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dchansen/gist:7410575

Created November 11, 2013 09:39
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gistfile1.js
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from mpltools import style\n",
"from mpltools import layout\n",
"\n",
"style.use('ggplot')\n",
"%load_ext autoreload\n",
"%pylab inline\n",
"%autoreload 1\n",
"from stat_helper import *\n",
"from statsmodels.formula.api import Logit,gls,glm,ols\n",
"import pandas\n",
"import scipy.stats as stats\n",
"import statsmodels.api as sm\n",
"%config InlineBackend.figure_format=\"png\"\n",
"pylab.rcParams['savefig.dpi'] = 120\n",
"pylab.rcParams[\"figure.figsize\"] = (4.0,3.0)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n",
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['f', 'pylab', 'diff']\n",
"`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"birthw = pandas.read_stata(\"birthweight.dta\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Part A:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"birthw[\"bmi\"] = (birthw.prepregwei/birthw.height**2)\n",
"birthw[\"hasEndwei\"] = birthw.endwei.notnull()\n",
"birthw.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>birthweight</th>\n",
" <th>parity</th>\n",
" <th>prepregwei</th>\n",
" <th>height</th>\n",
" <th>endwei</th>\n",
" <th>id</th>\n",
" <th>bmi</th>\n",
" <th>hasEndwei</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 1658.000000</td>\n",
" <td> 1660.00000</td>\n",
" <td> 1660.000000</td>\n",
" <td> 1635.000000</td>\n",
" <td> 910.000000</td>\n",
" <td> 1660.000000</td>\n",
" <td> 1635.000000</td>\n",
" <td> 1660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 3595.531363</td>\n",
" <td> 0.88012</td>\n",
" <td> 69.005031</td>\n",
" <td> 1.675260</td>\n",
" <td> 84.776923</td>\n",
" <td> 1003.190964</td>\n",
" <td> 24.559389</td>\n",
" <td> 0.5481928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 512.581482</td>\n",
" <td> 1.03091</td>\n",
" <td> 14.877474</td>\n",
" <td> 0.064762</td>\n",
" <td> 15.160468</td>\n",
" <td> 579.191974</td>\n",
" <td> 4.883568</td>\n",
" <td> 0.497822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 1540.000000</td>\n",
" <td> 0.00000</td>\n",
" <td> 39.000000</td>\n",
" <td> 1.440000</td>\n",
" <td> 51.200001</td>\n",
" <td> 1.000000</td>\n",
" <td> 15.622619</td>\n",
" <td> False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 3260.000000</td>\n",
" <td> 0.00000</td>\n",
" <td> 59.000000</td>\n",
" <td> 1.630000</td>\n",
" <td> 74.500000</td>\n",
" <td> 502.750000</td>\n",
" <td> 21.230572</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 3600.000000</td>\n",
" <td> 1.00000</td>\n",
" <td> 66.000000</td>\n",
" <td> 1.680000</td>\n",
" <td> 82.199997</td>\n",
" <td> 1002.500000</td>\n",
" <td> 23.437498</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 3930.000000</td>\n",
" <td> 1.00000</td>\n",
" <td> 76.000000</td>\n",
" <td> 1.720000</td>\n",
" <td> 92.500000</td>\n",
" <td> 1506.250000</td>\n",
" <td> 26.729927</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 5490.000000</td>\n",
" <td> 10.00000</td>\n",
" <td> 199.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 166.000000</td>\n",
" <td> 1998.000000</td>\n",
" <td> 64.979591</td>\n",
" <td> True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
" birthweight parity prepregwei height endwei id bmi hasEndwei\n",
"count 1658.000000 1660.00000 1660.000000 1635.000000 910.000000 1660.000000 1635.000000 1660\n",
"mean 3595.531363 0.88012 69.005031 1.675260 84.776923 1003.190964 24.559389 0.5481928\n",
"std 512.581482 1.03091 14.877474 0.064762 15.160468 579.191974 4.883568 0.497822\n",
"min 1540.000000 0.00000 39.000000 1.440000 51.200001 1.000000 15.622619 False\n",
"25% 3260.000000 0.00000 59.000000 1.630000 74.500000 502.750000 21.230572 0\n",
"50% 3600.000000 1.00000 66.000000 1.680000 82.199997 1002.500000 23.437498 1\n",
"75% 3930.000000 1.00000 76.000000 1.720000 92.500000 1506.250000 26.729927 1\n",
"max 5490.000000 10.00000 199.000000 2.000000 166.000000 1998.000000 64.979591 True"
]
}
],
"prompt_number": 40
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Binomial regression to find the risk of not being weighed for obese and non-obese respectively"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"levels = [False,True]\n",
"fit = glm(\"C(hasEndwei,levels=levels) ~ (bmi > 30)-1\",birthw,family= sm.families.Binomial(sm.families.links.identity)).fit()\n",
"print fit.conf_int()\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0 1\n",
"bmi > 30[False] 0.438064 0.489614\n",
"bmi > 30[True] 0.310229 0.437519\n"
]
},
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>['C(hasEndwei, levels=levels)[False]', 'C(hasEndwei, levels=levels)[True]']</td> <th> No. Observations: </th> <td> 1660</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 1658</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>identity</td> <th> Scale: </th> <td>1.0</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -1139.7</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Deviance: </th> <td> 2279.4</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:37:48</td> <th> Pearson chi2: </th> <td>1.66e+03</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>3</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>bmi > 30[False]</th> <td> 0.4638</td> <td> 0.013</td> <td> 35.271</td> <td> 0.000</td> <td> 0.438 0.490</td>\n",
"</tr>\n",
"<tr>\n",
" <th>bmi > 30[True]</th> <td> 0.3739</td> <td> 0.032</td> <td> 11.514</td> <td> 0.000</td> <td> 0.310 0.438</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"=======================================================================================================================================\n",
"Dep. Variable: ['C(hasEndwei, levels=levels)[False]', 'C(hasEndwei, levels=levels)[True]'] No. Observations: 1660\n",
"Model: GLM Df Residuals: 1658\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: identity Scale: 1.0\n",
"Method: IRLS Log-Likelihood: -1139.7\n",
"Date: Mon, 11 Nov 2013 Deviance: 2279.4\n",
"Time: 09:37:48 Pearson chi2: 1.66e+03\n",
"No. Iterations: 3 \n",
"===================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"-----------------------------------------------------------------------------------\n",
"bmi > 30[False] 0.4638 0.013 35.271 0.000 0.438 0.490\n",
"bmi > 30[True] 0.3739 0.032 11.514 0.000 0.310 0.438\n",
"===================================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 41
