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mooc-rr
Commit 3bde54b9 authored by db4d42e543ac9826803271b6e416a5b1's avatar db4d42e543ac9826803271b6e416a5b1
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moocrr-reproducibility-study

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module3/data_shuttle.csv 0 → 100644
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Date,Count,Temperature,Pressure,Malfunction
4/12/81,6,66,50,0
11/12/81,6,70,50,1
3/22/82,6,69,50,0
11/11/82,6,68,50,0
4/04/83,6,67,50,0
6/18/82,6,72,50,0
8/30/83,6,73,100,0
11/28/83,6,70,100,0
2/03/84,6,57,200,1
4/06/84,6,63,200,1
8/30/84,6,70,200,1
10/05/84,6,78,200,0
11/08/84,6,67,200,0
1/24/85,6,53,200,2
4/12/85,6,67,200,0
4/29/85,6,75,200,0
6/17/85,6,70,200,0
7/2903/85,6,81,200,0
8/27/85,6,76,200,0
10/03/85,6,79,200,0
10/30/85,6,75,200,2
11/26/85,6,76,200,0
1/12/86,6,58,200,1
module3/exo3.ipynb deleted 100644 → 0
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module3/moocrr-reproducibility-study.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.6.4 |Anaconda, Inc.| (default, Mar 13 2018, 01:15:57) \n",
"[GCC 7.2.0]\n",
"uname_result(system='Linux', node='98ca55aa610e', release='4.15.0-142-generic', version='#146~16.04.1-Ubuntu SMP Tue Apr 13 09:27:15 UTC 2021', machine='x86_64', processor='x86_64')\n",
"IPython 7.12.0\n",
"IPython.core.release 7.12.0\n",
"PIL 7.0.0\n",
"PIL.Image 7.0.0\n",
"PIL._version 7.0.0\n",
"_csv 1.0\n",
"_ctypes 1.1.0\n",
"_curses b'2.2'\n",
"decimal 1.70\n",
"argparse 1.1\n",
"backcall 0.1.0\n",
"cffi 1.13.2\n",
"csv 1.0\n",
"ctypes 1.1.0\n",
"cycler 0.10.0\n",
"dateutil 2.8.1\n",
"decimal 1.70\n",
"decorator 4.4.1\n",
"distutils 3.6.4\n",
"ipaddress 1.0\n",
"ipykernel 5.1.4\n",
"ipykernel._version 5.1.4\n",
"ipython_genutils 0.2.0\n",
"ipython_genutils._version 0.2.0\n",
"ipywidgets 7.2.1\n",
"ipywidgets._version 7.2.1\n",
"jedi 0.16.0\n",
"json 2.0.9\n",
"jupyter_client 6.0.0\n",
"jupyter_client._version 6.0.0\n",
"jupyter_core 4.6.3\n",
"jupyter_core.version 4.6.3\n",
"kiwisolver 1.1.0\n",
"logging 0.5.1.2\n",
"matplotlib 2.2.3\n",
"matplotlib.backends.backend_agg 2.2.3\n",
"numpy 1.15.2\n",
"numpy.core 1.15.2\n",
"numpy.core.multiarray 3.1\n",
"numpy.lib 1.15.2\n",
"numpy.linalg._umath_linalg b'0.1.5'\n",
"numpy.matlib 1.15.2\n",
"optparse 1.5.3\n",
"pandas 0.22.0\n",
"_libjson 1.33\n",
"parso 0.6.0\n",
"patsy 0.5.1\n",
"patsy.version 0.5.1\n",
"pexpect 4.8.0\n",
"pickleshare 0.7.5\n",
"platform 1.0.8\n",
"prompt_toolkit 3.0.3\n",
"ptyprocess 0.6.0\n",
"pygments 2.5.2\n",
"pyparsing 2.4.6\n",
"pytz 2019.3\n",
"re 2.2.1\n",
"scipy 1.1.0\n",
"scipy._lib.decorator 4.0.5\n",
"scipy._lib.six 1.2.0\n",
"scipy.fftpack._fftpack b'$Revision: $'\n",
"scipy.fftpack.convolve b'$Revision: $'\n",
"scipy.integrate._dop b'$Revision: $'\n",
"scipy.integrate._ode $Id$\n",
"scipy.integrate._odepack 1.9 \n",
"scipy.integrate._quadpack 1.13 \n",
"scipy.integrate.lsoda b'$Revision: $'\n",
"scipy.integrate.vode b'$Revision: $'\n",
"scipy.interpolate._fitpack 1.7 \n",
"scipy.interpolate.dfitpack b'$Revision: $'\n",
"scipy.linalg 0.4.9\n",
"scipy.linalg._fblas b'$Revision: $'\n",
"scipy.linalg._flapack b'$Revision: $'\n",
"scipy.linalg._flinalg b'$Revision: $'\n",
"scipy.ndimage 2.0\n",
"scipy.optimize._cobyla b'$Revision: $'\n",
"scipy.optimize._lbfgsb b'$Revision: $'\n",
"scipy.optimize._minpack 1.10 \n",
"scipy.optimize._nnls b'$Revision: $'\n",
"scipy.optimize._slsqp b'$Revision: $'\n",
"scipy.optimize.minpack2 b'$Revision: $'\n",
"scipy.signal.spline 0.2\n",
"scipy.sparse.linalg.eigen.arpack._arpack b'$Revision: $'\n",
"scipy.sparse.linalg.isolve._iterative b'$Revision: $'\n",
"scipy.special.specfun b'$Revision: $'\n",
"scipy.stats.mvn b'$Revision: $'\n",
"scipy.stats.statlib b'$Revision: $'\n",
"seaborn 0.8.1\n",
"seaborn.external.husl 2.1.0\n",
"seaborn.external.six 1.10.0\n",
"six 1.14.0\n",
"statsmodels 0.9.0\n",
"statsmodels.__init__ 0.9.0\n",
"traitlets 4.3.3\n",
"traitlets._version 4.3.3\n",
"urllib.request 3.6\n",
"zlib 1.0\n",
"zmq 17.1.2\n",
"zmq.sugar 17.1.2\n",
"zmq.sugar.version 17.1.2\n"
]
}
],
"source": [
"def print_imported_modules():\n",
" import sys\n",
" for name, val in sorted(sys.modules.items()):\n",
" if(hasattr(val, '__version__')): \n",
" print(val.__name__, val.__version__)\n",
"# else:\n",
"# print(val.__name__, \"(unknown version)\")\n",
"def print_sys_info():\n",
" import sys\n",
" import platform\n",
" print(sys.version)\n",
" print(platform.uname())\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm\n",
"import seaborn as sns\n",
"\n",
"print_sys_info()\n",
"print_imported_modules()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading and inspecting data"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>Date</th>\n",
" <th>Count</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Malfunction</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4/12/81</td>\n",
" <td>6</td>\n",
" <td>66</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11/12/81</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3/22/82</td>\n",
" <td>6</td>\n",
" <td>69</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11/11/82</td>\n",
" <td>6</td>\n",
" <td>68</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4/04/83</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6/18/82</td>\n",
" <td>6</td>\n",
