Small fix in hypotheses lesson

lessons/pydata/hypotheses/index.ipynb

@@ -71,7 +71,7 @@
  "\n",
  "Čím menší je p-value, tím menší je pravděpodobnost, že se zkoumaný jev (H0) v dané míře objeví jen náhodou. p-value u předchozího příkladu bude tedy daleko větší u datové sady s deseti záznamy, protože tam se vysoká korelace může daleko snáze objevit jen náhodou, zatímco u tisíce záznamů je pravděpodobnost takové náhody daleko menší.\n",
  "\n",
- "p-value nám hlavně pomůže rozhodnout, zda zamítneme nulovou hypotézu, či nikoli. K tomu si ale musíme pro p-value stanovit nějakou hranici a to ještě před provedením testu samotného. Této hranici se říká hladina významnosti a označuje se řeckým písmenem α. Nejběžněji používanou hladinou významnosti pro p-value je 0,05. Pokud bude výsledná p-value menší, efekt se v datech nevyskytuje náhodou a my můžeme zamítnout nulovou hypotézu a podpořit hypotézu alternativní. Pokud bude p-value naopak větší, je pravděpodobnější, že data daný jev opravdu reprezentují a nulová hypotéza zůstane platná.\n",
+ "p-value nám hlavně pomůže rozhodnout, zda zamítneme nulovou hypotézu, či nikoli. K tomu si ale musíme pro p-value stanovit nějakou hranici a to ještě před provedením testu samotného. Této hranici se říká hladina významnosti a označuje se řeckým písmenem α. Nejběžněji používanou hladinou významnosti pro p-value je 0,05. Pokud bude výsledná p-value menší, efekt se v datech může vyskytovat náhodou a my můžeme zamítnout nulovou hypotézu a podpořit hypotézu alternativní. Pokud bude p-value naopak větší, je pravděpodobnější, že data daný jev opravdu reprezentují a nulová hypotéza zůstane platná.\n",
  "\n",
  "Pozor na to, že zde není žádná šedá zóna. Jakmile si hladinu významnosti stanovíme, je výsledek buď černý nebo bíly, buď ano nebo ne. Nic mezi tím. Ani kdyby p-value vyšla jen o 0,01 vyšší než je naše α.\n",
  "\n",
@@ -94,7 +94,7 @@
  "source": [
  "from matplotlib import pyplot as plt\n",
  "import numpy as np\n",
- "from scipy.stats import shapiro, kstest\n",
+ "from scipy.stats import shapiro\n",
  "\n",
  "%matplotlib inline"
  ]
@@ -129,7 +129,7 @@
  "outputs": [
  {