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Binomial regression to find the risk difference of not being weighed between obese and non-obese"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit = glm(\"C(hasEndwei,levels=levels) ~ (bmi > 30)\",birthw,family= sm.families.Binomial(sm.families.links.identity)).fit()\n",
"print fit.conf_int()\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0 1\n",
"Intercept 0.438064 0.489614\n",
"bmi > 30[T.True] -0.158631 -0.021299\n"
]
},
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>['C(hasEndwei, levels=levels)[False]', 'C(hasEndwei, levels=levels)[True]']</td> <th> No. Observations: </th> <td> 1660</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 1658</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>identity</td> <th> Scale: </th> <td>1.0</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -1139.7</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Deviance: </th> <td> 2279.4</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:37:48</td> <th> Pearson chi2: </th> <td>1.66e+03</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>3</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 0.4638</td> <td> 0.013</td> <td> 35.271</td> <td> 0.000</td> <td> 0.438 0.490</td>\n",
"</tr>\n",
"<tr>\n",
" <th>bmi > 30[T.True]</th> <td> -0.0900</td> <td> 0.035</td> <td> -2.568</td> <td> 0.010</td> <td> -0.159 -0.021</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 42,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"=======================================================================================================================================\n",
"Dep. Variable: ['C(hasEndwei, levels=levels)[False]', 'C(hasEndwei, levels=levels)[True]'] No. Observations: 1660\n",
"Model: GLM Df Residuals: 1658\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: identity Scale: 1.0\n",
"Method: IRLS Log-Likelihood: -1139.7\n",
"Date: Mon, 11 Nov 2013 Deviance: 2279.4\n",
"Time: 09:37:48 Pearson chi2: 1.66e+03\n",
"No. Iterations: 3 \n",
"====================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------------\n",
"Intercept 0.4638 0.013 35.271 0.000 0.438 0.490\n",
"bmi > 30[T.True] -0.0900 0.035 -2.568 0.010 -0.159 -0.021\n",
"====================================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 42
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Confirmation of regression models by \"hand\" calculations"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def is_obese(x):\n",
" if pandas.isnull(x):\n",
" return None\n",
" if (x > 30):\n",
" return \"obese\"\n",
" else:\n",
" return \"normal\"\n",
"birthw[\"group\"] = pandas.Categorical(birthw.bmi.map(is_obese))\n",
"groups = birthw.groupby(\"group\")\n",
"obese = groups.get_group(\"obese\")\n",
"slim = groups.get_group(\"normal\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ciS=conf_interval(logical_not(slim.hasEndwei),kind=\"binomial\")\n",
"ciS"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 44,
"text": [
"(0.4365931808950429, 0.46213729653220098, 0.4885426123385605)"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ciO=conf_interval(logical_not(obese.hasEndwei),kind=\"binomial\")\n",
"ciO"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"(0.3143411491642051, 0.37387387387387389, 0.4411175095429777)"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"seO=sqrt(ciO[1]*(1.0-ciO[1])/obese.shape[0])\n",
"seS = sqrt(ciS[1]*(1.0-ciS[1])/slim.shape[0])\n",
"\n",
"seRD = sqrt(seO**2+seS**2)\n",
"RD = ciO[1]-ciS[1]\n",
"\n",
"RD-1.96*seRD,RD,RD+1.96*seRD"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"(-0.15701399048736531, -0.088263422658327095, -0.019512854829288898)"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"twoway = [[sum(obese.hasEndwei),obese.hasEndwei.count()-sum(obese.hasEndwei)],[sum(slim.hasEndwei),slim.hasEndwei.count()-sum(slim.hasEndwei)]]\n",
"stats.fisher_exact(twoway)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 47,
"text": [
"(1.4389188332276475, 0.016501119153887894)"
]
}
],
"prompt_number": 47
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Part B:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"birthw[\"weightGain\"] = birthw.endwei-birthw.prepregwei\n",
"birthw[\"prepregwei60\"] = birthw.prepregwei-60\n",
"birthw[\"group\"] = pandas.Categorical(birthw.bmi.map(is_obese))\n",
"groups = birthw.groupby(\"group\")\n",
"obese = groups.get_group(\"obese\")\n",
"slim = groups.get_group(\"normal\")\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bland_altman(birthw.endwei,birthw.prepregwei)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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MYKVHR+LogP6ylyK5UiSWJchwQmfCFuUsj2T8/9fajQsWG3belI49nYOIFAhC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91uR6k8k17T6XQAFBYWhj131apVYQflQgmPn0tEk48rLpo+Lsm24O9LiosmVKYuOMX4z6lqSq/O\nQ2fWU5Wt5hOTg01dVmpT82k67C12Wc53The7GAyGCWvL+UBWVhYff/AeG1JVEY9bnK5mfwiR9Yux\nv5F+e+Gfbz97X3+RH738X+e10CtWTFaR7snYJ61WywsvvHBO73PRBceFCxfy17/+lW3btnHbbbcB\nnpTqjh07KC0tjVeqjgGxVDLuMZhHSbb541zs8UUrFFqLZ1/sOz/5t0lTGHKxVeKeS5yt6k8ccVwM\nuOiCY0lJCQsXLuStt95iaGiI3Nxcdu7ciV6v54knnrjQzbukEEsl4/Z+Ez8unxL2+0jFIJEQiV/Z\n09vLvCiWhJPNEeJirMQ9VxiL6s9keb5xTD5cPDkNP3zzm9/k1ltvZffu3fz2t79FEASeeeYZysvL\nL3TTLilEkmzzFnvUWYb5VZc57PfjqV70ptWef3wN4juvsYFBtl1TwAYGEd95jaPv/ok323sRIliJ\nns9CoPOBcFJ0/pgscm1nq/oTRxwXAy66lSN4aBsPPPAADzzwwIVuyiWNWBwpSktL0el0E2pRFTWt\nNi2TdQebfe4PoTBZUoxejIWPeKnjckohxzF5cVEGxzgmDrGQ2CfaoiqmtFq6NiK/crKkGL04l0bS\nFxsupxRyHJMXF2VaNY5LG7Gk1a6Pwq+cLClGLy4njuHllEKOY/IivnKMY8IRc1otAr9ysqQY/XGu\njKQvNlxOKeQ4Ji/iwTGOCUesaTVkcrZ06INSjHb+f3t3HhTFte8B/AuOwCibBFFBg4BbjNG4ERG9\nLmgAE7mW3ho1EcE8sxl9msRUFo0arSQmajQpE0uhNDFxL7Tc0IigIgImllWCCigYIm5BtrAMMEv3\n+8NHX2aBC95xZhq+nyqr8PQZ6G+P9o8+031OZq3QZoYY26P2NIRMbReLI1lcS1eKiHglGg7dfAxu\nBJo0959YOmwYV4SQsZbcCMb3l+wdiyNZXIuH1f71L5MhxrY4o0d71F6GkKntYnEki2vJsNofOhGC\nICB+21aDSQImR03D4KFDLbrIMhFRazmIYjNPYrch7XFuVVsSBEEaVstMScaN/P8fVpsYhpDQMfjf\nN+ZLc2+O9uyMvm4uuFlVh/QKNS7WCXY59+bjsuf36XExkzy01UzmljW0NF450hPR3LDa9evXOfcm\nEdk1+f9aTrLTmrk3iYhsgcWRrI5zbxKRvWNxJKu7WVCAvm4uzfbp66rEjXzOvUlEtsHPHMnqjCcJ\nEEQReVW1yCirRmZpFW5W18JP6QQXd29cv36dd64SkdWxOJLVNZ4kQBBFRKTlIKCTM154yg3v9fWV\n7lxNK6vGN2//T5u6c5WI5IFnG7K60WPG4mLdoyeI8qpqEdDJGVuHB+G13j4Y4K5EBwcHDHBXYn7v\nrtjaryt6dwDy8vJsvNdE1J7wypGsrvEkAY7qKgR7uTbbn6vGE5G18cqRrK7x8k33OndB6FPuzfbn\nnatEZG0sjmQTDZMEVNXU8M5VIrI7LI5kU4/uXK1rtg9XjScia2NxJJviqvFEZI9YHMmmGt+52pTM\nWhGhXDWeiKyId6uSTXHVeCKyRyyOZFPGq8Z/fz4V167kcNV4IrIpFkeyucbLW328bLldrD8nCAJy\nc3ORnnbeYDHmkLBJGD1mLKe0eww8piQnXOxYxtrqQqa2ziQIAiaPDW1iMeYaXKwTWzWlnT1ksrTW\nZrL0MX0S+D7JAxc7JrKR3NxcLsZsYTymJDccwyAywsWYLY/HlOSGxZHICBdjtjweU5IbFkciI1yM\n2fJ4TEluWByJjHBKO8vjMSW5YXEkMsIp7SyPx5TkhsWRyAintLM8HlOSGz7KQWSEU9pZHo8pyY1V\nimNFRQWOHz+O/Px8FBQUoL6+HitXrsTAgQPN9s/Ly8Mvv/yCwsJCKJVKhISEYPbs2XBxaf4DfSJL\nMJ7SblNKMm5cyeeUdv8FHlOSG6sUx7t37+LIkSPo0aMH/P39cePGjSb7FhYWYvXq1ejVqxdiYmJQ\nUlKCo0eP4sGDB/j444+tsbtEBlPazX/jTVvvTpvAY0pyYpXiGBQUhO3bt6Nz587IzMxstjju2bMH\nbm5uWLVqlXSl6OPjg61btyIrKwuDBw+2xi4TEVE7ZpUxDBcXF3Tu3PwDwACgVquRlZWFsWPHGgyh\n/uMf/4CLiwvS09Of5G4SEREBsLO7VW/fvg1BEBAUFGTQrlAo0Lt3bxQWFtpmx4iIqF2xq+JYUVEB\nAPD09DTZ5uHhgfLycmvvEhERtUOt/sxRFEVotdoW9XVycmrV99ZoNABgdjkSJycnaTsREdGT1Ori\neP36daxevbpFfTdu3AhfX98Wf++GYmqu+Go0mlYXW2Ndu3b9z51kRKFQMJMMMJM8MJM8KBTWeTy/\n1T/Fz88PCxYsaFFfc8OjLenfMLzaWEVFBby8vJp9/cmTJ/Hrr7+a3bZs2TJ4e3u3an/sXVVVFdzc\n3Gy9GxbFTPLATPLQFjMBwDfffIOioiKz28LDwxEREfHf/xDRyjIyMkSVSiVeu3bNZFtNTY04a9Ys\n8eeffzZo12q1YnR0tLhly5bH/rlLlix57NfaK2aSB2aSB2aSD2vksqsbcjp16oTBgwfj/PnzqKv7\n9wz+qampqK+vR0hIiA33joiI2gurza2akJAAANKlcGpqKnJycgAAM2bMkPrNmjULy5cvx8qVKxEW\nFoaysjIcO3YMQ4YMwZAhQ6y1u0RE1I5ZrTju37/f4O9nzpyRvm5cHAMCAvDpp59i165d2LlzJ5RK\nJSZOnIhXXnnFWrtKRETtnNWK4759+1rcd8CAAVizZs0T3BsiIqKm2dVnjkRERPagw6pVq1bZeies\npU+fPrbeBYtjJnlgJnlgJvl40rkcRFFsfnluIiKidobDqkREREZYHImIiIywOBIRERlhcSQiIjLC\n4khERGTEapMAWNPBgwexb98+9OzZExs2bDDYdufOHfz000/Iy8uDQqHAsGHDMHfuXLi7u9tob5t2\n69YtHDhwAHl5edBqtfDx8cGkSZMQGRkp9ZFTnoKCAiQkJKCgoABqtRre3t4IDQ1FVFSUwXJk9pqp\nrq4OR44cwc2bN5Gfnw+1Wo23334b48ePN+nbmgwpKSk4evQoiouL4e3tjcjISMusKtACLckkiiLO\nnTuHixcvorCwENXV1fDx8UFoaCimTp1qdv1Ve89kTKfT4YMPPsC9e/cwZ84cTJ061aSPXDIJgoDT\np08jKSkJ9+/fh7OzM/z9/RETEwN/f3/ZZRJFEUlJSUhOTsaDBw+gUCjQq1cvREVFYdiwYSbf11KZ\n2txzjqWlpdi0aRMUCgVcXV3x4osvGmxbtmwZtFotpk+fjsDAQJw7dw6XLl3ChAkT4OhoPxfSV65c\nwZo1a9C5c2eEh4dj5MiRcHd3R21tLQYPHgxAXnlu376N5cuXQ6PRICIiAsHBwRBFEYmJibh9+zZC\nQ0MB2HemsrIybNiwAXq9Hn5+fnj48CGCg4PRu3dvg36tyZCUlIS4uDgMHDgQU6ZMgSAIOHz4MJyc\nnDBgwAC7yFRfX49ly5bBzc0NY8aMwahRo6T3Ljc31+RkJodMxhITE3Hp0iXo9XoMGTIE/fr1M9gu\np0w//PADjhw5gqFDh2LSpEno168fNBoNunfvjm7dusku0549e7Bnzx4EBgYiIiIC/fv3x82bN3H8\n+HH4+/vDz8/vyWR64ut+WNnGjRvF1atXi6tWrRLfe+89g21xcXHinDlzxJKSEqktKytLVKlUYlJS\nkrV3tUk1NTXi/PnzxfXr1zfbTy55RFEUd+/eLapUKrGoqMigffPmzaJKpRJrampEUbTvTFqtVqyo\nqBBFURQLCgpElUolnj171qRfSzPU19eLr732mrh27VqD13/33XdidHS0WF1d/YSS/FtLMmm1WjEv\nL8/ktQcOHBBVKpWYlZUltcklU2MVFRVibGysmJCQIKpUKvHo0aMG2+WU6cKFC6JKpRJ/++23Zr+f\nnDK98cYb4ieffGLQplarxblz54pfffWV1GbpTPZzaWEB169fx8WLFxEbGwtRFOHg4GCw/eLFixg+\nfDieeuopqe25555Djx49kJGRYe3dbVJaWhoqKysxe/ZsAI+GHwRBMOknlzwApGFTDw8Pg3ZPT084\nOjpKq3vbcyaFQiHtv9jM3BktzXD16lVUV1cjPDzc4PXh4eGor6/H5cuXLZzAVEsyKRQKkyspAAgO\nDgYA3L17V2qTS6bGdu3aBV9fX4wZM8bsdjllOn78OPr06YORI0dCEASDpf8ak1MmJycnkwWblUol\nnJ2d4ezsLLVZOlObKY6CIGDHjh0ICwtDr169TLaXlZWhsrISgYGBJtuCgoJQWFhohb1smezsbCiV\nSpSUlGDx4sWIiYlBbGws4uPjodVqAcgrDwBMmDABHh4e2LJlCwoLC1FSUoL09HQkJSUhMjISTk5O\nsstkTmsyNHxt3DcwMBAODg52n7eiogIADD5HlVum/Px8pKamIjY2tsk+csmkVquRn5+PoKAg7N69\nG7GxsYiJicGiRYtMfrGUSyYAmDZtGq5cuYKTJ0+iuLgYd+/eRXx8PGprazFlyhSpn6UztZkbck6d\nOoWSkhKsWLHC7Pby8nIAQJcuXUy2denSBdXV1dDpdNIVjC09ePAAer0e69atQ1hYGAYOHIhr167h\n5MmTqKmpweLFi2WVBwC8vLywZs0afPnll/jwww+l9unTp2PmzJkA5PUeNaU1GcrLy+Ho6Ghyk45C\noYCbm5v0vezV4cOH0alTJzz//PNSm5wyiaKI7du3Y/To0ejbty+Ki4vN9pNLpr/++gsAcOHCBSgU\nCkRHR0OpVOLEiRPYtGkTlEql9F7JJRMAhIWFwdHREdu2bcOOHTsAAG5ublixYgX69u0r9bN0Jvs9\ny7RCVVUV9u/fjxkzZphcfjfQaDQAYPbOuoY2jUZjFyfeuro6aDQaTJ48WfqNNjg4GDqdDqdPn8bM\nmTNllQd4dJXxxRdfAADefPNNuLq64vLlyzh48CA8PDwQEREhu0zmtCZDc1katturgwcP4urVq5g/\nfz46deoktcsp09mzZ1FUVISlS5c2208umRqGUKurq/H5559LE3OPGDECCxcuxMGDB6XiKJdMAJCR\nkYFt27Zh1KhRGDVqFGpra3H8+HGsX78en332Gbp37w7A8pns9yzTCnv37oWbm5vBIw7GGj7zahiW\nbKyhrfHjBLbUsB/Gn4GEhobi9OnTuHHjhnSHlhzyAEBCQgLKysrw7bffwsvLC8Cjgi8IAnbt2oUx\nY8bI6j1qSmsyODk5QafTmf0+Wq3WbrOmp6dj3759mDhxIiZPnmywTS6Z1Go1du/ejaioKOnfY1Pk\nkqlhP3x8fAxWrHBxccGwYcOQlpYGQRDg6Ogom0wajQbx8fEYOnQoFi9eLLWPGDECixcvxt69e7Fk\nyRIAln+fZP+Z4/3795GcnIzIyEiUlpaiuLgYxcXF0Gq10Ol0ePjwIaqrq6VhLnOX1uXl5XB1dbWb\nK5KGfTW+eaXh7zU1NbLKAwC5ubkICAgwORGNGDECGo0GhYWFsstkTmsydOnSBYIgoLKy0qCfTqcz\n+DdrT7KysrB582YMHz4cr7/+usl2uWQ6evQo9Ho9QkJCpHNGWVkZgEdXXsXFxdKJVi6ZGvbD09PT\nZJuHhwf0ej3q6+ulvnLIdO/ePVRXV2PEiBEG7a6urujfvz/y8vKkNktnsu8zTQuUlZVBFEXs2LFD\nGo9ubOHChZgyZQpiYmLg7u6OgoICkz75+fnNPgNlbYGBgcjOzkZpaSl69OghtTeccN3d3eHl5SWb\nPACg1+vN3nHbcALS6/Wyy2ROazI0fF1QUIChQ4dK7QUFBRBF0e7y3rx5E+vWrUOfPn3w7rvvmn3m\nVC6ZSktLUVNTg/fff99k26FDh3Do0CF8/fXX8Pf3l00mLy8veHp6SkW+sfLycjg5OUGpVAKQz/vU\ncH4wd+4wPqdYOpPsi+PTTz+NpUuXGjy2IYoi9u7di7q6OsybN0968PWFF17AuXU5pVoAAANkSURB\nVHPnUFpaKt1mn52djQcPHpidEcNWRo8ejcOHDyMlJQWDBg2S2pOTk9GhQwc8++yzAOSTBwACAgKQ\nmZmJ+/fvGxT8CxcuwNHRUZq5Q06ZmtLSDIMGDYKrqytOnTpl8J/51KlTcHZ2Njv7h63cuXMHa9eu\nRbdu3fDRRx+Z/UwVkE+myMhI6VGUBhUVFYiLi8P48eMxcuRIdO3aFYB8MgFASEgITpw4gaysLGmy\nkMrKSvz+++/SeQOQT6aePXtCoVDgwoULmDRpktReWlqKnJwcPPPMM1KbpTPJfoYcZ2dn+Pn5wdfX\nV/rj5+eHjIwMCIKAefPmScOR/v7+SElJQXp6OhwcHJCdnY0ff/wRPXr0wPz58+1mRhlPT0+UlpYi\nNTUVd+/exd9//40jR44gMzMT06ZNk4YY5JIHALp3746UlBSkpaVBq9WiqKgIBw4cwOXLlzFx4kRp\nhhx7z3Ty5ElcuXIFOTk5uHXrFhwcHHD//n3k5OSgd+/e6NixY4szdOjQAUqlEomJiSgqKoJarUZi\nYiLS0tKgUqmkk5utM+l0OixfvhyVlZV46aWXUF5ejj///FP6U19fL/0SIJdMPj4+BucMX19fuLq6\n4sSJExg7dizGjx8v/QIgl0wdO3ZEQEAAzp8/j/Pnz0On0+GPP/5AfHw81Go1lixZIp0L5ZJJqVSi\nvr4eaWlpyMnJQW1tLbKzsxEXFwe1Wo233noL3t7eTySTg9iSp2Rl6LPPPkNVVRXWr19v0N4w52Vu\nbi46duxoN/N2GtPr9Th06BDOnDmD8vJydO3aFeHh4QbP9QDyyQM8Glbcv3+/NFdst27dMG7cOERF\nRRkUPXvO9M4776CkpMTstu+//176j9qaDMnJyTh27Jg0F6S59/lJ+k+ZBEHAokWLmnz9uHHjsGDB\nAoM2e8/U8D41VlxcjEWLFiE6Ohovv/yyyXa5ZCouLsbOnTtx9epV6PV69OvXD6+++qrZZ2/lkikx\nMdFgbtU+ffpgxowZGDhwoMnrLJWpzRZHIiKix2U/425ERER2gsWRiIjICIsjERGRERZHIiIiIyyO\nRERERlgciYiIjLA4EhERGWFxJCIiMsLiSEREZITFkYiIyAiLIxERkREWRyIiIiMsjkREREZYHImI\niIywOBIRERn5P+JXhMdNnvmdAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x12532780>"
]
}
],
"prompt_number": 86
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear least squares model to test for correlation between weight gain and pre-pregnancy weight:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit = ols(\"weightGain ~ prepregwei60\",birthw).fit()\n",
"print fit.pvalues\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Intercept 0.000000e+00\n",
"prepregwei60 1.506404e-09\n",
"dtype: float64\n"
]
},
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.039</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.038</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 37.29</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td>1.51e-09</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:17:19</td> <th> Log-Likelihood: </th> <td> -2949.0</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 910</td> <th> AIC: </th> <td> 5902.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 908</td> <th> BIC: </th> <td> 5912.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 15.9299</td> <td> 0.244</td> <td> 65.408</td> <td> 0.000</td> <td> 15.452 16.408</td>\n",
"</tr>\n",
