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28 changes: 14 additions & 14 deletions inst/doc/bootstrapping.html
Original file line number Diff line number Diff line change
@@ -274,16 +274,16 @@ <h1>Tidy bootstrapping with dplyr+broom</h1>
## Groups: replicate
##
## replicate term estimate stderror statistic p.value
## 1 1 k 44.315 4.612 9.6090 1.618e-10
## 2 1 b 4.947 1.973 2.5078 1.800e-02
## 3 2 k 40.004 3.977 10.0579 5.788e-11
## 4 2 b 7.074 1.524 4.6432 6.832e-05
## 5 3 k 55.617 5.161 10.7761 1.179e-11
## 6 3 b 1.178 1.804 0.6529 5.189e-01
## 7 4 k 42.556 3.103 13.7153 3.303e-14
## 8 4 b 4.824 1.053 4.5803 8.132e-05
## 9 5 k 51.902 5.271 9.8472 6.540e-11
## 10 5 b 3.222 1.945 1.6561 1.081e-01
## 1 1 k 40.883 4.260 9.598 1.179e-10
## 2 1 b 6.434 1.531 4.202 2.185e-04
## 3 2 k 52.013 4.797 10.842 1.022e-11
## 4 2 b 3.119 1.827 1.707 9.857e-02
## 5 3 k 45.412 3.669 12.377 2.565e-13
## 6 3 b 4.055 1.311 3.093 4.256e-03
## 7 4 k 43.474 4.358 9.975 6.986e-11
## 8 4 b 5.539 1.615 3.431 1.829e-03
## 9 5 k 34.995 3.585 9.763 1.135e-10
## 10 5 b 8.428 1.298 6.491 4.181e-07
## .. ... ... ... ... ... ...
</code></pre>

@@ -296,9 +296,9 @@ <h1>Tidy bootstrapping with dplyr+broom</h1>

<pre><code>## Source: local data frame [2 x 3]
##
## term low high
## 1 b 1.296 7.449
## 2 k 37.423 55.670
## term low high
## 1 b 0.08649 6.86
## 2 k 38.08916 60.98
</code></pre>

<p>Or you can use histograms to give you a more detailed idea of the uncertainty in each estimate:</p>
@@ -307,7 +307,7 @@ <h1>Tidy bootstrapping with dplyr+broom</h1>
ggplot(bootnls, aes(estimate)) + geom_histogram(binwidth=2) + facet_wrap(~ term, scales=&quot;free&quot;)
</code></pre>

<p><img 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EAAAcsCgR7BT5w4UfSfDm+88YZowq+rq5OZM2fK4sWLZcWKFeYofvbs2ZbDpjoEEEAAAQSSLRBogs+mO//88zOjeppy/vz55vR8Oc+6zRTICwQQQAABBBDIEQj0FH1OTQVGSO4FUJiEAAIIIICADwKhJngf2k8RCCCAAAIIIFBAgARfAIVJCCCAAAIIxF2ABB/3HqT9CCCAAAIIFBAgwRdAYRICCCCAAAJxFyDBx70HaT8CCCCAAAIFBEjwBVCYhAACCCCAQNwFSPBx70HajwACCCCAQAEBEnwBFCYhgAACCCAQdwESfNx7kPYjgAACCCBQQIAEXwCFSQgggAACCMRdgAQf9x6k/QgggAACCBQQIMEXQGESAggggAACcRcgwce9B2k/AggggAACBQSsPS62QN1MQgABBBAoItDc3FxkCWYjUFiAI/jCLkxFAAEEEEAg1gIk+Fh3H41HAAEEEECgsAAJvrALUxFAAAEEEIi1AAk+1t1H4xFAAAEEECgsQIIv7MJUBBBAAAEEYi1Ago9199F4BBBAAAEECguQ4Au7MBUBBBBAAIFYC5DgY919NB4BBBBAAIHCAiT4wi5MRQABBBBAINYCJPhYdx+NRwABBBBAoLAACb6wC1MRQAABBBCItQAJPtbdR+MRQAABBBAoLECCL+zCVAQQQAABBGItQIKPdffReAQQQAABBAoLkOALuzAVAQQQQACBWAuQ4GPdfTQeAQQQQACBwgLVhSczFQEE0ijQ1dUld9xxh1xwwQXS2Ngo27ZtkzvvvDND0dzcLNOmTcuM8wIBBKIrQIKPbt/QMgSsCmzatEkeeughWb9+vfT29pq6W1paZOLEiTJnzhwzXlVVZbVNVIYAAt4FOEXv3Y41EUiUwNatW+Xss8+WvfbaKxOXJvjq6mp5/vnn5YMPPpDKytxdRnd3t2T/cz4YZArgBQIIhCbAEXxo9FSMQLQEJk2atFuDOjo6pKKiQvbee29ZtGiRzJ07V/bbb7/McldeeaXoMs5w7rnnyowZM5zRfv8OHjy43/lxnDlgwAAZPnx4LJo+YcIE1+0cNWqU62XjtODYsWMj31z9mszrQIL3Ksd6CKRA4NRTT81EqUfnK1euzEnwN954Y2a+vti8ebO88847OdPyR5zE3tramj/L9/Gamhrp6ekxZxl8LzyvQE3sbW1tsn379rw50Rwt1k9Oq/WMzpYtW6S9vd2ZFNjfuro6K/VoAJrc9esoPQMV9FBOXPqhcY899vDURBK8JzZWQiAdAkuWLJHDDjtMxo8fLxs3bjRH8tmR19bWZo+aU/huTtPrMm6Wyyncw4hTj426tHlOfR6aan0VtybOcs7foBtqqx6NQ+uyVZ+terL7hwSfrcFrBBDIEdDT7U888YT5Hl6PThcsWJAznxEEEIiuQGwSvH4PaGvQumzWlx1XGPWGGW927G5e++njZ1lu2u4sE4Z3fp16NNFX/JdddpnTVHMa85JLLjGnnuvr6zPTeYEAAtEXiEWC1yt39bs0G4Pu9PKvFLZRr16prHXbijM7Jpu+2fV6ee2Xj/7cy6+ySo1D69XvhW0Ozs/bnNOE/SX4Qu0iuRdSYRoC0RaIRYLXnWH2lbpBkuqOUBNeZ2dnkNXsVrbucPWfrTizG6DxhlFvdhvcvvarnWHGrDHYTvDqq9u0k+A1fgYEEEi2AO/yZPcv0SGAAAIIpFSABJ/SjidsBBBAAIFkC5Dgk92/RIcAAgggkFIBEnxKO56wEUAAAQSSLUCCT3b/Eh0CCCCAQEoFSPAp7XjCRgABBBBItgAJPtn9S3QIIIAAAikVIMGntOMJGwEEEEAg2QIk+GT3L9EhgAACCKRUgASf0o4nbAQQQACBZAuQ4JPdv0SHAAIIIJBSARJ8SjuesBFAAAEEki1Agk92/xIdAggggEBKBUjwKe14wkYAAQQQSLYACT7Z/Ut0CCCAAAIpFSDBp7TjCRsBBBBAINkCJPhk9y/RIYAAAgikVIAEn9KOJ2wEEEAAgWQLkOCT3b9EhwACCCCQUgESfEo7nrARQAABBJItQIJPdv8SHQIIIIBASgVI8CnteMJGAAEEEEi2AAk+2f1LdAgggAACKRUgwae04wkbAQQQQCDZAiT4ZPcv0SGAAAIIpFSABJ/SjidsBBBAAIFkC5Dgk92/RIcAAgggkFIBEnxKO56wEUAAAQSSLUCCT3b/Eh0CCCCAQEoFSPAp7XjCRgABBBBItgAJPtn9S3QIIIAAAikVIMGntOMJGwEEEEAg2QIk+GT3L9EhgAACCKRUgASf0o4nbAQQQACBZAuQ4JPdv0SHAAIIIJBSARJ8SjuesBFAAAEEki1Agk92/xIdAggggEBKBUjwKe14wkYAAQQQSLYACT7Z/Ut0CCCAAAIpFagOOu5XX31VVq5cKb29vXLGGWfInnvuKV1dXfLII4/Ixo0bZfLkyTJr1qygm0H5CCCAAAIIpEog0CP4nTt3yvLly2XBggXS3NwsS5YsMbjLli2TUaNGycKFC02SX7VqVarQCRYBBBBAAIGgBQI9gm9sbJTLLrtMuru7Ze3atTJ48GATz5o1a2TevHlSVVUl06dPl9WrV8uUKVPMPD3S37JlS07cOq26OtCmZuqrrKwU/ad12hzUoqKiwlqc2bFp3bZ8s+v18tqvdoYZs8bQ09PjJXzP62i8tuv03FhWRAABXwSsZE09il+xYoXMmTPHNLq1tVU0+eugf3fs2GFe63/t7e1y0003Zcb1xUUXXSQTJ07MmRbUiCZZHWwneK1TP1jstdde+tLqoDGHEa+XIP3yCTPmkSNHegm9rHXC3K7LajgrI4CAZwErCf7444+Xo48+Wn72s5/JtGnTpKGhwSTympoa6ejokKampkwA9fX18otf/CIzri/Wr18v7733Xs60oEb0SEcTbWdnZ1BVFCxXLXTH39LSUnB+kBPVvK2tLcgqfCvbr+0grJjHjx8v77//vvWj6draWrNNOx/kJkyY4FufUBACCERTINDv4D/88EO57777MpHrTlUT6JgxY2TdunVmup66Hz16dGYZXiCAAAIIIIBA+QKBHsEPHz5cBg0aJHfffbc5UtcL7TTBz5w5UxYvXmxO2+uR6+zZs8uPhBIQQCB0Af0qQN/T/Q16hkyHYsv1V4bbec4ZOadOt+t5WU7r0PpsxOWlffnruG2n9qmtuNTQbbvy4/EyrnXZ2jZsxuVYBJrgtZLTTz/dJHcNzvkecMiQITJ//nwzXU8dMiCAQDIE9CsAvai2v0GXcbNcf2W4naf7HL240MYFhhqT1lMsfrdtD3q5UtppKy69ALWUdpVrpHXZqM92XI5L4AleK+orifc13WkcfxFAIH4CxZKpk+CLLedH5Emtyw8bt/62Dd22yy8DG/U5H/78aHMpZQT6HXwpDWFZBBBAAAEEEPBPgATvnyUlIYAAAgggEBkBEnxkuoKGIIAAAggg4J8ACd4/S0pCAAEEEEAgMgIk+Mh0BQ1BAAEEEEDAPwESvH+WlIQAAggggEBkBEjwkekKGoIAAggggIB/AiR4/ywpCQEEEEAAgcgIkOAj0xU0BAEEEEAAAf8ESPD+WVISAggggAACkREgwUemK2gIAggggAAC/gmQ4P2zpCQEEEAAAQQiI2DlYTORiZaGiD6ylwEBBBBAIPkCHMEnv4+JEAEEEEAghQIk+BR2OiEjgAACCCRfgASf/D4mQgQQQACBFAqQ4FPY6YSMAAIIIJB8ARJ88vuYCBFAAAEEUihAgk9hpxMyAggggEDyBUjwye9jIkQAAQQQSKEACT6FnU7ICCCAAALJFyDBJ7+PiRABBBBAIIUCJPgUdjohI4AAAggkX4AEn/w+JkIEEEAAgRQKkOBT2OmEjAACCCCQfAESfPL7mAgRQAABBFIoQIJPYacTMgIIIIBA8gVI8MnvYyJEAAEEEEihAAk+hZ1OyAgggAACyReoTn6IRIgAAgjYETjqqKPsVGS5lmOPPdZVjUuXLnW1HAvZEeAI3o4ztSCAAAIIIGBVgARvlZvKEEAAAQQQsCNAgrfjTC0IIIAAAghYFSDBW+WmMgQQQAABBOwIkODtOFMLAggggAACVgVI8Fa5qQwBBBBAAAE7AiR4O87UggACCCCAgFUBErxVbipDAAEEEEDAjkAsbnRTVVUl9fX1VkQqKipE/2mdNgetT+u1FafN2Pysyy+f6urq0Kw1hp6eHj9Zipal21dl5a7P893d3da38aKNZAEEEPBVIBYJXndGHR0dvgbeV2HOjrCzs7OvRQKZXlNTI729vdLW1hZI+Ukp1C8fTbJ+lVWqrdZrO8HX1taKbtO6jelg+wNsqUYsjwAC5Qvs+khfflmUgAACCCCAAAIRESDBR6QjaAYCCCCAAAJ+CpDg/dSkLAQQQAABBCIiQIKPSEfQDAQQQAABBPwUIMH7qUlZCCCAAAIIRESABB+RjqAZCCCAAAII+CngKcFv3rx5tzZs2rRJCk3fbUEmIIAAAggggEDgAiUleP39rv477rjjzF9nfOfOnXLppZfKk08+GXiDqQABBIIT6Orqkttvv130Pa2Dji9atEhuueUWeeqpp4KrmJIRQMB3gZIS/KmnnioNDQ3yxhtvmL/6Wv81NTXJSy+9JEceeaTvDaRABBCwI6Bn4W677TZ55513MjfEWbZsmYwaNUoWLlwoGzdulFWrVtlpDLUggEDZAiXdye7ZZ58VvavcueeeKw888ECmcr0FZvZtMDMzeIEAArER2Lp1q5x99tnmiN1p9Jo1a2TevHnmznfTp0+X1atXy5QpU5zZovOz78qnd8yrq6vLzC/0wrmLXrHlCq1b6jS9JbHevU/3W0EPcdsHuvUvJS63ZfbVF84dPfua7/d03V6zt1+/y3fKKycuvYW516GkBK8V6RvmoYce8lof6yGAQEQFJk2atFvLWltbpbGx0UzXvzt27MhZ5rHHHjO3wHUmnnLKKXLAAQc4owX/Ogled65BD7rPcm7PG3Rdum+M0zBs2DBXzXX6y83Cbsvsqyyb/aV1DRkypK+m+Dq9nLg++eQTz23xtEW++OKL8pOf/EQ2bNiQ8+nn1ltvlS9+8YueG8OKCCAQLQH9Cq69vV30CESfB6Ffx2UP3//+97NHzWn89evX50zLH9GdqiZd/fAQ9KDt1iM0G0fwI0aMCDocX8sv1k9OZXvttZfzsuhft2X2VZDNZ0SMGzfObK82to1y4sp/z/VlV2i6pwR//vnny4UXXmgutsv+1FroCKBQpUxDAIF4CIwZM0bWrVsn06ZNk7Vr18ro0aPj0XBaiQACUnKC10/e+l3dD37wA/N4UwwRQCC5AjNnzpTFixfLihUrzFH87NmzkxsskSGQMIGSE7x+l9Dc3Cz33HOPnHPOOWLje7SEmRMOApEWuOyyyzLt09Pp8+fPN6fnea9nWHiBQCwESvqZnBPRtm3bzJteL6jQK2qdf3/4wx