standarddeviation.ipynb

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   "source": [
    "# Measure of Variability - Standard Deviation\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Data"
   ]
  },
  {
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   "execution_count": 16,
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   "outputs": [],
   "source": [
    "data = {4, 6, 9, 3, 7}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Population Standard Deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Standard Deviation Calculation using Formula\n",
    "\n",
    "<img src=\"https://cdn-images-1.medium.com/max/800/1*ZIklcOp7x2ntL33eg0530Q.png\" width=\"45000\" height=\"50000\">\n",
    "\n",
    "By Applying this formula in the code, we will get Population Standard Deviation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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     "text": [
      "2.1354156504062622\n"
     ]
    }
   ],
   "source": [
    "def popstddev(data):\n",
    "     # Number of observations\n",
    "     n = len(data)\n",
    "     # Mean of the data\n",
    "     mean = sum(data) / n\n",
    "     # Square deviations\n",
    "     deviations = [(x - mean) ** 2 for x in data]\n",
    "    # stddev\n",
    "     stddev = math.sqrt(sum(deviations) / (n))\n",
    "     return stddev\n",
    "\n",
    "print(popstddev(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Population Standard Deviation Calculation using Python Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1354156504062622"
      ]
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     "execution_count": 18,
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   ],
   "source": [
    "statistics.pstdev(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample Standard Deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sample Standard Deviation Calculation using Formula\n",
    "\n",
    "<img src=\"https://cdn-images-1.medium.com/max/800/1*7hs4AnYrvJc9rmqNLEcWQA.png\" width=\"45000\" height=\"50000\">\n",
    "\n",
    "By Applying this formula in the code, we will get Sample Standard Deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3874672772626644\n"
     ]
    }
   ],
   "source": [
    "def samplestddev(data):\n",
    "     # Number of observations\n",
    "     n = len(data)\n",
    "     # Mean of the data\n",
    "     mean = sum(data) / n\n",
    "     # Square deviations\n",
    "     deviations = [(x - mean) ** 2 for x in data]\n",
    "    # stddev\n",
    "     stddev = math.sqrt(sum(deviations) / (n - 1))\n",
    "     return stddev\n",
    "\n",
    "print(samplestddev(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sample Standard Deviation Calculation using Statistics Python Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3874672772626644"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics.stdev(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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