"<tr>\n",
" <th>prepregwei60</th> <td> -0.0831</td> <td> 0.014</td> <td> -6.107</td> <td> 0.000</td> <td> -0.110 -0.056</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>50.916</td> <th> Durbin-Watson: </th> <td> 2.029</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 86.849</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.415</td> <th> Prob(JB): </th> <td>1.38e-19</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.266</td> <th> Cond. No. </th> <td> 21.3</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 85,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.039\n",
"Model: OLS Adj. R-squared: 0.038\n",
"Method: Least Squares F-statistic: 37.29\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 1.51e-09\n",
"Time: 10:17:19 Log-Likelihood: -2949.0\n",
"No. Observations: 910 AIC: 5902.\n",
"Df Residuals: 908 BIC: 5912.\n",
"Df Model: 1 \n",
"================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"--------------------------------------------------------------------------------\n",
"Intercept 15.9299 0.244 65.408 0.000 15.452 16.408\n",
"prepregwei60 -0.0831 0.014 -6.107 0.000 -0.110 -0.056\n",
"==============================================================================\n",
"Omnibus: 50.916 Durbin-Watson: 2.029\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 86.849\n",
"Skew: 0.415 Prob(JB): 1.38e-19\n",
"Kurtosis: 4.266 Cond. No. 21.3\n",
"==============================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hist_test(fit.resid)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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gnvNIFEp8Lor19fVISkpCeLjrVE1aWhoAoKGhwWtsQ0OD27VDANDpdBBFEQaD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\nYc81ev4HBsjHx8d2iuO36jns7vnrMDQ0FKVSSUlJCTExMbZ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"text": [
"<matplotlib.figure.Figure at 0x1236ee48>"
]
}
],
"prompt_number": 51
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the non-obese group:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit = ols(\"weightGain ~ prepregwei60\",slim).fit()\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.001</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> -0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 0.8664</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td> 0.352</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:37:49</td> <th> Log-Likelihood: </th> <td> -2423.3</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 760</td> <th> AIC: </th> <td> 4851.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 758</td> <th> BIC: </th> <td> 4860.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 15.8718</td> <td> 0.241</td> <td> 65.750</td> <td> 0.000</td> <td> 15.398 16.346</td>\n",
"</tr>\n",
"<tr>\n",
" <th>prepregwei60</th> <td> -0.0205</td> <td> 0.022</td> <td> -0.931</td> <td> 0.352</td> <td> -0.064 0.023</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>59.403</td> <th> Durbin-Watson: </th> <td> 1.954</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 100.722</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.544</td> <th> Prob(JB): </th> <td>1.34e-22</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.413</td> <th> Cond. No. </th> <td> 12.4</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.001\n",
"Model: OLS Adj. R-squared: -0.000\n",
"Method: Least Squares F-statistic: 0.8664\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 0.352\n",
"Time: 09:37:49 Log-Likelihood: -2423.3\n",
"No. Observations: 760 AIC: 4851.\n",
"Df Residuals: 758 BIC: 4860.\n",
"Df Model: 1 \n",
"================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"--------------------------------------------------------------------------------\n",
"Intercept 15.8718 0.241 65.750 0.000 15.398 16.346\n",
"prepregwei60 -0.0205 0.022 -0.931 0.352 -0.064 0.023\n",
"==============================================================================\n",
"Omnibus: 59.403 Durbin-Watson: 1.954\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 100.722\n",
"Skew: 0.544 Prob(JB): 1.34e-22\n",
"Kurtosis: 4.413 Cond. No. 12.4\n",
"==============================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hist_test(fit.resid)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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ZNoK6p2i321FaWoqamhrYbDao1Wrk5eUhLS1tzFibzYZDhw7h7NmzYFkWWq0W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\n8B8HC9HXdlzUXIjuyOjTQdSX4eHHCmr47efl1Bs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rudFoBK52hXW2r7bPh4v+iFVXRlqs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\nVpIUhRBCiFb/D1q8V5c4k2jCAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x125486d8>"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bland_altman(slim.weightGain,slim.prepregwei)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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il+tV+2CS45pSUAD7ZwfwcEyEzzzdvCgRttReYXSEh9vVeCk7BzdnyBHD42LF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\nzTUN9hUiIcAPG9OST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"text": [
"<matplotlib.figure.Figure at 0x127fb358>"
]
}
],
"prompt_number": 54
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And for the obese group"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit = ols(\"weightGain ~ prepregwei\",obese).fit()\n",
"print fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.026\n",
"Model: OLS Adj. R-squared: 0.019\n",
"Method: Least Squares F-statistic: 3.613\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 0.0594\n",
"Time: 09:37:50 Log-Likelihood: -475.61\n",
"No. Observations: 139 AIC: 955.2\n",
"Df Residuals: 137 BIC: 961.1\n",
"Df Model: 1 \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"Intercept 20.1042 4.364 4.607 0.000 11.474 28.734\n",
"prepregwei -0.0862 0.045 -1.901 0.059 -0.176 0.003\n",
"==============================================================================\n",
"Omnibus: 0.892 Durbin-Watson: 2.110\n",
"Prob(Omnibus): 0.640 Jarque-Bera (JB): 0.538\n",
"Skew: 0.119 Prob(JB): 0.764\n",
"Kurtosis: 3.191 Cond. No. 663.\n",
"==============================================================================\n"
]
}
],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hist_test(fit.resid)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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pCSaabE6OlsiEG+EZpCgKt5Z17V63zlNxwrPcFRuEwUsjE26Ex5CiKNxWW4eT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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x127e59b0>"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bland_altman(obese.weightGain,obese.prepregwei)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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csKPtFJEP8u500vQnIXZA5zSNeMo5zSgvWP0DOtKzktauf76jy+Cc5mjvbUtu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\nREQkEYsmERGRRF12cYOeoL6+HgcPHkRNTQ10Oh08PDwQHBy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"text": [
