From bd72d97a56fe0a369e25fae3327718a9698340be Mon Sep 17 00:00:00 2001 From: Agathe Schmider Date: Wed, 15 Apr 2020 15:49:46 +0200 Subject: [PATCH] end exercice 2 --- .../challenger-checkpoint.ipynb | 598 +++++++++++++----- module4/challenger.ipynb | 235 +------ 2 files changed, 452 insertions(+), 381 deletions(-) diff --git a/module4/.ipynb_checkpoints/challenger-checkpoint.ipynb b/module4/.ipynb_checkpoints/challenger-checkpoint.ipynb index 1200c12..39938e1 100644 --- a/module4/.ipynb_checkpoints/challenger-checkpoint.ipynb +++ b/module4/.ipynb_checkpoints/challenger-checkpoint.ipynb @@ -50,12 +50,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "3.7.6 (default, Jan 8 2020, 19:59:22) \n", - "[GCC 7.3.0]\n", + "3.6.4 |Anaconda, Inc.| (default, Mar 13 2018, 01:15:57) \n", + "[GCC 7.2.0]\n", "uname_result(system='Linux', node='H2-SCHMIDER', release='5.3.0-46-generic', version='#38~18.04.1-Ubuntu SMP Tue Mar 31 04:17:56 UTC 2020', machine='x86_64', processor='x86_64')\n", "IPython 7.13.0\n", "IPython.core.release 7.13.0\n", - "html 6.0.3\n", "_csv 1.0\n", "_ctypes 1.1.0\n", "_curses b'2.2'\n", @@ -68,27 +67,34 @@ "dateutil 2.8.1\n", "decimal 1.70\n", "decorator 4.4.2\n", - "distutils 3.7.6\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 7.5.1\n", + "ipywidgets._version 7.5.1\n", "jedi 0.16.0\n", "json 2.0.9\n", "jupyter_client 6.1.2\n", "jupyter_client._version 6.1.2\n", "jupyter_core 4.6.3\n", "jupyter_core.version 4.6.3\n", - "kiwisolver 1.2.0\n", + "kiwisolver 1.1.0\n", "logging 0.5.1.2\n", - "matplotlib 3.2.1\n", - "matplotlib.backends.backend_agg 3.2.1\n", - "numpy 1.18.2\n", - "numpy.core 1.18.2\n", + "matplotlib 3.1.3\n", + "matplotlib.backends.backend_agg 3.1.3\n", + "mkl 2.3.0\n", + "numpy 1.18.1\n", + "numpy.core 1.18.1\n", "numpy.core._multiarray_umath 3.1\n", - "numpy.lib 1.18.2\n", + "numpy.lib 1.18.1\n", "numpy.linalg._umath_linalg b'0.1.5'\n", - "pandas 1.0.3\n", + "numpy.matlib 1.18.1\n", + "optparse 1.5.3\n", + "pandas 0.22.0\n", + "_libjson 1.33\n", "parso 0.6.2\n", "patsy 0.5.1\n", "patsy.version 0.5.1\n", @@ -101,10 +107,11 @@ "pyparsing 2.4.6\n", "pytz 2019.3\n", "re 2.2.1\n", - "scipy 1.4.1\n", - "scipy._lib._uarray 0.5.1+5.ga864a57.scipy\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", @@ -133,17 +140,15 @@ "seaborn 0.10.0\n", "seaborn.external.husl 2.1.0\n", "six 1.14.0\n", - "statsmodels 0.11.1\n", - "statsmodels.__init__ 0.11.1\n", - "statsmodels.api 0.11.1\n", - "statsmodels.tools.web 0.11.1\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 3.7\n", + "urllib.request 3.6\n", "zlib 1.0\n", - "zmq 18.1.1\n", - "zmq.sugar 18.1.1\n", - "zmq.sugar.version 18.1.1\n" + "zmq 17.1.2\n", + "zmq.sugar 17.1.2\n", + "zmq.sugar.version 17.1.2\n" ] } ], @@ -473,7 +478,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 21Dep. Variable: Frequency No. Observations: 23
Model Family: Binomial Df Model: 1Model: GLM Df Residuals: 21
Link Function: logit Scale: 1.0000Model Family: Binomial Df Model: 1
Method: IRLS Log-Likelihood: -3.9210Link Function: logit Scale: 1.0000
Date: Tue, 14 Apr 2020 Deviance: 3.0144Method: IRLS Log-Likelihood: -3.9210
Time: 10:27:51 Pearson chi2: 5.00Date: Wed, 15 Apr 2020 Deviance: 3.0144
No. Iterations: 6 Time: 15:20:47 Pearson chi2: 5.00
Covariance Type: nonrobust No. Iterations: 6 Covariance Type: nonrobust