  "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAANYElEQVR4nO3df6zd9V3H8edL6lRwEUjvam2LF5duhi3CyJWhUwOiE+iy4j8E4o86SWoMzGkW9TIT5z8z9ed0UTF1sHYRQYIgjcVJU38QE2Erk/FzSLMVuLXQThSnJJtlb/+4X/SsvZf745zT772fPR/JzTnnc358398Unv3e773nNFWFJKktX9f3AJKk0TPuktQg4y5JDTLuktQg4y5JDVrT9wAAa9eurcnJyb7HkKRV5aGHHvpCVU3Mdd+KiPvk5CQHDhzoewxJWlWSPDPffZ6WkaQGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGrYh3qEoLmZze29u2D+3Y0tu2peXyyF2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalBC8Y9yaYkf5fkiSSPJ3lft352kn1Jnu4uz+rWk+QjSQ4meSTJhePeCUnSV1vMkftx4P1VdR5wMXB9kvOAaWB/VW0G9ne3Aa4ANndf24GbRj61JOk1rVnoAVV1BDjSXf9ikieBDcBW4JLuYbuBvwd+uVv/eFUV8ECSM5Os715HWnUmp/f2st1DO7b0sl21YUnn3JNMAm8DHgTWDQT7eWBdd30D8NzA02a6tRNfa3uSA0kOHDt2bIljS5Jey6LjnuSbgb8Afr6q/nPwvu4ovZay4araWVVTVTU1MTGxlKdKkhawqLgn+Xpmw35rVd3VLb+QZH13/3rgaLd+GNg08PSN3Zok6RRZzG/LBLgZeLKqfnfgrj3Atu76NuCegfWf7H5r5mLgJc+3S9KpteAPVIF3AD8BPJrk4W7tA8AO4I4k1wHPAFd3990LXAkcBF4G3jPSiSVJC1rMb8v8I5B57r5sjscXcP2Qc0mShuA7VCWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQYv5xzqk/zM5vbfvESQtgkfuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDVow7kluSXI0yWMDa7+W5HCSh7uvKwfuuzHJwSRPJfmRcQ0uSZrfYo7cdwGXz7H+4aq6oPu6FyDJecA1wFu65/xRktNGNawkaXEWjHtV3Q+8uMjX2wrcXlVfqqrPAweBi4aYT5K0DMOcc78hySPdaZuzurUNwHMDj5np1k6SZHuSA0kOHDt2bIgxJEknWm7cbwLeCFwAHAF+Z6kvUFU7q2qqqqYmJiaWOYYkaS7LintVvVBVr1TVV4A/4f9PvRwGNg08dGO3Jkk6hZYV9yTrB27+KPDqb9LsAa5J8g1JzgU2A58cbkRJ0lKtWegBSW4DLgHWJpkBPghckuQCoIBDwM8AVNXjSe4AngCOA9dX1SvjGV2SNJ8F415V186xfPNrPP5DwIeGGUqSNBzfoSpJDTLuktSgBU/LSOrH5PTeXrZ7aMeWXrar0fLIXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIaZNwlqUHGXZIatGDck9yS5GiSxwbWzk6yL8nT3eVZ3XqSfCTJwSSPJLlwnMNLkua2mCP3XcDlJ6xNA/urajOwv7sNcAWwufvaDtw0mjElSUuxYNyr6n7gxROWtwK7u+u7gasG1j9esx4AzkyyflTDSpIWZ7nn3NdV1ZHu+vPAuu76BuC5gcfNdGsnSbI9yYEkB44dO7bMMSRJcxn6B6pVVUAt43k7q2qqqqYmJiaGHUOSNGC5cX/h1dMt3eXRbv0wsGngcRu7NUnSKbRmmc/bA2wDdnSX9wys35DkduDtwEsDp280IpPTe/seQdIKt2Dck9wGXAKsTTIDfJDZqN+R5DrgGeDq7uH3AlcCB4GXgfeMYWZJ0gIWjHtVXTvPXZfN8dgCrh92KEnScHyHqiQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ1yLhLUoPW9D2ApJVlcnpvb9s+tGNLb9tujUfuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktSgoT4VMskh4IvAK8DxqppKcjbw58AkcAi4uqr+fbgxJUlLMYoj90ur6oKqmupuTwP7q2ozsL+7LUk6hcZxWmYrsLu7vhu4agzbkCS9hmHjXsB9SR5Ksr1bW1dVR7rrzwPrhtyGJGmJhv2XmL6vqg4neQOwL8lnB++sqkpScz2x+8tgO8A555wz5BiSpEFDHblX1eHu8ihwN3AR8EKS9QDd5dF5nruzqqaqampiYmKYMSRJJ1h23JOckeT1r14H3gk8BuwBtnUP2wbcM+yQkqSlGea0zDrg7iSvvs6fVdUnknwKuCPJdcAzwNXDjylJWoplx72qPgecP8f6vwGXDTOUJGk4vkNVkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkhpk3CWpQcZdkho07Oe5f02bnN7b9wiSNCeP3CWpQR65S1ox+vpu+NCOLb1sd5w8cpekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQcZekBhl3SWqQH/kr6Wten//