"<matplotlib.figure.Figure at 0x1251d5f8>"
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pred_interval(birthw.weightGain)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 58,
"text": [
"(2.755579769501395, 15.128571428571428, 27.501563087641461)"
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pred_interval(slim.weightGain)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 59,
"text": [
"(4.2481513496025318, 15.766312448601974, 27.284473547601415)"
]
}
],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pred_interval(obese.weightGain)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 60,
"text": [
"(-2.8956627174650897, 11.897121950876798, 26.689906619218686)"
]
}
],
"prompt_number": 60
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear model for weight gain as a function of parity"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit=ols(\"weightGain ~ (parity > 0)\",birthw).fit()\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.026</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.025</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 24.69</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td>8.05e-07</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:37:51</td> <th> Log-Likelihood: </th> <td> -2955.1</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 910</td> <th> AIC: </th> <td> 5914.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 908</td> <th> BIC: </th> <td> 5924.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 16.2875</td> <td> 0.312</td> <td> 52.278</td> <td> 0.000</td> <td> 15.676 16.899</td>\n",
"</tr>\n",
"<tr>\n",
" <th>parity > 0[T.True]</th> <td> -2.0679</td> <td> 0.416</td> <td> -4.969</td> <td> 0.000</td> <td> -2.885 -1.251</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>34.051</td> <th> Durbin-Watson: </th> <td> 2.012</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 68.485</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.226</td> <th> Prob(JB): </th> <td>1.35e-15</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.265</td> <th> Cond. No. </th> <td> 2.78</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 61,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.026\n",
"Model: OLS Adj. R-squared: 0.025\n",
"Method: Least Squares F-statistic: 24.69\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 8.05e-07\n",
"Time: 09:37:51 Log-Likelihood: -2955.1\n",
"No. Observations: 910 AIC: 5914.\n",
"Df Residuals: 908 BIC: 5924.\n",
"Df Model: 1 \n",
"======================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"--------------------------------------------------------------------------------------\n",
"Intercept 16.2875 0.312 52.278 0.000 15.676 16.899\n",
"parity > 0[T.True] -2.0679 0.416 -4.969 0.000 -2.885 -1.251\n",
"==============================================================================\n",
"Omnibus: 34.051 Durbin-Watson: 2.012\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 68.485\n",
"Skew: 0.226 Prob(JB): 1.35e-15\n",
"Kurtosis: 4.265 Cond. No. 2.78\n",
"==============================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit=ols(\"weightGain ~ (parity > 0)\",slim).fit()\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.038</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.037</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 29.97</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td>5.96e-08</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:37:51</td> <th> Log-Likelihood: </th> <td> -2409.0</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 760</td> <th> AIC: </th> <td> 4822.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 758</td> <th> BIC: </th> <td> 4831.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 17.0425</td> <td> 0.313</td> <td> 54.415</td> <td> 0.000</td> <td> 16.428 17.657</td>\n",
"</tr>\n",
"<tr>\n",
" <th>parity > 0[T.True]</th> <td> -2.3038</td> <td> 0.421</td> <td> -5.475</td> <td> 0.000</td> <td> -3.130 -1.478</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>60.017</td> <th> Durbin-Watson: </th> <td> 1.958</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 102.836</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.545</td> <th> Prob(JB): </th> <td>4.67e-23</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.435</td> <th> Cond. No. </th> <td> 2.76</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 62,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.038\n",
"Model: OLS Adj. R-squared: 0.037\n",
"Method: Least Squares F-statistic: 29.97\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 5.96e-08\n",
"Time: 09:37:51 Log-Likelihood: -2409.0\n",
"No. Observations: 760 AIC: 4822.\n",
"Df Residuals: 758 BIC: 4831.\n",
"Df Model: 1 \n",
"======================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"--------------------------------------------------------------------------------------\n",
"Intercept 17.0425 0.313 54.415 0.000 16.428 17.657\n",
"parity > 0[T.True] -2.3038 0.421 -5.475 0.000 -3.130 -1.478\n",
"==============================================================================\n",
"Omnibus: 60.017 Durbin-Watson: 1.958\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 102.836\n",
"Skew: 0.545 Prob(JB): 4.67e-23\n",
"Kurtosis: 4.435 Cond. No. 2.76\n",
"==============================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit=ols(\"weightGain ~ (parity > 0)\",obese).fit()\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> -0.007</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td>0.006664</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td> 0.935</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:37:51</td> <th> Log-Likelihood: </th> <td> -477.42</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 139</td> <th> AIC: </th> <td> 958.8</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 137</td> <th> BIC: </th> <td> 964.7</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 11.9618</td> <td> 1.019</td> <td> 11.733</td> <td> 0.000</td> <td> 9.946 13.978</td>\n",
"</tr>\n",
"<tr>\n",