\n", "\n", @@ -572,10 +565,9 @@ "Model Family: Binomial Df Model: 1\n", "Link Function: logit Scale: 1.0000\n", "Method: IRLS Log-Likelihood: -3.9210\n", - "Date: Tue, 14 Apr 2020 Deviance: 3.0144\n", - "Time: 10:27:51 Pearson chi2: 5.00\n", - "No. Iterations: 6 \n", - "Covariance Type: nonrobust \n", + "Date: Wed, 15 Apr 2020 Deviance: 3.0144\n", + "Time: 15:20:47 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", @@ -614,46 +606,34 @@ "execution_count": 6, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/aschmide/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: DeprecationWarning: Calling Family(..) with a link class as argument is deprecated.\n", - "Use an instance of a link class instead.\n", - " \n" - ] - }, { "data": { "text/html": [ "
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Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 21Dep. Variable: Frequency No. Observations: 23
Model Family: Binomial Df Model: 1Model: GLM Df Residuals: 21
Link Function: logit Scale: 1.0000Model Family: Binomial Df Model: 1
Method: IRLS Log-Likelihood: -23.526Link Function: logit Scale: 1.0000
Date: Tue, 14 Apr 2020 Deviance: 18.086Method: IRLS Log-Likelihood: -23.526
Time: 10:27:51 Pearson chi2: 30.0Date: Wed, 15 Apr 2020 Deviance: 18.086
No. Iterations: 6 Time: 15:21:05 Pearson chi2: 30.0
Covariance Type: nonrobust No. Iterations: 6 Covariance Type: nonrobust
\n", "\n", @@ -678,10 +658,9 @@ "Model Family: Binomial Df Model: 1\n", "Link Function: logit Scale: 1.0000\n", "Method: IRLS Log-Likelihood: -23.526\n", - "Date: Tue, 14 Apr 2020 Deviance: 18.086\n", - "Time: 10:27:51 Pearson chi2: 30.0\n", - "No. Iterations: 6 \n", - "Covariance Type: nonrobust \n", + "Date: Wed, 15 Apr 2020 Deviance: 18.086\n", + "Time: 15:21:05 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", @@ -729,7 +708,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -780,55 +759,6 @@ "plt.grid(True)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There were warnings during the construction of the log model. let's try and change it" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "ename": "DistributionNotFound", - "evalue": "The 'statsmodel==0.9.0' distribution was not found and is required by the application", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mDistributionNotFound\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpkg_resources\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpkg_resources\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"statsmodel==0.9.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstatsmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/pkg_resources/__init__.py\u001b[0m in \u001b[0;36mrequire\u001b[0;34m(self, *requirements)\u001b[0m\n\u001b[1;32m 899\u001b[0m \u001b[0mincluded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meven\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mwere\u001b[0m \u001b[0malready\u001b[0m \u001b[0mactivated\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mworking\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 900\u001b[0m \"\"\"\n\u001b[0;32m--> 901\u001b[0;31m \u001b[0mneeded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparse_requirements\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequirements\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 902\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 903\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdist\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneeded\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/pkg_resources/__init__.py\u001b[0m in \u001b[0;36mresolve\u001b[0;34m(self, requirements, env, installer, replace_conflicting, extras)\u001b[0m\n\u001b[1;32m 