.33\n",
+       "8         20        48        4              0                     45  100.00\n",
+       "9         10        42        7              1                     37   70.00"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('data.csv', sep=';')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2158a205-2c9e-41b9-9fe0-d56dd7b97213",
+   "metadata": {},
+   "source": [
+    "Teraz obliczamy podstawowy model regresji liniowej i wyliczamy błąd (czyli jak bardzo obliczone wartości funkcji regresji różnią się od tych rzeczywistych) - weźmy miarę RMSE (omówimy ją na późniejszych zajęciach - na razie zapamiętajmy: im niższy błąd tym lepiej):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "f7eade7c-def7-4944-8eea-7cba985e5bf5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Współczynnik a:  4.8869552414605435\n",
+      "Wyraz wolny:  11.935883392226131\n",
+      "Błąd: Root Mean Squared Error (RMSE): 8.27\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LinearRegression()\n",
+    " \n",
+    "X = data[['cwiczenia']]\n",
+    "y = data['test']\n",
+    " \n",
+    "model.fit(X,y, sample_weight=None)\n",
+    "y_pred = model.predict(X)\n",
+    " \n",
+    "print('Współczynnik a: ', model.coef_[0])\n",
+    "print('Wyraz wolny: ', model.intercept_)\n",
+    "print('Błąd: Root Mean Squared Error (RMSE): %.2f'% np.sqrt(mean_squared_error(y, y_pred)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04bf6923-da81-40ca-96bd-33f1ad973ddc",
+   "metadata": {},
+   "source": [
+    "A co by było jakbyśmy wzięli dodatkowo pod uwagę czas spędzony w kursie:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "2b232e81-9d0e-4817-ac0a-5f7b1d293666",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Współczynnik przy argumencie \"cwiczenia\":  2.793448759670846\n",
+      "Współczynnik przy argumencie \"czas_min\":  0.9017691542564438\n",
+      "Wyraz wolny:  4.156132897112705\n",
+      "Błąd: Root Mean Squared Error (RMSE): 5.19\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = data[['cwiczenia', 'czas_min']]\n",
+    "y = data['test']\n",
+    " \n",
+    "model.fit(X,y, sample_weight=None)\n",
+    "y_pred = model.predict(X)\n",
+    " \n",
+    "print('Współczynnik przy argumencie \"cwiczenia\": ', model.coef_[0])\n",
+    "print('Współczynnik przy argumencie \"czas_min\": ', model.coef_[1])\n",
+    "print('Wyraz wolny: ', model.intercept_)\n",
+    "print('Błąd: Root Mean Squared Error (RMSE): %.2f'% np.sqrt(mean_squared_error(y, y_pred)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49fa078-6216-4d83-bbbd-db0b58aa7c8e",
+   "metadata": {},
+   "source": [
+    "Mamy lepszy wynik - błąd jest niższy :)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d501bcc0-d3bf-4552-bf2b-c78753b4d5d6",
+   "metadata": {},
+   "source": [
+    "A jak wyglądają nasze wyznaczone wartości funkcji regresji?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e539d645-86c6-42c0-9059-7a3c310cc2a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 26.15115779,  49.04795183,  86.54933494,  68.24952796,\n",
+       "        44.27468217,  14.25187619,  81.31500261, 104.03551406,\n",
+       "       103.31002749,  69.96492497])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_y_pred = data.copy()\n",
+    "data_with_y_pred['y_pred'] = y_pred\n",
+    "data_with_y_pred\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ea9c2cb-b654-4190-9e4f-972e4d3bf92f",
+   "metadata": {},
+   "source": [
+    "## Zadanie 1.\n",
+    "\n",
+    "Dla całego zbioru danych oblicz sumę kwadratów róźnic pomiędzy kolumnami **test** i **y_pred**. Wynik zaokrąglij do dwóch miejsc po przecinku (pamiętaj, że od 5 zaokrąglamy w górę). Podpowiedź:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90d8f306-e823-4552-8f41-6aac1ee37611",
+   "metadata": {},
+   "source": [
+    "- `data_with_y_pred['nazwa_kolumny'].size` - liczba elementów (wierszy) w danej kolumnie\n",
+    "\n",
+    "- `data_with_y_pred['nazwa_kolumny'][0]` - pierwszy (indeksujemy od zera) element (wiersz) kolumny"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d9c213b-5758-409b-a73d-1012a3709032",
+   "metadata": {},
+   "source": [
+    "Konkretne zadanie: napisz funkcję o nazwie `fun1()`, która zwróci oczekiwany wynik. Kod tej funkcji wklej na Moodle'u w aktywności o nazwie **Regresja liniowa - zadanie 1**. Na Moodle'u poza powyższym zbiorem danych kod będzie sprawdzony na innym zbiorze danych, więc nie można napisać prostej funkcji, która zwróci wartość obliczoną np. na kartce - niestety trzeba napisać funkcję obliczającą poszukiwaną wartość ;)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28e2ef8c-dbbf-4c3d-924e-f59318e4f383",
+   "id": "7d1a8edd-0a16-4504-8383-1e9240504306",
    "metadata": {},
    "outputs": [],
    "source": []