+cRfiLAAIJEiC5J6gzCSU1AiUfwavMtddeKz/60Y92Q+I7+N1ImIAAAggggEAoAp4S/FVXXWWuoM9v8c033ywnnXRS/mTGEUAAAQQQQMCygKcEr0fw+pMZHfSv3tnu4YcflsMPP9xy86kOAQQQQAABBAoJeErwRxxxRE5ZeqVtS0uLPPfcc/KlL30pZx4jCCCAAAIIIGBfwNNFdvnN1J/O6f2rP/744/xZjCOAAAIIIIBACAKejuD1e3Z98IQOmtz1MbF6xyh9ChUDAggggAACCIQv4CnB33jjjeb2lU7z9XaWeqcrfkrjiPAXAQQQQACBcAU8JXi9mE7v76yn5fUiu/3224+nyYXbj9SOAAIIIIBAjoCn7+Dfeust0UdHamL/7Gc/K3vssYfccccdOQUzggACCCCAAALhCXhK8BdddJHMmTPHfA+/ZcsWefrpp83Nb958883wIqFmBBBAAAEEEMgIeDpFr4l86dKlme/cjzrqKLngggvMAykOPPDATOG8QAABBBBAAIFwBDwdwesFda+//npOi5cvXy7Dhw/PmcYIAggggAACCIQj4OkI/lvf+pa5Je1xxx0n48aNM0fzgwcPlrlz54YTBbUigAACCCCAQI6ApyN4vVud3rVuxowZ5jS9Phv+xRdfNL+FzymdEQQQQAABBBAIRcDTEfzWrVtlyZIlct1110lVVZV885vfNL+D1yvqGRBAAAEEEEAgfAFPR/B6Fb3ellZ/C6/DCSecIGeddZboFfUMCCCAAAIIIBC+gKcj+JdfflnefvttqaioMBFoctcr65944gk5//zzw4+KFiCAAAIIIJByAU9H8E1NTfLKK6/k0L3wwgsycODAnGmMIIAAAggggEA4Ap6O4K+//no58cQT5dBDD5WxY8fK888/LwcccABX0YfTh9SKAAIIIIDAbgKeEvxpp50mK1euNDe22bFjh7nYbsKECbsVzgQEEEAAAQQQCEfAU4LXpk6ePNn8C6fZ1IoAAggggAAC/Ql4+g6+vwKZhwACCCCAAALhC5Dgw+8DWoAAAggggIDvAiR430kpEAEEEEAAgfAFPH8H77bpr776qrz22mvmjnfHHHOMTJo0Sbq6uuSRRx4xj5vV7/JnzZrltjiWQwABBBBAAAEXAoEewesV9voTOn2UrN4MR29v29vbK8uWLZNRo0bJwoULTZJftWqVi6ayCAIIIIAAAgi4FQj0CL62tlb0trbV1dXS3d1tbm+rCX7NmjUyb948c1Q/ffp0Wb16tUyZMsW0uaOjQx566KGc9h977LEyYsSInGlBjejd+fSfcxveoOrJL1frrKys5JG7+TB54349klifoaA3bApjGDZsmPmga7Nu3bb0vaf/GBBAIB0CgSb4mpoa84Q5PSX/4IMPysknn2ySWGtrqzQ2Nhph/atH+s6gO959993XGTV/dVp7e3vOtKBGnESrH0hsDhpjQ0ODtThtxuZnXX5tB/qhU7dL28OAAQNMH9tOtLp9ZW/TYX24se1NfQikWSDQBK+wekR+zz33mN/M65G4Dk4i0w8AOj97Z6M7oqOPPtos5/y3fv162bZtmzMa6F+tX492Ojs7A60nv3C1UAdbcebXH5dxv3zq6+ulra3NethDhw6V7du3Wz9DpGfTdJt2PljoWQQGBBBItkCg38Hrae677rpLDjvsMPPEOYdyzJgxsm7dOjO6du1aGT16tDOLvwgggAACCCDgg0CgR/Cvv/66+b5dj7qee+4509xLL71UZs6cKYsXLza3utUj19mzZ/sQCkUggAACCCCAgCMQaILXh9Hov/xBv3efP3++OT2vpw4ZEEAAAQQQQMBfgUBP0RdrKsm9mBDzEUAAAQQQ8CYQaoL31mTWQgABBBBAAIFiAiT4YkLMRwABBBBAIIYCJPgYdhpNRgABBBBAoJgACb6YEPMRQAABBBCIoQAJPoadRpMRQAABBBAoJkCCLybEfAQQQAABBGIoQIKPYafRZAQQQAABBIoJkOCLCTEfAQQQQACBGAqQ4GPYaTQZAQQQQACBYgIk+GJCzEcAAQQQQCCGAiT4GHYaTUYAAQQQQKCYAAm+mBDzEUAAAQQQiKEACT6GnUaTEUAAAQQQKCYQ6ONii1XOfAQQSJZARUWF1NfX9xtUVVWVmV9suX4LcTmzsrJSent7zT+Xq3hezInLcwGWV3Trr4ZuB7dl9lVedXV10e2nr3W9TK+rq5Oenh4vq5a0ju24nMaR4B0J/iKAQNkCmkzb2tr6LaehocEk3GLL9VuIy5k1NTVmB97d3e1yDe+L2ajDe+t2X9OtfykJ0G2Zu7fmf1P0A0K5ZfRVdqHp7e3tYqPfyomrqampUNNdTXP/0cxVcSyEAAIIIIAAAlEQIMFHoRdoAwIIIIAAAj4LkOB9BqU4BBBAAAEEoiBAgo9CL9AGBBBAAAEEfBYgwfsMSnEIIIAAAghEQYAEH4VeoA0IIIAAAgj4LECC9xmU4hBAAAEEEIiCAAk+Cr1AGxBAAAEEEPBZgATvMyjFIYAAAgggEAUB7mQXhV6gDQgggEACBJqbm11HsXTpUtfLsqA3AY7gvbmxFgIIIIAAApEWIMFHuntoHAIIIIAAAt4ESPDe3FgLAQQQQACBSAuQ4CPdPTQOAQQQQAABbwIkeG9urIUAAggggECkBUjwke4eGocAAggggIA3ARK8NzfWQgABBBBAINICJPhIdw+NQwABBBBAwJsACd6bG2shgAACCCAQaQESfKS7h8YhgAACCCDgTYAE782NtRBAAAEEEIi0AAk+0t1D4xBAAAEEEPAmEJuHzVRW2vksUlFRIfrPVn1Ot4VVr1N/XP761S9h9LFj7FcMTnlu/jrx9vb2msX1r05jQACB5ArEIsHrDrG62k5TnR2h7S534nP+2q4/LvWddNJJgTb1j3/8Y6Dla+Haxz09PYHXk11BVVWVGSXBZ6vwGoFkC9jJmmUa6s6wo6OjzFLcra47Qv1A0dnZ6W4Fn5bSHa/+sxWnT81OXDE2/LUO2wleO0q3aSfBh3EWIXEbCwEhEHEBO+e9I45A8xBAAAEEEEiaAAk+aT1KPAgggAACCHwqQIJnM0AAAQQQQCCBAiT4BHYqISGAAAIIIECCZxtAAAEEEEAggQIk+AR2KiEhgAACCCBAgmcbQAABBBBAIIECJPgEdiohIYAAAgggQIJnG0AAAQQQQCCBAiT4BHYqISGAAAIIIECCZxtAAAEEEEAggQIk+AR2KiEhgAACCCBAgmcbQAABBBBAIIECJPgEdiohIYAAAgggQIJnG0AAAQQQQCCBAiT4BHYqISGAAAIIIECCZxtAAAEEEEAggQIk+AR2KiEhgAACCCBAgmcbQAABBBBAIIECJPgEdiohIYAAAgggQIJnG0AAAQQQQCCBAiT4BHYqISGAAAIIIECCZxtAAAEEEEAggQLVCYyJkBBAwCeBbdu2yZ133pkprbm5WaZNm5YZ5wUCCERXgAQf3b6hZQiELtDS0iITJ06UOXPmmLZUVVWF3iYagAAC7gRI8O6cWAqBVApogq+urpbnn39epk6dKnvvvXeOw7Jly6SrqyszTT8MDB8+PDNe6EVdXZ2ZPGjQoEKzfZ1WWVkpvb295p+vBRcorKampsDU6E5y6x/Uh7pC9eu25mwfQctVVFTIwIEDpaenJ+iqzHvIVlzZwZDgszV4jQACOQIdHR2iO0JN7IsWLZK5c+fKfvvtl1nmo48+ks7Ozsz4uHHjpFhC0PJ0KLZcptAyXmiC16GcnfiMGTPKaEF0V3Xr7/SX35EUql+n6QcyW4NuH0HFlx1DOXGVs+2S4LN7gdcIIJAjcOqpp2bGdce7cuXKnAR/5plnZubri40bN8qWLVtypuWPDBkyxOzEW1tb82f5Pq5H1bqD7O7u9r3suBdYrJ+c+II68ixUf319vbS1tTlVB/q3qalJdBu0sW2UE5e20+vAVfRe5VgPgRQILFmyRN555x0TqSbv/FP0KSAgRARiK8ARfGy7joYjELyAnp5+4oknzHeIemS1YMGC4CulBgQQ8EWABO8LI4UgkEyBsWPHyiWXXGJOm+ppRgYEEIiPAKfo49NXtBSB0ARI7qHRUzECngVI8J7pWBEBBBBAAIHoCpDgo9s3tAwBBBBAAAHPAiR4z3SsiAACCCCAQHQFSPDR7RtahgACCCCAgGcBErxnOlZEAAEEEEAgugIk+Oj2DS1DAAEEEEDAswAJ3jMdKyKAAAIIIBBdASsJfvPmzXLHHXdkFPTpU/rgiltuuUWeeuqpzHReIIAAAggggIA/AoEn+DfffFPuvvtu0STvDPqIyVGjRsnChQvNwylWrVrlzOIvAggggAACCPggEPitatvb2+Xiiy+W2267LdPcNWvWyLx588zjIqdPny6rV6+WKVOmmPn65KcNGzZkltUXOs3Ws5b1sX42Hh+YE+CnI/ocZK3XVpz59TP+PwEb/s4Tzmya6/alg81HcdqMj7oQQGB3gcATvCbw/Mfx6SP6GhsbTWv0744dOzIt02dL//rXv86M64vzzjtPJkyYkDMtyBFNtGHsCPXZxCNHjgwyNMouImDDf8SIEUVa4f/ssLZp/yOhRAQQcCsQeIIv1JCGhgbRI3s9kuno6JDs593qs4evu+66nNXWr18vLS0tOdOCGtEjeE20+kHD5qAWmlxsxWkztjjVFbT/+PHjRbdnPStlc6itrTXbtPPB1eYHZptxUhcCCOwSCPw7+F1V7Xo1ZswYWbdunZmwdu1aGT169K6ZvEIAAQQQQACBsgVCOYKfOXOmLF68WFasWGGO4mfPnl12IBSAAAIIIIAAArsErCR4Pe39wx/+MFPrkCFDZP78+eb0vJ46ZEAAAQQQQAABfwWsJPi+mkxy70uG6QgggECyBZqbm10HuHTpUtfLsuAugVC+g99VPa8QQAABBBBAIAgBEnwQqpSJAAIIIIBAyAIk+JA7gOoRQAABBBAIQiDU7+CDCCjOZU6dOjXOzaftCCCAAAIREuAIPkKdQVMQQAABBBDwS4AE75ck5SCAAAIIIBAhARJ8hDqDpiCAAAIIIOCXAAneL0nKQQABBBBAIEICJPgIdQZNQQABBBBAwC8BErxfkpSDAAIIIIBAhARI8BHqDJqCAAIIIICAXwIkeL8kKQcBBBBAAIEICZDgI9QZNAUBBBBAAAG/BEjwfklSDgIIIIAAAhESIMFHqDNoCgIIIIAAAn4JkOD9kqQcBBBAAAEEIiRAgo9QZ9AUBBBAAAEE/BIgwfslSTkIIIAAAghESIAEH6HOoCkIIIAAAgj4JUCC90uSchBAAAEEEIiQQHWE2kJTEEAAAQQsCTQ3N1uqiWrCEuAIPix56kUAAQQQQCBAARJ8gLgUjQACCCCAQFgCJPiw5KkXAQQQQACBAAVI8AHiUjQCCCCAAAJhCZDgw5KnXgQQQAABBAIUIMEHiEvRCCCAAAIIhCVAgg9LnnoRQAABBBAIUIAEHyAuRSOAAAIIIBCWAAk+LHnqRQABBBBAIEAB7mQXIC5FI5A2gYqKCqmvr+837KqqKjO/2HL9FuJyZmVlpfT29pp/LldhsQgKBLWt1NXVSU9PT+ARV1dXF31fBNEIEnwQqpSJQEoFNJm2tbX1G31DQ4NJuMWW67cQlzNramrMDry7u9vlGiwWRYGgtpX29naxsW3oBxSvMTQ1NXnuEk7Re6ZjRQQQQAABBKIrQIKPbt/QMgQQQAABBDwLkOA907EiAggggAAC0RUgwUe3b2gZAggggAACngVI8J7pWBEBBBBAAIHoCpDgo9s3tAwBBBBAAAHPArH4mZz+bjao30Hmy+nvePWf81vd/PlBjduuL6g44l6uje1M67Dx29vsvtDtS38T7gz60yC2OUeDvwgkUyAWCV53Rh0dHVZ6wNkRdnZ2WqnPqUR/r8sQvoDX36qW0nKtw3aCr62tFd2m9XfqOpDcS+kxlkUgngK7PtLHs/20GgEEEEAAAQQKCMTiCL5Au5mEAAIIeBZobm72vC4rIhAXAY7g49JTtBMBBBBAAIESBEjwJWCxKAIIIIAAAnERIMHHpadoJwIIIIAAAiUIkOBLwGJRBBBAAAEE4iJAgo9LT9FOBBBAAAEEShAgwZeAxaIIIIAAAgjERYAEH5eeop0IIIAAAgiUIECCLwGLRRFAAAEEEIiLAAk+Lj1FOxFAAAEEEChBgARfAhaLIoAAAgggEBcBEnxceop2IoAAAgggUIIACb4ELBZFAAEEEEAgLgIk+Lj0FO1EAAEEEECgBAESfAlYLIoAAggggEBcBEjwcekp2okAAggggEAJAiT4ErBYFAEEEEAAgbgIkODj0lO0EwEEEEAAgRIESPAlYLEoAggggAACcREgwcelp2gnAggggAACJQiQ4EvAYlEEEEAAAQTiIlAdl4bSTgQQSKdAc3NzOgMn6oxAKdvA0qVLM+v59SLs+r3GwRG8VznWQwABBBBAIMICJPgIdw5NQwABBBBAwKsAp+i9yrFeIgVKORXnFeCZZ57xuirrIYAAAq4FOIJ3TcWCCCCAAAIIxEeABB+fvqKlCCCAAAIIuBYgwbumYkEEEEAAAQTiI0CCj09f0VIEEEAAAQRcC5DgXVOxIAIIIIAAAvERIMHHp69oKQIIIIAAAq4FSPCuqVgQAQQQQACB+AiQ4OPTV7QUAQQQQAAB1wLc6MY1lYiNm6CU0BwWRQABBBBAoE8BjuD7pGEGAggggAAC8RUgwce372g5AggggAACfQqQ4PukYQYCCCCAAALxFSDBx7fvaDkCCCCAAAJ9CpDg+6RhBgIIIIAAAvEVCOUq+q6uLnnkkUdk48aNMnnyZJk1a1Z8BWk5AgkW4L2a4M4ltMQLhHIEv2zZMhk1apQsXLjQJPlVq1YlHpoAEYijAO/VOPYabUbgfwKhHMGvWbNG5s2bJ1VVVTJ9+nRZvXq1TJkyxbSoo6ND7r333pz+Ofnkk2XkyJE504IaqaioMEX39vYGVQXlplxg+PDh1gUqKyulp6en5Hr7e69qYfpe1fesMxx11FGyzz77OKMF/9bU1JjptbW1BeczEYFyBPbcc09Xq+u+Xt+Lfu/rC9Xv9f2ngWS/v1wFlrVQKAm+tbVVGhsbTTP0744dOzJN0qR/yCGHZMb1RXV1dc4yOTN9HtGO0I7v7u7ereQXX3xxt2l+TdC4Bw0aJFu2bPGrSNfl6A