diff --git a/How Much is Your Car Worth.ipynb b/How Much is Your Car Worth.ipynb
index bfc2fbe..cf06089 100644
--- a/How Much is Your Car Worth.ipynb	
+++ b/How Much is Your Car Worth.ipynb	
@@ -11,7 +11,19 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from sklearn import linear_model"
+    "from sklearn import linear_model\n",
+    "from sklearn.cross_validation import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
    ]
   },
   {
@@ -67,6 +79,396 @@
    "source": [
     "df = pd.read_csv(\"car_data.csv\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/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>Price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>Make</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Trim</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Cylinder</th>\n",
+       "      <th>Liter</th>\n",
+       "      <th>Doors</th>\n",
+       "      <th>Cruise</th>\n",
+       "      <th>Sound</th>\n",
+       "      <th>Leather</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17314.103129</td>\n",
+       "      <td>8221</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>17542.036083</td>\n",
+       "      <td>9135</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>16218.847862</td>\n",
+       "      <td>13196</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16336.913140</td>\n",
+       "      <td>16342</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>16339.170324</td>\n",
+       "      <td>19832</td>\n",
+       "      <td>Buick</td>\n",
+       "      <td>Century</td>\n",
+       "      <td>Sedan 4D</td>\n",
+       "      <td>Sedan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Price  Mileage   Make    Model      Trim   Type  Cylinder  Liter  \\\n",
+       "0  17314.103129     8221  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "1  17542.036083     9135  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "2  16218.847862    13196  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "3  16336.913140    16342  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "4  16339.170324    19832  Buick  Century  Sedan 4D  Sedan         6    3.1   \n",
+       "\n",
+       "   Doors  Cruise  Sound  Leather  \n",
+       "0      4       1      1        1  \n",
+       "1      4       1      1        0  \n",
+       "2      4       1      1        0  \n",
+       "3      4       1      0        0  \n",
+       "4      4       1      0        1  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/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>Price</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>Cylinder</th>\n",
+       "      <th>Liter</th>\n",
+       "      <th>Doors</th>\n",
+       "      <th>Cruise</th>\n",
+       "      <th>Sound</th>\n",
+       "      <th>Leather</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>804.000000</td>\n",
+       "      <td>804.000000</td>\n",
+       "      <td>804.000000</td>\n",
+       "      <td>804.000000</td>\n",
+       "      <td>804.000000</td>\n",
+       "      <td>804.000000</td>\n",
+       "      <td>804.000000</td>\n",
+       "      <td>804.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>21343.143767</td>\n",
+       "      <td>19831.934080</td>\n",
+       "      <td>5.268657</td>\n",
+       "      <td>3.037313</td>\n",
+       "      <td>3.527363</td>\n",
+       "      <td>0.752488</td>\n",
+       "      <td>0.679104</td>\n",
+       "      <td>0.723881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9884.852801</td>\n",
+       "      <td>8196.319707</td>\n",
+       "      <td>1.387531</td>\n",
+       "      <td>1.105562</td>\n",
+       "      <td>0.850169</td>\n",
+       "      <td>0.431836</td>\n",
+       "      <td>0.467111</td>\n",
+       "      <td>0.447355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>8638.930895</td>\n",
+       "      <td>266.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>14273.073870</td>\n",
+       "      <td>14623.500000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>18024.995019</td>\n",
+       "      <td>20913.500000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>26717.316636</td>\n",
+       "      <td>25213.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>3.800000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>70755.466717</td>\n",
+       "      <td>50387.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Price       Mileage    Cylinder       Liter       Doors  \\\n",
+       "count    804.000000    804.000000  804.000000  804.000000  804.000000   \n",
+       "mean   21343.143767  19831.934080    5.268657    3.037313    3.527363   \n",
+       "std     9884.852801   8196.319707    1.387531    1.105562    0.850169   \n",
+       "min     8638.930895    266.000000    4.000000    1.600000    2.000000   \n",
+       "25%    14273.073870  14623.500000    4.000000    2.200000    4.000000   \n",
+       "50%    18024.995019  20913.500000    6.000000    2.800000    4.000000   \n",
+       "75%    26717.316636  25213.000000    6.000000    3.800000    4.000000   \n",
+       "max    70755.466717  50387.000000    8.000000    6.000000    4.000000   \n",
+       "\n",
+       "           Cruise       Sound     Leather  \n",
+       "count  804.000000  804.000000  804.000000  \n",
+       "mean     0.752488    0.679104    0.723881  \n",
+       "std      0.431836    0.467111    0.447355  \n",
+       "min      0.000000    0.000000    0.000000  \n",
+       "25%      1.000000    0.000000    0.000000  \n",
+       "50%      1.000000    1.000000    1.000000  \n",
+       "75%      1.000000    1.000000    1.000000  \n",
+       "max      1.000000    1.000000    1.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "train, test = train_test_split(df[[\"Mileage\", \"Price\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression(fit_intercept=True)\n",
+    "model.fit(train[\"Mileage\"].to_frame(), train[\"Price\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.018400048296921878"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(test[\"Mileage\"].to_frame(), test[\"Price\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x109ac6f28>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ivcVUJOWvUTw5YWwbSBrRS1wrXC88TEbYW6i2io5VjY3WBQZ1hUO2Jo38CxSm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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x106fa13c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(0,55000,500), model.predict(pd.Series(range(0,55000,500)).to_frame()))\n",
+    "plt.scatter(df.Mileage.to_frame(), df.Price)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Mileage is not a good predictor for car prices when use on its own"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Simple Linear Regression.ipynb b/Simple Linear Regression.ipynb
index 65d531a..2f917a1 100644
--- a/Simple Linear Regression.ipynb	
+++ b/Simple Linear Regression.ipynb	
@@ -14,6 +14,17 @@
