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Small fix in hypotheses lesson
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‎lessons/pydata/hypotheses/index.ipynb‎

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@@ -71,7 +71,7 @@
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"\n",
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"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",
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"\n",
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"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",
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"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",
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"\n",
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"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",
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"\n",
@@ -94,7 +94,7 @@
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"source": [
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"from matplotlib import pyplot as plt\n",
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"import numpy as np\n",
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"from scipy.stats import shapiro, kstest\n",
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"from scipy.stats import shapiro\n",
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"\n",
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"%matplotlib inline"
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]
@@ -129,7 +129,7 @@
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"outputs": [
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{
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"data": {
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132+
"image/png": 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kDPjGvQe4Mcm9wBbg93quZ2TdO5JPAXcD9zH372pV3i6e5Cbgn4FXJzmU5ApgJ/BLSR5m7t3Kzj5rXI4FxvUnwMuAvUnuSfLnvRa5DAuMa9VbYFzXAz/WXTp5M7Bt2Lsuv6pAkhrlGbwkNcqAl6RGGfCS1CgDXpIaZcBLUqMMeElqlAEvSY36X6dkmRCj0iL1AAAAAElFTkSuQmCC\n",
133133
"text/plain": [
134134
"<Figure size 432x288 with 1 Axes>"
135135
]
@@ -152,7 +152,7 @@
152152
{
153153
"data": {
154154
"text/plain": [
155-
"(0.9978026151657104, 0.20951364934444427)"
155+
"(0.9974613189697266, 0.12187974900007248)"
156156
]
157157
},
158158
"execution_count": 4,
@@ -242,7 +242,7 @@
242242
" </thead>\n",
243243
" <tbody>\n",
244244
" <tr>\n",
245-
" <td>count</td>\n",
245+
" <th>count</th>\n",
246246
" <td>1000.000000</td>\n",
247247
" <td>1000.000000</td>\n",
248248
" <td>1000.000000</td>\n",
@@ -251,7 +251,7 @@
251251
" <td>1000.000000</td>\n",
252252
" </tr>\n",
253253
" <tr>\n",
254-
" <td>mean</td>\n",
254+
" <th>mean</th>\n",
255255
" <td>499.500000</td>\n",
256256
" <td>41.155000</td>\n",
257257
" <td>4.944000</td>\n",
@@ -260,7 +260,7 @@
260260
" <td>175.388000</td>\n",
261261
" </tr>\n",
262262
" <tr>\n",
263-
" <td>std</td>\n",
263+
" <th>std</th>\n",
264264
" <td>288.819436</td>\n",
265265
" <td>13.462995</td>\n",
266266
" <td>2.013186</td>\n",
@@ -269,7 +269,7 @@
269269
" <td>96.778311</td>\n",
270270
" </tr>\n",
271271
" <tr>\n",
272-
" <td>min</td>\n",
272+
" <th>min</th>\n",
273273
" <td>0.000000</td>\n",
274274
" <td>18.000000</td>\n",
275275
" <td>0.000000</td>\n",
@@ -278,7 +278,7 @@
278278
" <td>3.000000</td>\n",
279279
" </tr>\n",
280280
" <tr>\n",
281-
" <td>25%</td>\n",
281+
" <th>25%</th>\n",
282282
" <td>249.750000</td>\n",
283283
" <td>30.000000</td>\n",
284284
" <td>4.000000</td>\n",
@@ -287,7 +287,7 @@
287287
" <td>101.000000</td>\n",
288288
" </tr>\n",
289289
" <tr>\n",
290-
" <td>50%</td>\n",
290+
" <th>50%</th>\n",
291291
" <td>499.500000</td>\n",
292292
" <td>41.000000</td>\n",
293293
" <td>5.000000</td>\n",
@@ -296,7 +296,7 @@
296296
" <td>173.000000</td>\n",
297297
" </tr>\n",
298298
" <tr>\n",
299-
" <td>75%</td>\n",
299+
" <th>75%</th>\n",
300300
" <td>749.250000</td>\n",
301301
" <td>53.000000</td>\n",
302302
" <td>6.000000</td>\n",
@@ -305,7 +305,7 @@
305305
" <td>248.000000</td>\n",
306306
" </tr>\n",
307307
" <tr>\n",
308-
" <td>max</td>\n",
308+
" <th>max</th>\n",
309309
" <td>999.000000</td>\n",
310310
" <td>64.000000</td>\n",
311311
" <td>10.000000</td>\n",
@@ -384,7 +384,7 @@
384384
" </thead>\n",
385385
" <tbody>\n",
386386
" <tr>\n",
387-
" <td>id</td>\n",
387+
" <th>id</th>\n",
388388
" <td>1.000000</td>\n",
389389
" <td>-0.033595</td>\n",
390390
" <td>-0.004993</td>\n",
@@ -393,7 +393,7 @@
393393
" <td>0.000071</td>\n",
394394
" </tr>\n",
395395
" <tr>\n",
396-
" <td>age</td>\n",
396+
" <th>age</th>\n",
397397
" <td>-0.033595</td>\n",
398398
" <td>1.000000</td>\n",
399399
" <td>-0.014969</td>\n",
@@ -402,7 +402,7 @@
402402
" <td>-0.058026</td>\n",
403403
" </tr>\n",
404404
" <tr>\n",
405-
" <td>healthy_eating</td>\n",
405+
" <th>healthy_eating</th>\n",
406406
" <td>-0.004993</td>\n",
407407
" <td>-0.014969</td>\n",
408408
" <td>1.000000</td>\n",
@@ -411,7 +411,7 @@
411411
" <td>0.700612</td>\n",
412412
" </tr>\n",
413413
" <tr>\n",
414-
" <td>active_lifestyle</td>\n",
414+
" <th>active_lifestyle</th>\n",
415415
" <td>0.028897</td>\n",
416416
" <td>0.148267</td>\n",
417417
" <td>0.031613</td>\n",
@@ -420,7 +420,7 @@
420420
" <td>-0.225714</td>\n",
421421
" </tr>\n",
422422
" <tr>\n",
423-
" <td>salary</td>\n",
423+
" <th>salary</th>\n",
424424
" <td>-0.012048</td>\n",
425425
" <td>-0.072231</td>\n",
426426
" <td>0.858405</td>\n",
@@ -429,7 +429,7 @@
429429
" <td>0.799953</td>\n",
430430
" </tr>\n",
431431
" <tr>\n",
432-
" <td>months_of_exp</td>\n",
432+
" <th>months_of_exp</th>\n",
433433
" <td>0.000071</td>\n",
434434
" <td>-0.058026</td>\n",
435435
" <td>0.700612</td>\n",

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