Project Questions.ipynb

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     "output_type": "stream",
     "text": [
      "/Users/jas/P1-Test-a-Perceptual-Phenomenon\n"
     ]
    },
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     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Congruent</th>\n",
       "      <th>Incongruent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.079</td>\n",
       "      <td>19.278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.791</td>\n",
       "      <td>18.741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.564</td>\n",
       "      <td>21.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.630</td>\n",
       "      <td>15.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.669</td>\n",
       "      <td>22.803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.238</td>\n",
       "      <td>20.878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.692</td>\n",
       "      <td>24.572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.987</td>\n",
       "      <td>17.394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.401</td>\n",
       "      <td>20.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.480</td>\n",
       "      <td>26.282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22.328</td>\n",
       "      <td>24.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.298</td>\n",
       "      <td>18.644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.073</td>\n",
       "      <td>17.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16.929</td>\n",
       "      <td>20.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18.200</td>\n",
       "      <td>35.255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12.130</td>\n",
       "      <td>22.158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18.495</td>\n",
       "      <td>25.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.639</td>\n",
       "      <td>20.429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.344</td>\n",
       "      <td>17.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12.369</td>\n",
       "      <td>34.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.944</td>\n",
       "      <td>23.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14.233</td>\n",
       "      <td>17.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19.710</td>\n",
       "      <td>22.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.004</td>\n",
       "      <td>21.157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Congruent  Incongruent\n",
       "0      12.079       19.278\n",
       "1      16.791       18.741\n",
       "2       9.564       21.214\n",
       "3       8.630       15.687\n",
       "4      14.669       22.803\n",
       "5      12.238       20.878\n",
       "6      14.692       24.572\n",
       "7       8.987       17.394\n",
       "8       9.401       20.762\n",
       "9      14.480       26.282\n",
       "10     22.328       24.524\n",
       "11     15.298       18.644\n",
       "12     15.073       17.510\n",
       "13     16.929       20.330\n",
       "14     18.200       35.255\n",
       "15     12.130       22.158\n",
       "16     18.495       25.139\n",
       "17     10.639       20.429\n",
       "18     11.344       17.425\n",
       "19     12.369       34.288\n",
       "20     12.944       23.894\n",
       "21     14.233       17.960\n",
       "22     19.710       22.058\n",
       "23     16.004       21.157"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Place dataset in current working directory\n",
    "#from IPython.external import mathjax; mathjax.install_mathjax()\n",
    "import os\n",
    "path = os.getcwd()\n",
    "print path\n",
    "import pandas\n",
    "df = pandas.read_csv(path + '/stroopdata.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Congruent</th>\n",
       "      <th>Incongruent</th>\n",
       "      <th>Subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.079</td>\n",
       "      <td>19.278</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.791</td>\n",
       "      <td>18.741</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.564</td>\n",
       "      <td>21.214</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.630</td>\n",
       "      <td>15.687</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.669</td>\n",
       "      <td>22.803</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.238</td>\n",
       "      <td>20.878</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.692</td>\n",
       "      <td>24.572</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.987</td>\n",
       "      <td>17.394</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.401</td>\n",
       "      <td>20.762</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.480</td>\n",
       "      <td>26.282</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22.328</td>\n",
       "      <td>24.524</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.298</td>\n",
       "      <td>18.644</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.073</td>\n",
       "      <td>17.510</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16.929</td>\n",
       "      <td>20.330</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18.200</td>\n",
       "      <td>35.255</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12.130</td>\n",
       "      <td>22.158</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18.495</td>\n",
       "      <td>25.139</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.639</td>\n",
       "      <td>20.429</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.344</td>\n",
       "      <td>17.425</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12.369</td>\n",
       "      <td>34.288</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.944</td>\n",
       "      <td>23.894</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14.233</td>\n",
       "      <td>17.960</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19.710</td>\n",
       "      <td>22.058</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.004</td>\n",
       "      <td>21.157</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Congruent  Incongruent  Subject\n",
       "0      12.079       19.278        1\n",
       "1      16.791       18.741        2\n",
       "2       9.564       21.214        3\n",
       "3       8.630       15.687        4\n",
       "4      14.669       22.803        5\n",
       "5      12.238       20.878        6\n",
       "6      14.692       24.572        7\n",
       "7       8.987       17.394        8\n",
       "8       9.401       20.762        9\n",
       "9      14.480       26.282       10\n",
       "10     22.328       24.524       11\n",
       "11     15.298       18.644       12\n",
       "12     15.073       17.510       13\n",
       "13     16.929       20.330       14\n",
       "14     18.200       35.255       15\n",
       "15     12.130       22.158       16\n",
       "16     18.495       25.139       17\n",
       "17     10.639       20.429       18\n",
       "18     11.344       17.425       19\n",
       "19     12.369       34.288       20\n",
       "20     12.944       23.894       21\n",
       "21     14.233       17.960       22\n",
       "22     19.710       22.058       23\n",