" <th>parity > 0[T.True]</th> <td> -0.1071</td> <td> 1.311</td> <td> -0.082</td> <td> 0.935</td> <td> -2.700 2.486</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 0.612</td> <th> Durbin-Watson: </th> <td> 2.145</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.736</td> <th> Jarque-Bera (JB): </th> <td> 0.327</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.097</td> <th> Prob(JB): </th> <td> 0.849</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.136</td> <th> Cond. No. </th> <td> 2.94</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 63,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.000\n",
"Model: OLS Adj. R-squared: -0.007\n",
"Method: Least Squares F-statistic: 0.006664\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 0.935\n",
"Time: 09:37:51 Log-Likelihood: -477.42\n",
"No. Observations: 139 AIC: 958.8\n",
"Df Residuals: 137 BIC: 964.7\n",
"Df Model: 1 \n",
"======================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"--------------------------------------------------------------------------------------\n",
"Intercept 11.9618 1.019 11.733 0.000 9.946 13.978\n",
"parity > 0[T.True] -0.1071 1.311 -0.082 0.935 -2.700 2.486\n",
"==============================================================================\n",
"Omnibus: 0.612 Durbin-Watson: 2.145\n",
"Prob(Omnibus): 0.736 Jarque-Bera (JB): 0.327\n",
"Skew: 0.097 Prob(JB): 0.849\n",
"Kurtosis: 3.136 Cond. No. 2.94\n",
"==============================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 63
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On average, people who give birth for the first time gain more weight"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Part C:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"birthw[\"birthweightKg\"] = birthw.birthweight/1000.0\n",
"birthw.sort(\"birthweightKg\")\n",
"first = birthw[birthw.parity == 0]\n",
"others = birthw[birthw.parity != 0]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hist_test(first.birthweightKg)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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B0+U2vHGoBOFBKvzhR4MxJCKo/Z28FBakxu/vGoSBYVr85UQ5tuc2n19LFGhY9Ih62BVz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\nBqtXr27x9ZkzZyItLc334/u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"text": [
"<matplotlib.figure.Figure at 0x127fbb38>"
]
}
],
"prompt_number": 65
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear model for weight gain as a function of birthweight for first time mothers"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit = ols(\"weightGain ~ birthweightKg\",first).fit()\n",
"print fit.pvalues\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Intercept 0.003031\n",
"birthweightKg 0.000024\n",
"dtype: float64\n"
]
},
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.044</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.041</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 18.27</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td>2.40e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:39:36</td> <th> Log-Likelihood: </th> <td> -1297.4</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 400</td> <th> AIC: </th> <td> 2599.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 398</td> <th> BIC: </th> <td> 2607.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 6.7325</td> <td> 2.257</td> <td> 2.983</td> <td> 0.003</td> <td> 2.295 11.170</td>\n",
"</tr>\n",
"<tr>\n",
" <th>birthweightKg</th> <td> 2.6987</td> <td> 0.631</td> <td> 4.274</td> <td> 0.000</td> <td> 1.457 3.940</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>15.943</td> <th> Durbin-Watson: </th> <td> 2.083</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 34.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.145</td> <th> Prob(JB): </th> <td>4.10e-08</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.399</td> <th> Cond. No. </th> <td> 28.0</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 74,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.044\n",
"Model: OLS Adj. R-squared: 0.041\n",
"Method: Least Squares F-statistic: 18.27\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 2.40e-05\n",
"Time: 09:39:36 Log-Likelihood: -1297.4\n",
"No. Observations: 400 AIC: 2599.\n",
"Df Residuals: 398 BIC: 2607.\n",
"Df Model: 1 \n",
"=================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"---------------------------------------------------------------------------------\n",
"Intercept 6.7325 2.257 2.983 0.003 2.295 11.170\n",
"birthweightKg 2.6987 0.631 4.274 0.000 1.457 3.940\n",
"==============================================================================\n",
"Omnibus: 15.943 Durbin-Watson: 2.083\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 34.018\n",
"Skew: 0.145 Prob(JB): 4.10e-08\n",
"Kurtosis: 4.399 Cond. No. 28.0\n",
"==============================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hist_test(fit.resid)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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kyZPQ6/VISEjA0qVLERcX12efp556Ci0tLXj66aeRkpLiStlEQ0bbYcTRi824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\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x166a5898>"
]
}
],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(fit.resid,fit.predict(),\"o\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 82,
"text": [
"[<matplotlib.lines.Line2D at 0x123443c8>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ayRKnfFlOg55TTba+q/auLpIVgRTrjL30Iu2GIzkkiGtVF8mYO92x/6GMzLFX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\nEUIIcRn/H6u/7kD+QM9HAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1234bc88>"
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"first[\"birthweight3500\"]= first.birthweightKg-3.5\n",
"fit = ols(\"weightGain ~ birthweight3500\",first).fit()\n",
"print fit.pvalues\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Intercept 3.296164e-179\n",
"birthweight3500 2.403302e-05\n",
"dtype: float64\n"
]
},
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.044</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.041</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 18.27</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td>2.40e-05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:48:09</td> <th> Log-Likelihood: </th> <td> -1297.4</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 400</td> <th> AIC: </th> <td> 2599.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 398</td> <th> BIC: </th> <td> 2607.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 16.1780</td> <td> 0.312</td> <td> 51.881</td> <td> 0.000</td> <td> 15.565 16.791</td>\n",
"</tr>\n",
"<tr>\n",