785\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdist\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[0mrequirers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequired_by\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mDistributionNotFound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequirers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 788\u001b[0m \u001b[0mto_activate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 789\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdist\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDistributionNotFound\u001b[0m: The 'statsmodel==0.9.0' distribution was not found and is required by the application" - ] - } - ], - "source": [ - "import pkg_resources\n", - "pkg_resources.require(\"statsmodel==0.9.0\")\n", - "import statsmodel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "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()\n" - ] - }, { "cell_type": "markdown", "metadata": { @@ -859,9 +789,17 @@ "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aschmide/miniconda3/envs/envStats9/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": { - "image/png": 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NXV0KUdgLKEAURYGiKHC5XDCZ2v+NmZSUhKysLBQUFODaa69FQUEBsrKy2nRfZWZmYtu2bf7Hy5cvR3NzMx5++OF2vx71bE6XiiiLBkXuXmchROEm4O+wW2+9FQ888AC2b9+O0tJSlJWV+f8EYuHChVixYgVyc3OxYsUKLFq0CAAwe/Zs7NmzR1/1RGegCQG709vVZRCFPUmInx/3WFVVhZSUFAwdOtS3gySh9S6SJGH//v2dW2U78BpI6OqIayBV9Q5omoAsSUiMs8Agd4+L6XzvQls4t+98roGc8wykZf2PAwcO4MCBA8jJyfH/+8CBA90qPIhaaEKg0e5GeP4qQdQ9nDNATj1B2bFjR6cVQ9SRXB4VDhe7sog6yzkD5NTx9Ofo8SLqVpqaPfCGaZcmUVc75ygsVVXxzTff+IPj1McA8Mtf/rLzKiQ6Dy1dWfExZnSPqyFE4eOcAZKUlIT58+f7H8fHx7d5LEkSPv/8886pjqgDuDwqml1eRJkDvm+WiAJwzu+oL774Ihh1EHUqe7MHZqPSbUZlEYUD/kpGIa+wuBprt5XC6VYRZTFgzNBUDOmb0GYbTQg02N1IjDEB7MwKWS3vdXW9E8lxFuRl98WIQcldXVaPxVt1KaQVFldj5WeHUGd3I8KioMHhweotJThYWnvath6PCrdH64IqqSO0fq8jLQbU2d1Y+dkhFBZXd3VpPRYDhELa2m2lUBQZZqMCSZJgMihQFBmbdp84bVsBoNHhCX6R1CFOfa/NRt97vXZbaVeX1mMxQCikVdc7YTK0/RgbFRm1jWde6Mzj1bhmSIg603ttMsiornd2UUXEAKGQlhxngdvbtlvKo2pIiDGfdR+704tuuFwIncOZ3mu3V0NynKWLKiIGCIW0vOy+UFXfWYUQAm6vClXVcOXIzLPu4/GqcPFaSMg59b12eXzvdV72uVdFpc7BUVgU0lpG4KzdVgqHU0VshBE5l/Q6bRRWa0IATrcXJgPXDAklrd9rjsLqHhggFPJGDErGiEHJbWbjPReHy4tIsxEGhX1ZoaTlvabugV1Y1CMJATQ2u7u6DKKQxgChHsvlUdHs5my9RHoxQKhHa2r2QNV4QZ1IDwYI9WiaJtBg93DhKSIdGCDU43HhKSJ9GCBEAJoc7Moiai8GCBF8XVmN7MoiahcGCNFJzpMLTxFRYBggRK00NbvhUdmVRRQIBgiFBVu9Ext2lqHpPKdrFwJosLsh2JlFdE6cyoTCwuotJdhUaEVCjBn/ljcUqQkRuo/l8WpocngRG2mEYI4QnRXPQCgsXDE8Ayajbx2Q1z7aix+P1Z/X8ZqdHjjdXDeE6OcwQCgsDO4Tj4dnXYqYSCOcbhVvfbof24oqdB9PCKDR7oYawMSMRD0VA4TCRv/0WMy5fjgykiKhCeCjzSVY8/UR3SHg1YTveggzhOiMGCAUVuKjzbhr2sXI6udbD2Tr3nL8/dMDuu80d3lUNDrcXMGQ6AwYIBR2zEYFN18zGFedXJXwx+P1eOWDvaioadZ1PIfTy/tDiM6AAUJhSZYl5GX3xY1XXwCDIsHW4MSrH+3F3pKadh9LAGhs9sDL6yFEbTBAKKyNujAZd027GHFRJrg9Gv7ns0NYt700oFULW9M0gfomFzReECHyY4BQ2OudEo051w/HgIxYAMCX35/A3z7d3+6bDj1eDQ3Nbt5iSHQSA4R6hOgII26fkoVxwzMAAMXHG/DS+3twtLyxXcdxutSTd6oTUdACpKSkBDNnzkRubi5mzpyJI0eOnLbNyy+/jClTpmDq1Km4/vrrsWnTpmCVRz2AIkuY/Mt++M3EC2Eyymiwu/HGmiJ8tftEu7qmHC4vh/cSIYgBsmDBAsyaNQvr1q3DrFmz8Pjjj5+2zYgRI/CPf/wDa9aswZIlS/Dggw/C6XQGq0TqIYYPTMKc64YjLSECmhBYu60U/732YLu6tBwuL+qbXZ1YJVH3F5QAsdlsKCoqQn5+PgAgPz8fRUVFqKlpOyLmyiuvRESEbw6jIUOGQAiBurq6YJRIPUxKfATuvm4YxgxJAQAcLKvDS+8VovhE4FOgOF0q6u3uziqRqNsLSoBYrVakpaVBURQAgKIoSE1NhdVqPes+H374Ifr27Yv09PRglEg9kMmg4Prxg3BjzgW+Lq1mD/5asB/rtpfCG+CU7g6XF40OD8CrItQDdcvZeLdv345ly5bhr3/9a7v3TUqK7oSKuo+UlJiuLqFTnU/7VE3AK0vtvjaRc1kUhl2Ygr+s3oej1gZ8+f0JlFgbcfu0i5GeFBXQMQxmA+JjzJB+5pZ1vnehLdzbp0dQAiQjIwMVFRVQVRWKokBVVVRWViIjI+O0bXft2oU//OEPeOWVVzBw4MB2v5bN1tTuMf6hIiUlBlVV7Rs1FErOt31CALX1Dl3vvwHAnVOGYsPOY/jq+xMorWjEk3/dhrzsvvjFxemQzzGXSQ0AW40BsVFGSDh9W753oS2c2yfLku5fvIPShZWUlISsrCwUFBQAAAoKCpCVlYXExMQ22xUWFuLBBx/Eiy++iIsvvjgYpRH5KbKM3Mv64s6pFyE+2gSvKlDw9VH89eP9qG0892AOh8uL2kY33F4N7NKinkASIjiDEYuLizFv3jw0NDQgNjYWS5cuxcCBAzF79mzMnTsXw4cPxw033IDjx48jLS3Nv98zzzyDIUOGBPw6PAMJXR1xBlKl8wzkVE63Fx9/fRTfHqoCAJiMMvKy++KyrLRzno1IEmA0KIi2GGE2yRCC712oC+f2nc8ZSNACJFgYIKGrOwVIiw07y/Dl9yf8U8KnJUTg8mHp2P1jNWobXUiIMePKkZkY0jfhtH0lCTAbFERGGNErI0532wqLq7F2Wymq651IjrMgL7svRgxKPq92dZTVmw9j/Y5jcHpUWIwKJo3tjWnj2t/13N2F8/det+/CIgpFB0trseuHKsRFmxBh9o0grKh14INNJSivdcBsUtDg8GD1lhIcLK09bX8hAKdHRW2jE7Z6p655tAqLq7Hys0Oos7sRaTGgzu7Gys8OobC4+rzbd75Wbz6M1V8fgcujwiD7pr5f/fURrN58uKtLoyBhgBCdxabdJ6AoMiwmAxJiLEiMNfu/1uz0orreCQhAUWRs2n3irMcRwtclVlPvhMPtbdfVkbXbSqEoMsxGBZIkwWxUoCgy1m4rPY+WdYz1O45BggRFliBJsu9vSFi/41hXl0ZBwgAhOovaRheMyk/fIhaToc34Kq8qUF3vhN3hga3h3BfZvZpAfZMbNQ0OOD2BrbdeXe+EydD229RkkH3h1cWcbi/kUy4HyZLveeoZGCBEZ5EQY4bnlBsKDYoEoyIhOc7iDxeHS0VjswffFJUHdP3F4xWoa3ShptEFt0f92TOS5DjLyVFdP3F7NSTHWdrdno5mMRlwanM14XueegYGCNFZXDkyE6qqwe1VIYSA26vCZFRgMhkACUiKMyMqwndWoglg9eYjePmDPTh8oiGg47s9KmoaXbA1OGB3eqBq2mlL5+Zl94WqanB5fDW4PCpUVUNedt+Ob3A7TRrbGwICqiYghOb7GwKTxvbu6tIoSJSFCxcu7OoiOpLDEb6zpEZFmdHcHL5zL3VE+5pd3g57/5PjIpAcZ0GFrRlNzR7ER5nw61/0w0X9E3zPOTxIjrPgmrF9YDEZYD353HeHqlBua0av5ChEWny/jUdEmOA4y2SNmga4PRqcLhVeVUCWJfhObiSkJUYiLSECxyqbUG93IzHGjOuvGtgtRmEN6ZsACIGj5U1wqwIWo4LJv+gblqOwwvl7T5IkREaa9O3LYbyhI5yHEgLdcxhvexyrbELB1iMorWgC4Js+PvuiNFx9aS/0yYxHTY09oOO03EcSaTHAbJTPeGd7d8PPZug6n2G87Kwk6iC9U6Px22kXo7DYhnXbS1HX5MbXe8vx7cEq5P6iHy65IAlmo3LO4wjh695ye1TIsgSzQYHZrMBkkM95EyNRMDFAiDqQJEkYeUEyLuqfiK37yrFx13E43SpWbzqMz3eW4VejMnFZVhqMhsAuP2qagMPthcPthSxLiDAbEGFWYFTksO2qpdDBACHqBEaDjKtGZmLMkFR8tfs4tu6rgN3hwcdbj2JToRW/GpWJMUNTYVACH8eiaQJ2hwfNTg8MBt+9ISaDctowX6Jg4TWQEBLO/bBA6F8D+TmSUcEHX/yAbw9W+adFiY0y4aqRGRgzNBUmw7m7ts54XAlQFAkRJgPMRqVdgdSR+NkMXbwGQtTNJcRYMP3KgRg/KhP/3HUC3x2sQoPdjYKvj+Kfu07gimHpyL4oDRHm9n1LCgF4vQKNXg+aJA8UWYLZZIDZoMBklCFJYFcXdRoGCFEQJcRYcP1VA3H1JZn48vsT+PZgFewOD9bv8E3aOHZoKi4fno74aPO5D3YKIXx3x3sdHtjhgSxLMBlkmEwKTIoMRZFDYDwXhRIGCFEXaDkjybm0N7bssWLb/gq4PCo277Hi671WDBuYhCuGp6NPqv5V8DRNwOlW4XSrkCRAliQYDTIMBhkGRYZB8c1jJUvtX8WRCGCAEHWp2JM3J/7qkl7YVlSBrXvL0ejwoLDYhsJiG3qnROGXw9IxfGDSeV3fEAJQhYDqVgG3bx4uSfKNGpNlwCDLvrvsDb4zFZldXxQAXkQPIeF8IQ8I74voiYlRAd1I6FU1FBbbsGWPFVZbs//5SIsBY4akYGxWGpJiO28eLEkCFEmCQZGhGE6epUgyJNl3Y6RyljXn+dkMXbyIThQmDIqMSwen4JILk3GkvBFb95WjqKQGzU4vvtptxVe7rRjUKxZjhqTiov6JAd9PEighAK8Q8Goq0GrG4JazFYMiw2yUYVQUKCe7wKjnYoAQdUOSJGFARiwGZMSiwe7GjgOV2HmgEvV2N4qPN6D4eAMsJgUjL0jGpYOT0TslGlIn3qUuBHwTSmq+O+QBD+ST3V8mgwJLpG8teEX2rS3P0V89AwOEqJuLjTJhwujeuPqSXvjhWB12HKjEgaN1cLpVbCuqwLaiCiTHWTDygmSMvCAJyXERQalLEwKaCnhVLxqa3ahpcP50sf5kF1jLmYsiSzDIEmRZ8l13YcCEBQYIUYiQZQlD+iZgSN8ENDa7sftHm2/m35pmVNc78fm3x/D5t8fQKzkKwwclYfjARCTEBHfdEP/F+lO6wICfusEAX1ed0eD7I58MlJagaX0i1TpkWp7XBBhA3QQDhCgExUSaMG5EBsaNyIDVZsf3P1Rjd7ENDXY3jlfbcbzajrXbStErOQoXD0jERf0TkZoQnDOTs2npBgPQqivMR5Lgm3VYQpswkX1fAIRvRUdN0yAEIJ0cOaYosj90WgLFNxBAhiz7QleWJHapdRIGCFGIy0iKQkZSFHKz++JoeSMKi23YV1KDJofHHybrd5QhOc6CrH4JGNovAX3TYrrVBXAhAAEBCMC3/qLAmVdPOUkDvFABnH1p4JYzHgm+desNsi+M/OEkS5DR8hz8C4MJCEjCdwBFAiRZgqaJ0xb78gWSAHrw7ZkcxhtCwnkoIcBhvB1J0wSOlDdib4kN+4/Uot7edjGkCLOCC3vHY3CfeFzQOw6xOhcUahHs9nUkyf+fU7rMTv5HgoSExEjU1zWffOyLDQgJibHmkJ9in8N4iagNWZYwMDMWAzNjMfXy/jhebcf+I7U4UFoLq60ZDpfqv1kRANITI3FB7zgMyoxF/4zYgNYtCRf+E4mzPC8g/NPEtNaNTuC6DAOEKMxJkoTeKdHonRKNa8b2QV2TCwdL6/DDsTr8eLwebo+G8ppmlNc0Y3OhFbIkoXdqFAZmxmFARgz6psbAbOo5gUKBY4AQ9TDx0WZkX5SG7IvS4FU1lFU24Ydj9Sg+Xo9jVU3QhEBpRRNKK5qwcZfvN+2M5Cj0S4tBv/QY9E2LQVzU+XV5UXhggBD1YAZF9t+wiLF94HR7UWJtxOET9SixNsJqs0MTwPEqO45X2fH13nIAvntT+qREo09qNHqlRiEiqv2zB1PoY4BQ+JCAaIvhpyE1OHlRVIgzdXG33u3kP6SfHkttv3byMBBCnHE4qKYKeDQVQvNtp4Xo2BSLyYCsfgnI6pcAAHC6vTha3uj7U9GIY5V2eFQNDXY39ro0cfYAABGfSURBVNlrsO9Izck99yM5zoLM5ChkJkUhIzkSGUlRiI4wdl1jqNMxQChsSAAiLV33A6vlXgNVE/CoGjRNQBMCqle0zrSzjvrsjpljMRn8Ny8CgKppKLc1o7SyCccqm1BW2QRbvRMCQHW9E9X1Tv+FeQCIiTAiPSkSaYmRSEuIQFpCJFISInrURfpwxgAh6iAtAeCbtbbtD8jEhEiIkzfOydJPd1W37CMACA1QhQa3W4PLq571bKcrKbKMXinR6JUSDVzse87p9qLRpeFASTWs1c04Xm1Hdb0DQgCNDg8aj9Xjh2P1bY4TH21CSnwEUuIjkBxvQXJcBJLjLIiNMoX8sNiehAFCFASKIsN0rplzFQCQEWFq6QbT4FEFHE4v3F6124VJC4vJgMz0KKTE/HRh3ePVUFHbjHKbb3RXRW0zymscsDt8twfWNblR1+Q+LViMiozEWDMSYy1IirUgIdaMxBgzEmItSIg2d/jsw3R+GCBE3VDLdByKDESYFHhVDU63CofTC7UbnpmcymiQ/UOHW7M7PaisdaCy1oHqOgeq6h2oqnOirtEFAcCjaqiodaCi1nHG40ZHGBEfbUJ8jBnxUWbERZsQF21GXJQJcVEmREcYIfMGjaBhgBB1c0L4uo6iLDIiLUZoqua7zqIJeNwq3Cevt4SCKIsRAzKMvlFfrXi8GmoanbDVO2Fr8P1d2+hCTYMLdU0uqCfb1+TwoMnhwbGqM9/1Lkm+6y4xUSbERJgQE2k8+ccXLjGRRkRHGBEVYYTJIHfqFPg9AQOEKIS0zOukKIAJgGQxQNUEvF4Bj+YLEqEJaJqAV9N8czuFyBlLWkIk0hIiT/uapgk0NLtR2+hCbaMvUOqa3KhvcqHe7kZ9kxuuk9eXhAAamj1oaPYA+PmpVYyKjEiLAVERRkRZDIiyGBFhMSDSbEBkq78jzAaosgy3ywuzSeE1mlYYIEQhTAjf+hsmowQTfro+0PIzTtWEbwoOIXwrDXo0eFQVqhYawQL4pmWJjzYjPtqMARln3sbp9qLB7kGD3Y2GZjcam91osHvQ6HCjsdmDpmbfv90ezb+PR9V8AXTKPGE/RwJgNimwmBREWYy4bvxAXHphynm2MHQFLUBKSkowb9481NXVIT4+HkuXLkX//v3bbKOqKp588kls2rQJkiThrrvuwowZM4JVIlHYaAkGWZJgMkgoLK7Guu2lqG9yIy0xEhNG98bgPvH4x8YfsedwDTyqBlUVGNonDhcNSMLmwhOw1TsRG2XClSMzMaRvAg6W1mLT7hOobXQhIcaMK0dm4nhVEzYXlsPlVWE2KBg3Ih05o/ucsaYz7X+247YMGw70GF/vsfrq8KgwG311TL2i/2n7u70q9vxow9Z95ai3uxFpNqBvWjScHhVHyxvhcquQZQlmowKvJuB0edG6d1AAcLpVON0q6prc+NvH+7EhtQx52X0xYlDyeb5roSdos/HeeuutuOGGG3Dttdfio48+wnvvvYe33367zTYffvgh1qxZgzfeeAN1dXWYPn06/ud//ge9e/cO+HU4G2/oCuf2dWXbCoursfKzQ/6RYG6vBlXVkBRjxoGyesjyyRUElZNrnhskJMVHwGI0wKsJeFUVw/onYteP1RAAFEmCy6uiockFh9t356QE35mOKoAJl/Y6LUQOltZi9ZYSKIoMoyKfDCwNowen4NtDVac9P+2KAaeFyNmO0S81GrsP10CCb4i0b0r2wOtwOL2AJCHCrJxWw+A+8XB5VJgsJpRXNsLh8qL4eD22H6iAyaAgNSECDc0eqKqGm68ZHJIh0u1n47XZbCgqKsLf/vY3AEB+fj6eeOIJ1NTUIDEx0b/dJ598ghkzZkCWZSQmJmLixIlYu3Yt7rzzzoBfK9xHYLB9oaur2ratqAKpiZEwGX66N8XtVVFV60BqQkSb+xpbfvWKMPtuyDSeXKxp1482xESZYDEqvmVpAVQaDZAl3/WL1m2raXIjIdbi6yLTfLPZllY2YVDveBhk2Te7LQCPR0NppR1902NhVGT/7LduVcXBsjoMH5TUprCio7VIT4qC0aDg5DS5cHtVWGscSEuIaFODpgkcLKtHbna/Nv8v9pXUnPb/wlbvG/GV1GopYLdXxb6SGgwbmASTUUF8fCTMJ3sId/9YjT5pMYiOMCHCrCAqwjfMeltRBUaFYHfW+XwugxIgVqsVaWlpUBTfm6YoClJTU2G1WtsEiNVqRWZmpv9xRkYGysvL2/VaCQlRHVN0N6X3N4VQEc7t66q2zb/9F13yuq397uYx532Mxwed/w/nBedxjPSkqPM+RrjhXTlERKRLUAIkIyMDFRUVUFXfUDtVVVFZWYmMjIzTtjtx4oT/sdVqRXp6ejBKJCKidgpKgCQlJSErKwsFBQUAgIKCAmRlZbXpvgKAvLw8rFq1CpqmoaamBhs2bEBubm4wSiQionYK2iis4uJizJs3Dw0NDYiNjcXSpUsxcOBAzJ49G3PnzsXw4cOhqioWL16MLVu2AABmz56NmTNnBqM8IiJqp6AFCBERhRdeRCciIl0YIEREpAsDhIiIdGGAEBGRLiE7G+8999yDY8eOQZZlREZG4o9//COysrICmrQxVLz00ktYvnw51qxZg8GDB+P777/H448/DpfLhV69euHZZ59FUlLSuQ/UDeXk5MBkMsFsNgMAHnroIVx55ZVh0UaXy4UlS5Zg69atMJvNGDVqFJ544omw+GweO3YMc+bM8T9ubGxEU1MTtm/fHhbtA4B//vOfWLZs2cnZigXuvfdeTJo0KWzat3HjRixbtgxerxdxcXF4+umn0adPH33tEyGqoaHB/+/PPvtMTJ8+XQghxC233CI+/PBDIYQQH374objlllu6pL7ztXfvXnHHHXeIq6++Whw8eFCoqiomTpwoduzYIYQQ4uWXXxbz5s3r4ir1a2lXa+HSxieeeEI89dRTQtM0IYQQVVVVQojw+Wy29uSTT4pFixYJIcKjfZqmiTFjxvg/m/v37xejRo0SqqqGRfvq6urEZZddJg4fPiyE8LXj9ttvF0Loe/9CNkBa++CDD8R1110nqqurxejRo4XX6xVCCOH1esXo0aOFzWbr4grbx+VyiRtvvFGUlZX5f9Du3r1bTJkyxb+NzWYTo0aN6sIqz8+ZAiQc2tjU1CRGjx4tmpqa2jwfLp/N1lwul8jOzhZ79+4Nm/ZpmiYuu+wysXPnTiGEENu3bxeTJk0Km/bt3r1bTJ482f+4trZWDB48WHf7QrYLCwAeffRRbNmyBUIIvPnmmwFP2tjdLVu2DNOmTWszjf2pE00mJiZC0zT/6WYoeuihhyCEwOjRo/G73/0uLNpYVlaG+Ph4vPTSS9i2bRuioqJw//33w2KxhMVns7UvvvgCaWlpuPjii7F3796waJ8kSXjhhRdwzz33IDIyEna7Ha+//nrY/GwZMGAAqqurUVhYiBEjRmDNmjUAAp/w9lQhfRH9qaeewsaNG/Hggw/imWee6epyOsSuXbuwd+9ezJo1q6tL6VQrV67E6tWr8d5770EIgcWLF3d1SR1CVVWUlZXhoosuwvvvv4+HHnoI9913H5qbm7u6tA733nvv4YYbbujqMjqU1+vFn//8Z7zyyiv45z//iVdffRUPPPBA2Lx/MTExeP755/H000/j+uuvh81mQ2xsrO72hXSAtJg+fTq2bduG9PT0gCZt7M527NiB4uJiTJgwATk5OSgvL8cdd9yBo0ePtplosqamBrIsh8xv5qdqeU9MJhNmzZqF77777rTJNEOxjRkZGTAYDMjPzwcAjBw5EgkJCbBYLCH/2WytoqICO3bswNSpUwEEPmFqd7d//35UVlZi9OjRAIDRo0cjIiICZrM5LNoHAJdffjneeecdvP/++/jXf/1XOJ1O9OrVS1f7QjJA7HY7rFar//EXX3yBuLi4gCdt7M7uuusubN68GV988QW++OILpKen4y9/+QvuvPNOOJ1O7Ny5EwDw7rvvIi8vr4ur1ae5uRmNjb7V+YQQ+OSTT5CVlYVhw4aFfBsTExORnZ3tn8+tpKQENpsN/fv3D/nPZmsffPABxo8fj4QE36qB4fC9BwDp6ekoLy/H4cOHAfjm8LPZbOjXr19YtA8AqqqqAACapuG5557DTTfdhF69eulqX0jOhVVdXY177rkHDocDsiwjLi4ODz/8MC6++OKzTtoYqnJycvDaa69h8ODB+O6777BgwYI2Q1yTk0NvCc2ysjLcd999UFUVmqZh0KBBeOyxx5CamhoWbSwrK8P8+fNRV1cHg8GABx54AOPHjw+rz2Zubi4effRRXHXVVf7nwqV9q1evxhtvvAFJ8q3UN3fuXEycODFs2vfoo4/iu+++g8fjwRVXXIH58+fDbDbral9IBggREXW9kOzCIiKirscAISIiXRggRESkCwOEiIh0YYAQEZEuDBAiItIlpOfCIjrVJZdc4v+3w+GAyWTyz++zaNEiTJs2ratK0y0nJwdPPvkkLr/88q4uhagNBgiFlV27dvn/HQo/eL1eLwyGzv02DMZrUM/ELizqETRNw+uvv46JEyciOzsb999/P+rq6gD4FkkaMmQI3nvvPYwfPx5jx47FO++8g8LCQkydOhVjxoxpM9nj+++/j5tuugmLFy/G6NGjkZeXh61bt/q/3tjYiPnz52PcuHG48sor8fzzz/vnGGrZd8mSJcjOzsby5ctRWlqKW2+9FdnZ2cjOzsbvf/97NDQ0AAD+8Ic/4MSJE/iP//gPXHLJJXjjjTewbdu2NneAA76w/PrrrwEAy5cvx9y5c/HQQw/h0ksvxQcffPCzNRHpxQChHuG///u/sWHDBqxYsQKbNm1CXFzcaTMA7969G+vXr8fzzz+PJUuW4LXXXsNbb72Fjz/+GJ9++im2b9/u37awsBB9+/bFN998g7lz5+Lee+/1B9K8efNgMBiwfv16fPjhh9iyZQtWrVrVZt8+ffpgy5YtuPvuuyGEwG9/+1ts2rQJn376KcrLy7F8+XIAwLPPPovMzEy89tpr2LVrF2bPnh1Qez///HPk5eVh586dmDp16jlrItKDAUI9wrvvvosHH3wQ6enpMJlMuPfee7Fu3Tp4vV7/NnPmzIHZbMa4ceMQGRmJ/Px8JCUlIS0tDWPGjEFRUZF/28TERPzbv/0bjEYjJk+ejAEDBmDjxo2orq7Gl19+ifnz5yMyMhJJSUn493//d3z88cf+fVNTU3HLLbfAYDDAYrGgX79+uOKKK2AymZCYmIjbbrsNO3bsOK/2jho1ChMnToQsy2hqajpnTUR6sGOUeoQTJ05gzpw5kOWffmeSZRk2m83/uPXa62az+bTHrddMSEtL80+2BwCZmZmorKzEiRMn4PV6MW7cOP/XNE1rMy12enp6m9qqq6vx1FNPYefOnbDb7RBCIDY29rza2/o1AqmJSA8GCPUI6enpWLJkiX+dh9aOHTvW7uNVVFRACOEPEavVipycHP8ZzjfffHPWC9etgwcAnnvuOUiShDVr1iA+Ph4bNmz42QW2IiIi4HQ6/Y9VVUVNTc1ZXyOQmoj0YBcW9Qi/+c1v8MILL+D48eMAfItVbdiwQffxampq8Pbbb8Pj8eDTTz9FcXExxo8fj9TUVFxxxRX405/+hKamJmiahtLS0jbXT05lt9sRGRmJmJgYVFRU4M0332zz9eTkZJSVlfkfDxgwAC6XCxs3boTH48Grr74Kt9t91uPrqYkoEAwQ6hFuvfVW5OTk4Pbbb8cll1yCG2+8EYWFhbqPN2LECBw9ehS/+MUv8MILL+DFF1/0L670zDPPwOPxYPLkyRg7dizmzp3rX8TnTO69914UFRVhzJgxuOuuuzBp0qQ2X7/rrrvw6quvYsyYMfjLX/6CmJgYLFiwAI899hiuuuoqREREnNYtdqr21kQUCK4HQtRO77//PlatWoV33nmnq0sh6lI8AyEiIl0YIEREpAu7sIiISBeegRARkS4MECIi0oUBQkREujBAiIhIFwYIERHpwgAhIiJd/h8ZPsfu8SN/bwAAAABJRU5ErkJggg==\n", 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"text/plain": [ "
" ] @@ -911,41 +849,191 @@ "
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TemperatureInterceptTemperatureFrequency
030.011.030.00.834373
130.511.030.50.826230
231.011.031.00.817774
331.511.031.50.809002
4132.00.799911
5132.50.790500
6133.00.780766
711.033.50.770712
8134.00.760339
9134.50.749648
10135.00.738645
11135.50.727334
12136.00.715721
13136.50.703816
14137.00.691626
15137.50.679164
16138.00.666441
17138.50.653471
18139.00.640269
19139.50.626851
20140.00.613235
21140.50.599439
22141.00.585485
23141.50.571391
24142.00.557181
25142.50.542876
26143.00.528501
27143.50.514078
28144.00.499631
29144.50.485186
......
91175.50.025508
92176.00.024110
93176.50.022787
94177.00.021535
95177.50.020350
96178.00.019229
97178.50.018169
98179.00.017166
99179.50.016217
100180.00.015321
101180.50.014473
102181.00.013671
103181.50.012913
104182.00.012197
105182.50.011520
106183.00.010880
107183.50.010275
108184.00.009703
109184.50.009163
110185.00.008653
111185.50.008171
112186.00.007716
113186.50.007286