       "23     16.004       21.157       24"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Subject'] = df.index+1\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###1. What is our independent variable? What is our dependent variable?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "INDEPENDENT - Word Condition (Congruent or Incongruent)  \n",
    "DEPENDENT   - Response Time in seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###2. What is an appropriate set of hypotheses for this task? What kind of statistical test do you expect to perform? Justify your choices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ho - **Null Hypothesis**: (  μi - μc = 0 ) There is **no** significant difference in the population average response time in viewing the congruent(c) words vs viewing the incongruent(i) words.\n",
    "\n",
    "Ha - **Alternative Hypothesis**: ( μi - μc ≠ 0 ) There is a significant difference, positive or negative, in the population average response times.\n",
    "\n",
    "The Dependent Samples t-Test is the appropriate statistical test as the same subjects are assigned two different conditions. The different conditions are dependent because, in theory, by doing the first test you have some practice doing it and you might have an unfair advantage due to this learning effect in doing the similar type of test second. In addition, we don't have any population parameters provided (so a z-test would not be appropriate here)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###3. Report some descriptive statistics regarding this dataset. Include at least one measure of central tendency and at least one measure of variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c = df['Congruent']; i = df['Incongruent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.05"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_mean = c.mean(); round(c_mean,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.02"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_mean = i.mean(); round(i_mean,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.56"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_sd = c.std(); round(c_sd,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_sd = i.std(); round(i_sd,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###4. Provide one or two visualizations that show the distribution of the sample data. Write one or two sentences noting what you observe about the plot or plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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d1M3HGq3/lKkETge2Be6PiHn9sM0zgYMj4lJJ+wJnAbvVSujYQWblbbfdxkyY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\nHuZKwMysh/1/qWr4zT155zQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1064a1210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "N = len(df)\n",
    "x = df['Subject']\n",
    "y = df['Congruent']\n",
    "colors = 'blue'\n",
    "area = np.pi * 15 # 0 to 15 point radiuses\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Congruent Words: ', fontsize=14, fontweight='bold')\n",
    "ax = fig.add_subplot(111)\n",
    "fig.subplots_adjust(top=0.85)\n",
    "ax.set_title('Sample Response Time Scatterplot')\n",
    "ax.set_xlabel('Subject #')\n",
    "ax.set_ylabel('Completion Time (seconds)')\n",
    "\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "plt.yticks(np.arange(0, 35, 2))\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.ylim([0,35])\n",
    "plt.xlim([0,24])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mA3cCT5FMSW8I7ARsA3y74yUcRuw+aRIX3XYbzJmz/OCNF2+1zEO33MIRdZTV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\nCRhjTB9jJWCMMX3M/we9dEiwcB+z9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1070ce850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = len(df)\n",
    "x = df['Subject']\n",
    "y = df['Incongruent']\n",
    "colors = 'red'\n",
    "area = np.pi * 15 # 0 to 15 point radiuses\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.suptitle('Incongruent Words: ', fontsize=14, fontweight='bold')\n",
    "ax = fig.add_subplot(111)\n",
    "fig.subplots_adjust(top=0.85)\n",
    "ax.set_title('Sample Response Time Scatterplot')\n",
    "ax.set_xlabel('Subject #')\n",
    "ax.set_ylabel('Completion Time (seconds)')\n",
    "\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 1.0))\n",
    "plt.yticks(np.arange(0, 35, 2))\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.ylim([0,35])\n",
    "plt.xlim([0,24])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The congruent words sample has a distribution which is between 8 and 22 seconds and has a lower average completion time compared to the incongruent words scatterplot which shows the distibtuion is between 15 to about 26 seconds with what appears to be one outlier at 35 seconds. The average completion time is definitely higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###5. Now, perform the statistical test and report your results. What is your confidence level and your critical statistic value? Do you reject the null hypothesis or fail to reject it? Come to a conclusion in terms of the experiment task. Did the results match up with your expectations?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confidence Level = 90%  \n",
    "t-critical value (2-sided test) -> from [t-table](https://s3.amazonaws.com/udacity-hosted-downloads/t-table.jpg)  \n",
    "df = n - 1 = 24  \n",
    "tcrit =  +- 1.711"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of subjects in the sample\n",
    "n = len(df)\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.96"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#point estimate of μi - μc -> PE (in seconds)\n",
    "PE = i_mean - c_mean\n",
    "round(PE,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.86"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#s - sample standard deviation of differences\n",
    "#1. D = sample difference\n",
    "df['D'] = df['Incongruent'] - df['Congruent']\n",
    "\n",
    "#2. DFM = difference from the mean\n",
    "#SQD = squared differences from the mean\n",
    "DFM = df['D'] - df['D'].mean()\n",
    "df['SQD'] = DFM*DFM\n",
    "\n",
    "#3. SSD = sum of squared differences\n",
    "SSD = df['SQD'].sum()\n",
    "\n",
    "#4. v = variance = SSD/(n-1)\n",
    "v = SSD/(n-1)\n",
    "\n",
    "#5. s = sqrt(v)\n",
    "from math import *\n",
    "s = sqrt(v)\n",
    "round(s,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0207"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#t-statistic\n",
    "#t = PE/(s/√n)\n",
    "#PE = (μi - μc)\n",
    "t = PE/(s/(sqrt(n)))\n",
    "round(t,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "t-statistic > t-critical  \n",
    "8.0207 > 1.711\n",
    "\n",
    "###Conclusion:\n",
    "\n",
    "We **reject** the Null Hypothesis which states that there is no significant differences between the population average completion time for the two different conditions (Congruent & Incongruent). In theory,\n",
    "if we were able to calculate the average difference of every single person person who took this pair of tests (the population) we expect there would be a significantly slower or significantly faster time in either the the incongruent or congruent condition. These are the results I expected since my own anecdotal experience showed that the second condition took me longer to complete. "
   ]
  }
 ],
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