" <th>birthweight3500</th> <td> 2.6987</td> <td> 0.631</td> <td> 4.274</td> <td> 0.000</td> <td> 1.457 3.940</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>15.943</td> <th> Durbin-Watson: </th> <td> 2.083</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 34.018</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.145</td> <th> Prob(JB): </th> <td>4.10e-08</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.399</td> <th> Cond. No. </th> <td> 2.04</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 83,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.044\n",
"Model: OLS Adj. R-squared: 0.041\n",
"Method: Least Squares F-statistic: 18.27\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 2.40e-05\n",
"Time: 09:48:09 Log-Likelihood: -1297.4\n",
"No. Observations: 400 AIC: 2599.\n",
"Df Residuals: 398 BIC: 2607.\n",
"Df Model: 1 \n",
"===================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"-----------------------------------------------------------------------------------\n",
"Intercept 16.1780 0.312 51.881 0.000 15.565 16.791\n",
"birthweight3500 2.6987 0.631 4.274 0.000 1.457 3.940\n",
"==============================================================================\n",
"Omnibus: 15.943 Durbin-Watson: 2.083\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 34.018\n",
"Skew: 0.145 Prob(JB): 4.10e-08\n",
"Kurtosis: 4.399 Cond. No. 2.04\n",
"==============================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 83
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Weight gain difference between a woman giving birth to a 3.7kg child and a woman giving birth to a 3.2kg"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"diff = (3.7-3.2)\n",
"f=fit.conf_int()\n",
"fit.params[\"birthweight3500\"]*diff,f.iat[1,0]*diff,f.iat[1,1]*diff"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 69,
"text": [
"(1.3493675947025474, 0.72871883616159117, 1.9700163532435035)"
]
}
],
"prompt_number": 69
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear model of weight gain as a function of first time parenthood and birthweight, with possible correlation between birthweight and parity"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit = ols(\"weightGain ~ (parity > 0)*birthweightKg\",birthw).fit()\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.088</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.085</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 29.25</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td>4.71e-18</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:37:52</td> <th> Log-Likelihood: </th> <td> -2925.3</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 910</td> <th> AIC: </th> <td> 5859.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 906</td> <th> BIC: </th> <td> 5878.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 3</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 6.7325</td> <td> 2.192</td> <td> 3.071</td> <td> 0.002</td> <td> 2.430 11.035</td>\n",
"</tr>\n",
"<tr>\n",
" <th>parity > 0[T.True]</th> <td> -5.1951</td> <td> 2.950</td> <td> -1.761</td> <td> 0.079</td> <td> -10.984 0.594</td>\n",
"</tr>\n",
"<tr>\n",
" <th>birthweightKg</th> <td> 2.6987</td> <td> 0.613</td> <td> 4.401</td> <td> 0.000</td> <td> 1.495 3.902</td>\n",
"</tr>\n",
"<tr>\n",
" <th>parity > 0[T.True]:birthweightKg</th> <td> 0.7682</td> <td> 0.813</td> <td> 0.944</td> <td> 0.345</td> <td> -0.828 2.365</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>29.286</td> <th> Durbin-Watson: </th> <td> 1.985</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 66.394</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.112</td> <th> Prob(JB): </th> <td>3.83e-15</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.304</td> <th> Cond. No. </th> <td> 80.0</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 70,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.088\n",
"Model: OLS Adj. R-squared: 0.085\n",
"Method: Least Squares F-statistic: 29.25\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 4.71e-18\n",
"Time: 09:37:52 Log-Likelihood: -2925.3\n",
"No. Observations: 910 AIC: 5859.\n",
"Df Residuals: 906 BIC: 5878.\n",
"Df Model: 3 \n",
"====================================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"----------------------------------------------------------------------------------------------------\n",
"Intercept 6.7325 2.192 3.071 0.002 2.430 11.035\n",
"parity > 0[T.True] -5.1951 2.950 -1.761 0.079 -10.984 0.594\n",
"birthweightKg 2.6987 0.613 4.401 0.000 1.495 3.902\n",
"parity > 0[T.True]:birthweightKg 0.7682 0.813 0.944 0.345 -0.828 2.365\n",
"==============================================================================\n",
"Omnibus: 29.286 Durbin-Watson: 1.985\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 66.394\n",
"Skew: 0.112 Prob(JB): 3.83e-15\n",
"Kurtosis: 4.304 Cond. No. 80.0\n",
"==============================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 70
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear model of weight gain as a function of first time parenthood and birthweight"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit = ols(\"weightGain ~ (parity > 0)+birthweightKg\",birthw).fit()\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.087</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.085</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 43.43</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td>9.72e-19</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>10:03:15</td> <th> Log-Likelihood: </th> <td> -2925.7</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 910</td> <th> AIC: </th> <td> 5857.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 907</td> <th> BIC: </th> <td> 5872.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 5.1869</td> <td> 1.458</td> <td> 3.557</td> <td> 0.000</td> <td> 2.325 8.049</td>\n",
"</tr>\n",
"<tr>\n",
" <th>parity > 0[T.True]</th> <td> -2.4363</td> <td> 0.406</td> <td> -6.002</td> <td> 0.000</td> <td> -3.233 -1.640</td>\n",
"</tr>\n",
"<tr>\n",
" <th>birthweightKg</th> <td> 3.1353</td> <td> 0.403</td> <td> 7.781</td> <td> 0.000</td> <td> 2.345 3.926</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>29.076</td> <th> Durbin-Watson: </th> <td> 1.984</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 65.568</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.112</td> <th> Prob(JB): </th> <td>5.78e-15</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.296</td> <th> Cond. No. </th> <td> 28.8</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 84,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.087\n",
"Model: OLS Adj. R-squared: 0.085\n",
"Method: Least Squares F-statistic: 43.43\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 9.72e-19\n",