114187.00.006879
115187.50.006496
11688.011.088.00.006133
11788.511.088.50.005791
11889.011.089.00.005467
11989.511.089.50.005162
12090.011.090.00.004873
\n", @@ -989,18 +1227,68 @@ "" ], "text/plain": [ - " Temperature Intercept Frequency\n", - "0 30.0 1 1.0\n", - "1 30.5 1 1.0\n", - "2 31.0 1 1.0\n", - "3 31.5 1 1.0\n", - "4 32.0 1 1.0\n", - ".. ... ... ...\n", - "116 88.0 1 1.0\n", - "117 88.5 1 1.0\n", - "118 89.0 1 1.0\n", - "119 89.5 1 1.0\n", - "120 90.0 1 1.0\n", + " Intercept Temperature Frequency\n", + "0 1 30.0 0.834373\n", + "1 1 30.5 0.826230\n", + "2 1 31.0 0.817774\n", + "3 1 31.5 0.809002\n", + "4 1 32.0 0.799911\n", + "5 1 32.5 0.790500\n", + "6 1 33.0 0.780766\n", + "7 1 33.5 0.770712\n", + "8 1 34.0 0.760339\n", + "9 1 34.5 0.749648\n", + "10 1 35.0 0.738645\n", + "11 1 35.5 0.727334\n", + "12 1 36.0 0.715721\n", + "13 1 36.5 0.703816\n", + "14 1 37.0 0.691626\n", + "15 1 37.5 0.679164\n", + "16 1 38.0 0.666441\n", + "17 1 38.5 0.653471\n", + "18 1 39.0 0.640269\n", + "19 1 39.5 0.626851\n", + "20 1 40.0 0.613235\n", + "21 1 40.5 0.599439\n", + "22 1 41.0 0.585485\n", + "23 1 41.5 0.571391\n", + "24 1 42.0 0.557181\n", + "25 1 42.5 0.542876\n", + "26 1 43.0 0.528501\n", + "27 1 43.5 0.514078\n", + "28 1 44.0 0.499631\n", + "29 1 44.5 0.485186\n", + ".. ... ... ...\n", + "91 1 75.5 0.025508\n", + "92 1 76.0 0.024110\n", + "93 1 76.5 0.022787\n", + "94 1 77.0 0.021535\n", + "95 1 77.5 0.020350\n", + "96 1 78.0 0.019229\n", + "97 1 78.5 0.018169\n", + "98 1 79.0 0.017166\n", + "99 1 79.5 0.016217\n", + "100 1 80.0 0.015321\n", + "101 1 80.5 0.014473\n", + "102 1 81.0 0.013671\n", + "103 1 81.5 0.012913\n", + "104 1 82.0 0.012197\n", + "105 1 82.5 0.011520\n", + "106 1 83.0 0.010880\n", + "107 1 83.5 0.010275\n", + "108 1 84.0 0.009703\n", + "109 1 84.5 0.009163\n", + "110 1 85.0 0.008653\n", + "111 1 85.5 0.008171\n", + "112 1 86.0 0.007716\n", + "113 1 86.5 0.007286\n", + "114 1 87.0 0.006879\n", + "115 1 87.5 0.006496\n", + "116 1 88.0 0.006133\n", + "117 1 88.5 0.005791\n", + "118 1 89.0 0.005467\n", + "119 1 89.5 0.005162\n", + "120 1 90.0 0.004873\n", "\n", "[121 rows x 3 columns]" ] @@ -1071,7 +1359,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.6.4" }, "toc": { "base_numbering": 1, diff --git a/module4/challenger.ipynb b/module4/challenger.ipynb index 1200c12..b4e8ba2 100644 --- a/module4/challenger.ipynb +++ b/module4/challenger.ipynb @@ -538,10 +538,10 @@ " Method: IRLS Log-Likelihood: -3.9210\n", "\n", "\n", - " Date: Tue, 14 Apr 2020 Deviance: 3.0144\n", + " Date: Wed, 15 Apr 2020 Deviance: 3.0144\n", "\n", "\n", - " Time: 10:27:51 Pearson chi2: 5.00 \n", + " Time: 15:47:34 Pearson chi2: 5.00 \n", "\n", "\n", " No. Iterations: 6 \n", @@ -572,8 +572,8 @@ "Model Family: Binomial Df Model: 1\n", "Link Function: logit Scale: 1.0000\n", "Method: IRLS Log-Likelihood: -3.9210\n", - "Date: Tue, 14 Apr 2020 Deviance: 3.0144\n", - "Time: 10:27:51 Pearson chi2: 5.00\n", + "Date: Wed, 15 Apr 2020 Deviance: 3.0144\n", + "Time: 15:47:34 Pearson chi2: 5.00\n", "No. Iterations: 6 \n", "Covariance Type: nonrobust \n", "===============================================================================\n", @@ -644,10 +644,10 @@ " Method: IRLS Log-Likelihood: -23.526\n", "\n", "\n", - " Date: Tue, 14 Apr 2020 Deviance: 18.086\n", + " Date: Wed, 15 Apr 2020 Deviance: 18.086\n", "\n", "\n", - " Time: 10:27:51 Pearson chi2: 30.0 \n", + " Time: 15:47:34 Pearson chi2: 30.0 \n", "\n", "\n", " No. Iterations: 6 \n", @@ -678,8 +678,8 @@ "Model Family: Binomial Df Model: 1\n", "Link Function: logit Scale: 1.0000\n", "Method: IRLS