63s7PT9fJ+LThgwADz5tq5c6dfRbouJ6yYdYeyefNm33cqxQLX95Bu087OzHn/FVuvv/eqrnvQQQflvFfy38+Fytd+1yH7fV9oOWdaOe87fV9pzF4+3Dj1u/07cOBAszNua2tzu4rn5Wxuv4MHDzZ9ZWMf4Udcbrer+vp60X2Pm22jlG2wUP3lxKXbsNchlATf0NAg7e3tokHrp5OmpqZM+zWYI444IjOuL9avX+96Z5CzoocRrV+TvI2NObt5aqE7iEIbR/ZyQbzWDd3GTim/7XV1dSY5pClmTfBudyr5XuWM69GybtNOgh8xYoSr4vp7r2oBhx9+eE45el1Nsf7Utmg7ii2XU7DHEX1f6Q680Ad2j0X2uZp+uNH9mo249L2jddkYdL+k+wcb+wib+6Jhw4aZ96KNbaOcuLLzY6n9Hcp38GPGjJF169aZtq5du1ZGjx5dartZHgEELAjwXrWATBUIBCQQyhH8zJkzZfHixbJixQpzFD979uyAwqNYBBAoR4D3ajl6rItAuAKhJPghQ4bI/Pnzzel5LrQJdwOgdgT6E+C92p8O8xCItkAop+gdEpK7I8FfBKItwHs12v1D6xAoJBBqgi/UIKYhgAACCCCAQPkCJPjyDSkBAQQQQACByAmQ4CPXJTQIAQQQQACB8gVI8OUbUgICCCCAAAKREyDBR65LaBACCCCAAALlC5DgyzekBAQQQAABBCInQIKPXJfQIAQQQAABBMoXIMGXb0gJCCCAAAIIRE6ABB+5LqFBCCCAAAIIlC9Agi/fkBIQQAABBBCInAAJPnJdQoMQQAABBBAoX4AEX74hJSCAAAIIIBA5ARJ85LqEBiGAAAIIIFC+QEXvp0P5xQRbwubNm6WzszPYSrJKr6ioENss3d3d8tFHH8nw4cOzWmLnZWVlpfT09NipLKuW7du3i1oPGDAga6qdl2HFvGnTJtPHWr/NIX+bHjVqVCDV6zbc3t7eb9na7zo0NTX1u5wfM/Pj9qPMvsr4+OOPpa6uTurr6/taxLfpNrffLVu2yB577CE1NTW+tb+vgmzG9cEHH8jQoUOlqqqqr+b4Nr2cuBoaGmTQoEGe2hLK8+BLbemwYcNKXSV2y2/YsEEWLVokN9xwQ+za7rXBjz76qDQ2NsqsWbO8FhG79W666Sa59tprZeDAgbFru5sG6/Pjiw2PP/64+WA3d+7cYovGav7vfvc7OeCAA+TII4+MVbuLNfb++++X008/XcaOHVts0VjN/+UvfylXX321SfKxangJjbV7GFFCw1gUAQQQQAABBLwLkOC927EmAggggAACkRUgwUe2a9LRMP2ONG1DGmPO7uOkxp/UuLTvkhpbUuNy3m+xuMjOaSx/EUAAAQQQQMCdAEfw7pxYCgEEEEAAgVgJkOBj1V00FgEEEEAAAXcCsfiZnLtQ4rvUhx9+KI899pjs2LFDjj32WDnkkEPiG4zLlj/99NPyz3/+0yytPxlbsGCByzXjt5jGuW7dOpkzZ45pfNr6+91335Vnn31WPvnkE7N9T5s2Tbq6uuSRRx6RjRs3yuTJk2P9U8mlS5ea34nrz+OSEFeh92YSttl//etf8uqrr4rec+S0004zP49LQlz97RFJ8P3pWJr38MMPm52/3nThtttuMzs8/X14kgd9s1188cVSW1ub2At4tP+eeeYZeemll2Tq1KmZ7kxbf+vv3r/+9a+bGxr9/Oc/lwMPPFCWLVsmerOdr3zlK/LAAw/IqlWrZMqUKRmjuLxYu3atrFixQk444QTT5CTEVei9Gfdt9r333pPly5ebfc5//vMfefnll+WUU06RuMdV7H3CKfpiQhbmb9261dxEQu/ots8++8g777xjodbwqtC75unZijfeeMN8og7jLnq2otc+1SSWPaStvxcuXCh6AxzdyeoHOr2r15o1a+TQQw81dxGbPn26rF69OpsoFq/1jn2///3vZebMmZn2xj2uvt6bcd9m9Syanhl95ZVXRO8Mp8ldh7jHldnw+nhBgu8DxtbknTt3SnX1rhMpeuSuyS/Jw7Zt28xtSvVWpXrb0t/85jeJDVdP22pCc4Y09rfGrkfoS5Yskf3339/cFrm1tdXcxVDnxXWb13j0Loz6ocUZ4h5XofdmErZZvY2wnknTD2V6x8EXX3xRkhCXs9319XfXnqevJZgeqIDeu7qjoyNTh762cY/uTIUhvND7Kn/3u981p61PPPFEaWtrE30DpmFIY39rv+rp9yuuuEL0vuZvvfWWOYpy7lkfx23+3//+tzkjodcXaDxvv/226O2m9egwznEVem/q+zPu+yj9EKZnjI4++miZN2+eOUWfhvciCT7krKIPOtCNT4/a9QE3usMI6kEgIYeaqV4vbNHvXXXQi5J056EPs0jDkLb+1lO+el2JkyB0XPt6zJgx5sJD7XP9Hnv06NGx6n59PoZ+765nH/T9qw9i0TNxcY+r0Htz8ODBsd9H6X309XS8Ds5DgdLwXuRGNxHYregRgF65qslOL0A66aSTItCqYJuwePFi8/Q8faLT8ccfL5/73OeCrTDE0vX0tH4H+OUvf9m0Im39rd97rly50iTAkSNHij5kRp86p9uAPiVSk+OFF14Y24stnVO/mvCTEFeh92bct1m9cv7JJ580Xwm+//775ihek37c4yq2WyPBFxOyON/Z2VmsMtSqNF79ftrG4xpDDbSPytPU33rkrjvZ/EeO6pF99nfYfVDFbnLc4+rrvRn3bVb7RbfB/FvUxj2uvt4gJPi+ZJiOAAIIIIBAjAX4Dj7GnUfTEUAAAQQQ6EuABN+XDNMRQAABBBCIsQAJPsadR9MRKFdAb7Oqd1/T4aqrrpIf//jH5RaZWf+nP/1p5ur5zEReIICANQG+g7dGTUUIRE/gjDPOkNNPP13OOecc0Zu06MVH+lvocge9oE5/Nqb3n6+vry+3ONZHAAEPAhzBe0BjFQSiKKD3Ubj++uvNb7H1d+U33HCDubeCtvXBBx+UcePGif5+W3+upz/nuueee8y98vXI/be//a3cfffdcu+995rQ9Pard911l0ycONHcpOZPf/qTfO9735M999xTvvrVr4re8UyHN9980/zMUT8UjB8/Xn71q1+Z6Weffbb5e/DBB4v+tlp/DqkfJvQ31TpN7wvOgAACwQqQ4IP1pXQErAloEtdErb/31acTLlq0SP7617+aOwV++9vflieeeMLcA15vqqS3B/7a174mxx13nFxzzTUm6W/atMkkY22w3lP90UcfNQ9SOfXUU0XvOKh3WHz99delpaXFfDDQ5fQhMnq71vXr15vk7tytTj8c6PDCCy+YDxXf+MY3zJkBvSeA3sXwggsuMPP5DwEEghMgwQdnS8kIWBW4//77TeLcd999zT3fNalqstfT7vo7dP2uXW+jqsn/6quvFr1Vp/4mWB+Io6/zh0svvdScDTjrrLPMTZg0ee+9997mka9/+ctfzOJ33nmnObLX9SdMmGBu1apH686dCfWIXc8WPPXUU3LllVeaO7+deeaZppx//OMf+VUyjgACPgqQ4H3EpCgEwhTQO3TdfPPNJrnrQ1309d/+9jeTvPWxmPoBQE/d6xG53ku92KDJXAe9v7q+dpK23phG77qogyZzvb+3nrq//PLLzc1s9MNE9qBH/PohQ+/0pu3Sf/rITj3tz4AAAsEJ7HqMWXB1UDICCFgQmDFjhhxzzDGyYMECU5s+qU8vdtOEqw/a+Pvf/y76rG89JX/JJZfIs88+22+rsp9yWGhBfXCMHo3rVwP6oUGP4vXe7HotQPagD5rR7+j1dr3Dhw83s/SDgR8X82XXw2sEEMgV4Ag+14MxBGIrcNppp5mL5PSUuCZZ/X5cL3rTi9ymTZtmvjufOnVq5lnYGqienter570M+gFCB/1+Xq+U1+/89cljettPvf2wJnx9sIce8etFe7fffrv5sKFPXdNnLuj38QwIIBCcAEfwwdlSMgJWBfRit4ceesh8Fz5ixAg54IADzG/b9RS7/r79C1/4gknompj1Ajod9PT6d77zHU9JXq/KP++888xV8Xp1viZtfWjQ6tWrzTR9iJA+XU0vzNPv/PW7fL1yX5O/ns7/zGc+Y9WHyhBImwC/g09bjxNv4gX0Knkd9Og8f9BT45r8swc96taL7bw+9Efr0+/Y9fR8/qDzstuh9etpel2eAQEEghUgwQfrS+kIIIAAAgiEIsB38KGwUykCCCCAAALBCpDgg/WldAQQQAABBEIRIMGHwk6lCCCAAAIIBCtAgg/Wl9IRQAABBBAIRYAEHwo7lSKAAAIIIBCsAAk+WF9KRwABBBBAIBQBEnwo7FSKAAIIIIBAsAIk+GB9KR0BBBBAAIFQBP4f2biyWfvlnxUAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-7"/> </p>
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" alt="plot of chunk unnamed-chunk-7"/> </p>

<p>With only a few small changes, one could easily perform bootstrapping with other kinds of predictive or hypothesis testing models, since the <code>tidy</code> function works for many stats outputs.</p>

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