     "from sklearn import linear_model"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -27,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -56,6 +67,79 @@
     "5. Interpolate data:  With a listening device, you discovered that on a particular morning the crickets were chirping at a rate of 18 chirps per second.  What was the approximate ground temperature that morning? "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.DataFrame(ground_cricket_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.69229465291470027"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(data[\"Chirps/Second\"].to_frame(), data[\"Ground Temperature\"])\n",
+    "model.score(data[\"Chirps/Second\"].to_frame(), data[\"Ground Temperature\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ground Tempreature = [ 3.410323] * Chirps/Second + 22.848982308066887\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1175449b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data[\"Chirps/Second\"], data[\"Ground Temperature\"])\n",
+    "plt.plot(range(14, 21), model.predict(pd.DataFrame(np.array(range(14, 21)))))\n",
+    "print(\"Ground Tempreature = {} * Chirps/Second + {}\".format(model.coef_, model.intercept_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The data does appear to support the hypothesis that circkets chirp more frequently the higher the temperature is."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -74,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -83,6 +167,153 @@
     "df = pd.read_fwf(\"brain_body.txt\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/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>Brain</th>\n",
+       "      <th>Body</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>198.789984</td>\n",
+       "      <td>283.134194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>899.158011</td>\n",
+       "      <td>930.278942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.005000</td>\n",
+       "      <td>0.140000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>4.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.342500</td>\n",
+       "      <td>17.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>48.202500</td>\n",
+       "      <td>166.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>6654.000000</td>\n",
+       "      <td>5712.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brain         Body\n",
+       "count    62.000000    62.000000\n",
+       "mean    198.789984   283.134194\n",
+       "std     899.158011   930.278942\n",
+       "min       0.005000     0.140000\n",
+       "25%       0.600000     4.250000\n",
+       "50%       3.342500    17.250000\n",
+       "75%      48.202500   166.000000\n",
+       "max    6654.000000  5712.000000"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.87266208430433312"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(df[\"Brain\"].to_frame(), df[\"Body\"])\n",
+    "model.score(df[\"Brain\"].to_frame(), df[\"Body\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The brain weight appears to be a fairly accurate predictor for body weight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Body Weight = [ 0.96649637] * Brain Weight + 91.00439620740681\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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B3yH9f3FcBKtKDsv6sUb0aVwOfBQYVigbGRGdq351kNYSh9QGe2/huPXAPsCmvN2pPZeb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\nWc2cNMzMrGb/H3qt8P+O9FzuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117c2b5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(df[\"Brain\"], df[\"Body\"])\n",
+    "plt.plot(range(0, 7000, 50), model.predict(pd.DataFrame(np.array(range(0, 7000, 50)))))\n",
+    "print(\"Body Weight = {} * Brain Weight + {}\".format(model.coef_, model.intercept_))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -109,15 +340,199 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/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>Sex</th>\n",
+       "      <th>Rank</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Degree</th>\n",
+       "      <th>YSdeg</th>\n",
+       "      <th>Salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>52.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.269231</td>\n",
+       "      <td>2.038462</td>\n",
+       "      <td>7.480769</td>\n",
+       "      <td>0.653846</td>\n",
+       "      <td>16.115385</td>\n",
+       "      <td>23797.653846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.447888</td>\n",
+       "      <td>0.862316</td>\n",
+       "      <td>5.507536</td>\n",
+       "      <td>0.480384</td>\n",
+       "      <td>10.222340</td>\n",
+       "      <td>5917.289154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>15000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6.750000</td>\n",
+       "      <td>18246.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>15.500000</td>\n",
+       "      <td>23719.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>23.250000</td>\n",
+       "      <td>27258.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>38045.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Sex       Rank       Year     Degree      YSdeg        Salary\n",
+       "count  52.000000  52.000000  52.000000  52.000000  52.000000     52.000000\n",
+       "mean    0.269231   2.038462   7.480769   0.653846  16.115385  23797.653846\n",
+       "std     0.447888   0.862316   5.507536   0.480384  10.222340   5917.289154\n",
+       "min     0.000000   1.000000   0.000000   0.000000   1.000000  15000.000000\n",
+       "25%     0.000000   1.000000   3.000000   0.000000   6.750000  18246.750000\n",
+       "50%     0.000000   2.000000   7.000000   1.000000  15.500000  23719.000000\n",
+       "75%     1.000000   3.000000  11.000000   1.000000  23.250000  27258.500000\n",
+       "max     1.000000   3.000000  25.000000   1.000000  35.000000  38045.000000"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df = pd.read_fwf(\"salary.txt\", header=None, \n",
-    "                 names=[\"Sex\", \"Rank\", \"Year\", \"Degree\", \"YSdeg\", \"Salary\"])"
+    "                 names=[\"Sex\", \"Rank\", \"Year\", \"Degree\", \"YSdeg\", \"Salary\"])\n",
+    "df.describe()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "model = linear_model.LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.85471806744109691"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(df.drop(\"Salary\", 1), df.Salary)\n",
+    "model.score(df.drop(\"Salary\", 1), df.Salary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8485077204335425"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(df.drop([\"Salary\", \"Sex\"], 1), df.Salary)\n",
+    "model.score(df.drop([\"Salary\", \"Sex\"], 1), df.Salary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sex appears to be an insignificant factor in determining salary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {