chp3 smoking example needs work. The analysis is wrong as I susptect the data may be wrong

Author: CamDavidsonPilon

Chapter3_MCMC/IntroMCMC.ipynb

@@ -272,7 +272,7 @@
      "metadata": {},
      "source": [
       "_____\n",
-      "### Example: Poisson Regression\n",
+      "### Example: Poisson Regression [Needs work]\n",
       "\n",
       "Perhaps the most important result from medical research was the *now obvious* link between *smoking and cancer*. We'll try to establish a link using Bayesian methods. We have a decision here: should we include a prior that biases us towards there existing a significant link between smoking and cancer? I think we should act like scientists at the turn of the century, and assume there's is no *a priori* reason to assume a link. \n",
       "\n",
@@ -353,7 +353,26 @@
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "(population*100000)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "5612200000.0"
+       ]
+      }
+     ],
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -550,7 +569,7 @@
       "plt.legend(loc=\"upper left\")\n",
       "plt.xlabel( \"age\" )\n",
       "plt.ylabel(\"expected death rate (deaths/population)\" )\n",
-      "plt.title( \"Expected deaths rate; partitioned by smoking habits\")\n",
+      "plt.title( \"Expected death rate; partitioned by smoking habits\")\n",
       "plt.xlim( 42, 87 )"
      ],
      "language": "python",
@@ -576,7 +595,7 @@
      "source": [
       "**Clearly** smoking affects how long you live. \n",
       "\n",
-      "We can interpret these rates are probabilities, for example, 0.032 is the rate of a non-smokers dying at age 40, which is equilivant to the probablity of a randomly selected non-smoker aged 40-45 will die. Hence  1- 0.032 = 0.968 is the probability he/she survives to 45. Similarly, with probability 0.045 a 45-50 year old non-smoker will survive until age 50. Hence, (1- 0.032)(1 - 0.045) is the probability a non-smoker, aged 40 will survive until 50, and so on. Let's plot this."
+      "We can interpret these rates as probabilities, for example, 0.032 is the rate of 40-45 non-smokers dying each year, which is equilivant to the probablity of a randomly selected non-smoker aged 40-45 will die within a year. Hence  1- 0.032 = 0.968 is the probability he/she survives to 45. Similarly, with probability 0.045, a 45-50 year old non-smoker will survive until age 50. Hence, (1- 0.032)(1 - 0.045) is the probability a non-smoker, aged 40 will survive until 50, and so on. Let's plot this."
      ]
     },
     {