wzrg+btgjd0lq0Ko/cvefupOkk3nkLkkNMu6S1CDjLkkNMu6S1CDjLkkNMu6S1KCxxT3J5UmeSnIwyfS4tiNJOtlY4p7kNOAPgSuA84Brk5w3jm1Jkk42riP3i4CDVfW5qvoycDuwdUzbkiSdYFzvUN0APDdwewZ4++ADkmwHtnc3/yvJU4t43bXAF0Yy4crg/qxcLe0LuD8rVn5jqH359vnu6O3jB6pqJ7BzKc9JcqCqpsY00inn/qxcLe0LuD8r2bj2ZVynZQ4DmwZub+zWJEmnwLji/ilgc5Jzk7wOuAbYM6ZtSZJOMJbTMlV1PMkNwN8ApwG3VNXjI3jpJZ3GWQXcn5WrpX0B92clG8u+pKrG8bqSpB75DlVJapBxl6QGrZq4JzktyT8n+au+ZxlWkjOT3Jnks0meTPI9fc80jCS/kOTxJI8luS3JN/Y901IkuSXJ0SSPDaydnWRfkqe7y7P6nHEp5tmf3+r+e3skyd1JzuxzxsWaa18G7nt/kkqyto/ZlmO+/Uny3u7P5/EkvzmKba2auAPvA57se4gR+X3gE1X1ncD5rOL9SrIB+DlgqqreyuwP0K/pd6ol2wVcfsLaNLC/qjYD+7vbq8UuTt6ffcBbq+q7gH8BbjzVQy3TLk7eF5JsAt4JPHuqBxrSLk7YnySXMvsO/vOr6i3Ab49iQ6si7kk2AluAj/Y9y7CSfAvwA8DNAFX15ar6j36nGtoa4JuSrAFOB/6153mWpKruB148YXkrsLu7vhu46pQONYS59qeq7quq493NB5h978mKN8+fDcCHgV8CVtVvhMyzPz8L7KiqL3WPOTqKba2KuAO/x+wf5Ff6HmQEzgWOAR/rTjN9NMkZfQ+1XFV1mNkjjWeBI8BLVXVfv1ONxLqqOtJdfx5Y1+cwI/bTwF/3PcRyJdkKHK6qz/Q9y4i8Cfj+JA8m+Yck3z2KF13xcU/yLuBoVT3U9ywjsga4ELipqt4G/Der61v+r9Kdi97K7F9a3wackeTH+51qtGr294VX1RHifJL8CnAcuLXvWZYjyenAB4Bf7XuWEVoDnA1cDPwicEeSDPuiKz7uwDuAdyc5xOynS/5gkj/td6ShzAAzVfVgd/tOZmO/Wv0Q8PmqOlZV/wPcBXxvzzONwgtJ1gN0lyP5VrlPSX4KeBfwY7V63+DyRmYPJD7TNWEj8Okk39rrVMOZAe6qWZ9k9gzF0D8kXvFxr6obq2pjVU0y+4O6v62qVXtkWFXPA88leXO3dBnwRI8jDetZ4OIkp3dHG5exin9APGAPsK27vg24p8dZhpbkcmZPbb67ql7ue57lqqpHq+oNVTXZNWEGuLD7/2q1+kvgUoAkbwJexwg+8XLFx71R7wVuTfIIcAHw6z3Ps2zddyB3Ap8GHmX2v6lV9dbwJLcB/wS8OclMkuuAHcAPJ3ma2e9OdvQ541LMsz9/ALwe2Jfk4SR/3OuQizTPvqxa8+zPLcB3dL8eeTuwbRTfWfnxA5LUII/cJalBxl2SGmTcJalBxl2SGmTcJalBxl2SGmTcJalB/wspkBlaDAkBvAAAAABJRU5ErkJggg==\n",
+ "image/png": "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\n",
  "text/plain": [
  "<Figure size 432x288 with 1 Axes>"
  ]
@@ -152,7 +152,7 @@
  {
  "data": {
  "text/plain": [
- "(0.9978026151657104, 0.20951364934444427)"
+ "(0.9974613189697266, 0.12187974900007248)"
  ]
  },
  "execution_count": 4,
@@ -242,7 +242,7 @@
  " </thead>\n",
  " <tbody>\n",
  " <tr>\n",
- " <td>count</td>\n",
+ " <th>count</th>\n",
  " <td>1000.000000</td>\n",
  " <td>1000.000000</td>\n",
  " <td>1000.000000</td>\n",
@@ -251,7 +251,7 @@
  " <td>1000.000000</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>mean</td>\n",
+ " <th>mean</th>\n",
  " <td>499.500000</td>\n",
  " <td>41.155000</td>\n",
  " <td>4.944000</td>\n",
@@ -260,7 +260,7 @@
  " <td>175.388000</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>std</td>\n",
+ " <th>std</th>\n",
  " <td>288.819436</td>\n",
  " <td>13.462995</td>\n",
  " <td>2.013186</td>\n",
@@ -269,7 +269,7 @@
  " <td>96.778311</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>min</td>\n",
+ " <th>min</th>\n",
  " <td>0.000000</td>\n",
  " <td>18.000000</td>\n",
  " <td>0.000000</td>\n",
@@ -278,7 +278,7 @@
  " <td>3.000000</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>25%</td>\n",
+ " <th>25%</th>\n",
  " <td>249.750000</td>\n",
  " <td>30.000000</td>\n",
  " <td>4.000000</td>\n",
@@ -287,7 +287,7 @@
  " <td>101.000000</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>50%</td>\n",
+ " <th>50%</th>\n",
  " <td>499.500000</td>\n",
  " <td>41.000000</td>\n",
  " <td>5.000000</td>\n",
@@ -296,7 +296,7 @@
  " <td>173.000000</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>75%</td>\n",
+ " <th>75%</th>\n",
  " <td>749.250000</td>\n",
  " <td>53.000000</td>\n",
  " <td>6.000000</td>\n",
@@ -305,7 +305,7 @@
  " <td>248.000000</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>max</td>\n",
+ " <th>max</th>\n",
  " <td>999.000000</td>\n",
  " <td>64.000000</td>\n",
  " <td>10.000000</td>\n",
@@ -384,7 +384,7 @@
  " </thead>\n",
  " <tbody>\n",
  " <tr>\n",
- " <td>id</td>\n",
+ " <th>id</th>\n",
  " <td>1.000000</td>\n",
  " <td>-0.033595</td>\n",
  " <td>-0.004993</td>\n",
@@ -393,7 +393,7 @@
  " <td>0.000071</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>age</td>\n",
+ " <th>age</th>\n",
  " <td>-0.033595</td>\n",
  " <td>1.000000</td>\n",
  " <td>-0.014969</td>\n",
@@ -402,7 +402,7 @@
  " <td>-0.058026</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>healthy_eating</td>\n",
+ " <th>healthy_eating</th>\n",
  " <td>-0.004993</td>\n",
  " <td>-0.014969</td>\n",
  " <td>1.000000</td>\n",
@@ -411,7 +411,7 @@
  " <td>0.700612</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>active_lifestyle</td>\n",
+ " <th>active_lifestyle</th>\n",
  " <td>0.028897</td>\n",
  " <td>0.148267</td>\n",
  " <td>0.031613</td>\n",
@@ -420,7 +420,7 @@
  " <td>-0.225714</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>salary</td>\n",
+ " <th>salary</th>\n",
  " <td>-0.012048</td>\n",
  " <td>-0.072231</td>\n",
  " <td>0.858405</td>\n",
@@ -429,7 +429,7 @@
  " <td>0.799953</td>\n",
  " </tr>\n",
  " <tr>\n",
- " <td>months_of_exp</td>\n",
+ " <th>months_of_exp</th>\n",
  " <td>0.000071</td>\n",
  " <td>-0.058026</td>\n",
  " <td>0.700612</td>\n",