stats1.ipynb

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    "%%javascript\n",
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   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.use('nbagg')\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic univariate statistics"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.4675589081711304,\n",
       " 1.862602113776709,\n",
       " 3.0233257263183977,\n",
       " 4.17022004702574,\n",
       " 7.203244934421581]"
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       "(2.664497346510712, 1.862602113776709, 2.2423459401169006, 5.028115315158748)"
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   "source": [
    "##%%capture                   \n",
    "# Suppresses output for the cell\n",
    "\n",
    "np.random.seed(1)            # For reproducibility\n",
    "\n",
    "# help(np.random)            # Help on the package\n",
    "x = 10 * np.random.ranf(7)   # 7 uniformly distributed numbers, scaled up\n",
    "\n",
    "sorted(x)[-5:]               # largest 5 elements\n",
    "\n",
    "np.mean(x),np.median(x),np.std(x), np.var(x)          # mean, median, standard deviation, variance\n",
    "np.amin(x),np.percentile(x,[25,50,75]),np.amax(x)     # Tukey's 5 number summary\n",
    "stats.skew(x),stats# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
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   "execution_count": 15,
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   "source": [
    "\n",
    "y = 10 * np.random.ranf(7)\n",
    "\n",
    "np.corrcoef(x,y)[0,1]         # Function returns the correlation matrix, we select off-diagonal element"
   ]
  }
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