" <td>72</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8/30/83</td>\n",
" <td>6</td>\n",
" <td>73</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11/28/83</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2/03/84</td>\n",
" <td>6</td>\n",
" <td>57</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4/06/84</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8/30/84</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>10/05/84</td>\n",
" <td>6</td>\n",
" <td>78</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11/08/84</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1/24/85</td>\n",
" <td>6</td>\n",
" <td>53</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>4/12/85</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>4/29/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6/17/85</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7/2903/85</td>\n",
" <td>6</td>\n",
" <td>81</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>8/27/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>10/03/85</td>\n",
" <td>6</td>\n",
" <td>79</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10/30/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>11/26/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1/12/86</td>\n",
" <td>6</td>\n",
" <td>58</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n",
"17 7/2903/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"data_shuttle.csv\")\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
"import matplotlib.pyplot as plt\n",
"\n",
"data[\"Frequency\"]=data.Malfunction/data.Count\n",
"data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Logistic regression\n",
"Let's assume O-rings independently fail with the same probability which solely depends on temperature. A logistic regression should allow us to estimate the influence of temperature."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 23</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 21</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -3.9210</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 14 Feb 2024</td> <th> Deviance: </th> <td> 3.0144</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:42:06</td> <th> Pearson chi2: </th> <td> 5.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 5.0850</td> <td> 7.477</td> <td> 0.680</td> <td> 0.496</td> <td> -9.570</td> <td> 19.740</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> -0.1156</td> <td> 0.115</td> <td> -1.004</td> <td> 0.316</td> <td> -0.341</td> <td> 0.110</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 23\n",
"Model: GLM Df Residuals: 21\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -3.9210\n",
"Date: Wed, 14 Feb 2024 Deviance: 3.0144\n",
"Time: 09:42:06 Pearson chi2: 5.00\n",
"No. Iterations: 6 Covariance Type: nonrobust\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n",
"Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n",
"===============================================================================\n",
"\"\"\""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statsmodels.api as sm\n",
"\n",
"data[\"Success\"]=data.Count-data.Malfunction\n",
"data[\"Intercept\"]=1\n",
"\n",
"logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n",
" family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n",
"logmodel.summary()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 23</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 21</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -23.526</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 14 Feb 2024</td> <th> Deviance: </th> <td> 18.086</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:42:25</td> <th> Pearson chi2: </th> <td> 30.0</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 5.0850</td> <td> 3.052</td> <td> 1.666</td> <td> 0.096</td> <td> -0.898</td> <td> 11.068</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> -0.1156</td> <td> 0.047</td> <td> -2.458</td> <td> 0.014</td> <td> -0.208</td> <td> -0.023</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 23\n",
"Model: GLM Df Residuals: 21\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -23.526\n",
"Date: Wed, 14 Feb 2024 Deviance: 18.086\n",
"Time: 09:42:25 Pearson chi2: 30.0\n",
"No. Iterations: 6 Covariance Type: nonrobust\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept 5.0850 3.052 1.666 0.096 -0.898 11.068\n",
"Temperature -0.1156 0.047 -2.458 0.014 -0.208 -0.023\n",
"===============================================================================\n",
"\"\"\""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n",
" family=sm.families.Binomial(sm.families.links.logit),\n",
" var_weights=data['Count']).fit()\n",
"\n",
"logmodel.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predicting failure probability\n",
"The temperature when launching the shuttle was 31°F. Let's try to estimate the failure probability for such temperature using our model.:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
"data_pred['Frequency'] = logmodel.predict(data_pred)\n",
"data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
"plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n",
"plt.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set(color_codes=True)\n",
"plt.xlim(30,90)\n",
"plt.ylim(0,1)\n",
"sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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