Log-Likelihood: -23.526\n", - "Date: Tue, 14 Apr 2020 Deviance: 18.086\n", - "Time: 10:27:51 Pearson chi2: 30.0\n", + "Date: Wed, 15 Apr 2020 Deviance: 18.086\n", + "Time: 15:47:34 Pearson chi2: 30.0\n", "No. Iterations: 6 \n", "Covariance Type: nonrobust \n", "===============================================================================\n", @@ -780,55 +780,6 @@ "plt.grid(True)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There were warnings during the construction of the log model. let's try and change it" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "ename": "DistributionNotFound", - "evalue": "The 'statsmodel==0.9.0' distribution was not found and is required by the application", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mDistributionNotFound\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpkg_resources\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpkg_resources\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"statsmodel==0.9.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstatsmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/pkg_resources/__init__.py\u001b[0m in \u001b[0;36mrequire\u001b[0;34m(self, *requirements)\u001b[0m\n\u001b[1;32m 899\u001b[0m \u001b[0mincluded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meven\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mwere\u001b[0m \u001b[0malready\u001b[0m \u001b[0mactivated\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mworking\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 900\u001b[0m \"\"\"\n\u001b[0;32m--> 901\u001b[0;31m \u001b[0mneeded\u001b[0m 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\u001b[0mreq\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDistributionNotFound\u001b[0m: The 'statsmodel==0.9.0' distribution was not found and is required by the application" - ] - } - ], - "source": [ - "import pkg_resources\n", - "pkg_resources.require(\"statsmodel==0.9.0\")\n", - "import statsmodel" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "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()\n" - ] - }, { "cell_type": "markdown", "metadata": { @@ -861,7 +812,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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"text/plain": [ "
" ] @@ -884,174 +835,6 @@ "source": [ "**I think I have managed to correctly compute and plot the uncertainty of my prediction.** Although the shaded area seems very similar to [the one obtained by with R](https://app-learninglab.inria.fr/moocrr/gitlab/moocrr-session3/moocrr-reproducibility-study/tree/master/challenger.pdf), I can spot a few differences (e.g., the blue point for temperature 63 is outside)... Could this be a numerical error ? Or a difference in the statistical method ? It is not clear which one is \"right\"." ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " Temperature Intercept Frequency\n", - "0 30.0 1 1.0\n", - "1 30.5 1 1.0\n", - "2 31.0 1 1.0\n", - "3 31.5 1 1.0\n", - "4 32.0 1 1.0\n", - ".. ... ... ...\n", - "116 88.0 1 1.0\n", - "117 88.5 1 1.0\n", - "118 89.0 1 1.0\n", - "119 89.5 1 1.0\n", - "120 90.0 1 1.0\n", - "\n", - "[121 rows x 3 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_pred" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1.0\n", - "1 1.0\n", - "2 1.0\n", - "3 1.0\n", - "4 1.0\n", - " ... \n", - "116 1.0\n", - "117 1.0\n", - "118 1.0\n", - "119 1.0\n", - "120 1.0\n", - "Length: 121, dtype: float64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n", - "logmodel.predict(data_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { -- 2.18.1