"Time: 10:03:15 Log-Likelihood: -2925.7\n",
"No. Observations: 910 AIC: 5857.\n",
"Df Residuals: 907 BIC: 5872.\n",
"Df Model: 2 \n",
"======================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"--------------------------------------------------------------------------------------\n",
"Intercept 5.1869 1.458 3.557 0.000 2.325 8.049\n",
"parity > 0[T.True] -2.4363 0.406 -6.002 0.000 -3.233 -1.640\n",
"birthweightKg 3.1353 0.403 7.781 0.000 2.345 3.926\n",
"==============================================================================\n",
"Omnibus: 29.076 Durbin-Watson: 1.984\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 65.568\n",
"Skew: 0.112 Prob(JB): 5.78e-15\n",
"Kurtosis: 4.296 Cond. No. 28.8\n",
"==============================================================================\n",
"\"\"\""
]
}
],
"prompt_number": 84
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear model of weight gain as a function of first time parenthood and birthweight for non-obese women"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit = ols(\"weightGain ~ (parity > 0)+birthweight\",slim).fit()\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.106</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.104</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 45.00</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td>3.43e-19</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:37:52</td> <th> Log-Likelihood: </th> <td> -2381.0</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 760</td> <th> AIC: </th> <td> 4768.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 757</td> <th> BIC: </th> <td> 4782.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 5.8345</td> <td> 1.505</td> <td> 3.876</td> <td> 0.000</td> <td> 2.880 8.789</td>\n",
"</tr>\n",
"<tr>\n",
" <th>parity > 0[T.True]</th> <td> -2.6119</td> <td> 0.408</td> <td> -6.403</td> <td> 0.000</td> <td> -3.413 -1.811</td>\n",
"</tr>\n",
"<tr>\n",
" <th>birthweight</th> <td> 0.0032</td> <td> 0.000</td> <td> 7.601</td> <td> 0.000</td> <td> 0.002 0.004</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>48.757</td> <th> Durbin-Watson: </th> <td> 1.928</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 83.712</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.457</td> <th> Prob(JB): </th> <td>6.64e-19</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 4.345</td> <th> Cond. No. </th> <td>2.70e+04</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 72,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.106\n",
"Model: OLS Adj. R-squared: 0.104\n",
"Method: Least Squares F-statistic: 45.00\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 3.43e-19\n",
"Time: 09:37:52 Log-Likelihood: -2381.0\n",
"No. Observations: 760 AIC: 4768.\n",
"Df Residuals: 757 BIC: 4782.\n",
"Df Model: 2 \n",
"======================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"--------------------------------------------------------------------------------------\n",
"Intercept 5.8345 1.505 3.876 0.000 2.880 8.789\n",
"parity > 0[T.True] -2.6119 0.408 -6.403 0.000 -3.413 -1.811\n",
"birthweight 0.0032 0.000 7.601 0.000 0.002 0.004\n",
"==============================================================================\n",
"Omnibus: 48.757 Durbin-Watson: 1.928\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 83.712\n",
"Skew: 0.457 Prob(JB): 6.64e-19\n",
"Kurtosis: 4.345 Cond. No. 2.70e+04\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] The condition number is large, 2.7e+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
}
],
"prompt_number": 72
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Linear model of weight gain as a function of first time parenthood and birthweight for obese women"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fit = ols(\"weightGain ~ (parity > 0)+birthweight\",obese).fit()\n",
"fit.summary()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>weightGain</td> <th> R-squared: </th> <td> 0.086</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.073</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 6.430</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 11 Nov 2013</td> <th> Prob (F-statistic):</th> <td>0.00215</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>09:37:52</td> <th> Log-Likelihood: </th> <td> -471.14</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 139</td> <th> AIC: </th> <td> 948.3</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 136</td> <th> BIC: </th> <td> 957.1</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> -2.6570</td> <td> 4.194</td> <td> -0.634</td> <td> 0.527</td> <td> -10.950 5.636</td>\n",
"</tr>\n",
"<tr>\n",
" <th>parity > 0[T.True]</th> <td> -0.8787</td> <td> 1.276</td> <td> -0.688</td> <td> 0.492</td> <td> -3.403 1.646</td>\n",
"</tr>\n",
"<tr>\n",
" <th>birthweight</th> <td> 0.0041</td> <td> 0.001</td> <td> 3.585</td> <td> 0.000</td> <td> 0.002 0.006</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 0.540</td> <th> Durbin-Watson: </th> <td> 2.151</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.763</td> <th> Jarque-Bera (JB): </th> <td> 0.224</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>-0.042</td> <th> Prob(JB): </th> <td> 0.894</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 3.178</td> <th> Cond. No. </th> <td>2.57e+04</td>\n",
"</tr>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 73,
"text": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: weightGain R-squared: 0.086\n",
"Model: OLS Adj. R-squared: 0.073\n",
"Method: Least Squares F-statistic: 6.430\n",
"Date: Mon, 11 Nov 2013 Prob (F-statistic): 0.00215\n",
"Time: 09:37:52 Log-Likelihood: -471.14\n",
"No. Observations: 139 AIC: 948.3\n",
"Df Residuals: 136 BIC: 957.1\n",
"Df Model: 2 \n",
"======================================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"--------------------------------------------------------------------------------------\n",
"Intercept -2.6570 4.194 -0.634 0.527 -10.950 5.636\n",
"parity > 0[T.True] -0.8787 1.276 -0.688 0.492 -3.403 1.646\n",
"birthweight 0.0041 0.001 3.585 0.000 0.002 0.006\n",
"==============================================================================\n",
"Omnibus: 0.540 Durbin-Watson: 2.151\n",
"Prob(Omnibus): 0.763 Jarque-Bera (JB): 0.224\n",
"Skew: -0.042 Prob(JB): 0.894\n",
"Kurtosis: 3.178 Cond. No. 2.57e+04\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] The condition number is large, 2.57e+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
}
],
"prompt_number": 73
}
],
"metadata": {}
}
]
}
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