Lesson116-Parametric Statistical Hypothesis Tests & Nonparametric.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第116讲 Parametric Statistical Hypothesis Tests\n",
    "\n",
    "## Python学习：https://www.ixigua.com/home/77346806707?utm_source=xiguastudio\n",
    "\n",
    "## Python源文件及数据下载链接: https://github.com/rayc2020/LessonPythonCode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Specify the Null Hypothesis. ...\n",
    "Step 2: Specify the Alternative Hypothesis. ...\n",
    "Step 3: Set the Significance Level (a) ...\n",
    "Step 4: Calculate the Test Statistic and Corresponding P-Value. ...\n",
    "Step 5: Drawing a Conclusion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Normality Tests - data has a Gaussian distribution.\n",
    "2. Correlation Tests - check if two samples are related\n",
    "3. Stationary Tests - to check if a time series is stationary or not\n",
    "4. Parametric Statistical Hypothesis Tests -  to compare data samples\n",
    "5. Nonparametric Statistical Hypothesis Tests - Tests whether the distributions of two independent samples are equal or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='D:\\python\\Interview-Prepartion-Data-Science-master\\Lesson116-1.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Parametric Tests\n",
    "The basic principle behind the parametric tests is that we have a fixed set of parameters that are used to determine a probabilistic model that may be used in Machine Learning as well.\n",
    "\n",
    "Parametric tests are those tests for which we have prior knowledge of the population distribution (i.e, normal), or if not then we can easily approximate it to a normal distribution which is possible with the help of the Central Limit Theorem.\n",
    "\n",
    "Parameters for using the normal distribution is –\n",
    "\n",
    "Mean\n",
    "Standard Deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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p3iHw1rOleItA1iBbqw1PTLuO7s72JhlZIpYyUdCOjKSDQndXWwPR2e5q0UUUAFFFFABRRRQAUUUUAFFFcz8W/jV4O+AHgubxJ488W+GfBXh23kSGXVNe1SDTbKJ3O1FaaZlQMx4AJyTwKTkluNJvY6aiuK+CP7Snw6/aY0S81P4ceP8AwV8QdO06cWt3d+GtctdWgtZSoYRu9u7qr7SDtJBwQa7Wqaa3EmnsFFFFIAooooAKKKKACiiigAooooAKKKKAPj//AII/TDWtE/aL1wSPIutfHXxWybnLkJBNFaDqARn7PnHOAQM16D+2r/wVM/Z//wCCd39nR/GP4n6F4Pv9VCva6b5c+oanLG3mBZ/sdrHLcCAtFIvnGMR7l27txAPn/wDwQ+8zUf2IL7XJcl/FHxC8ZavvO8GQSeIr8K2H5AKqMe2O+a/I/wAc/tL2n/BKH/g6d+IXxN/aV0HUrvwv4yS4Tw34kawa9Gj2VykEdrf2w27nSCCN7SUQgyIrTKA5+V4pO88PhW1H93FXe14U4rlWq1k07Pok3YuokliKyTdqk9F/eqS1e+i6+qXU/Z/9iL/grn+zt/wUa8Va3ofwa+JNr4w1nw9ape31k2lX+mzxwM+wSqt3BEZFDYDGPdtLJuxuXP0hXz3+zX+yT8Iov2mvGn7T/wAPtRj13WPjfomm29xqNlc21xpV1a26ARz2zRxhi0oEfmM0jhjDHgLtOfoStpKyimmpfaXZ3e2i6W3V73MYyu20046crXVWX63+Vj45/wCCh3/Cmv2F/B3xS+Ms3ge38QfF741aVB4Fs9OiEt9qXjq8MLw2WkwW7F1VGJBlEUYBSPe4cotM/wCCDH7AHir/AIJs/wDBN7wp8OvG+ord+LLi6udc1O0imE0GjS3TBvscbgkMIwBuK/KZGkKkqQTzX7av/BK745/tIft06X8bPAX7Utp8K5/C2ivofhrSpPhfY+JBoccwQ3kySXl1s8+dlw0qQo4iCxZK7t30R+w5+z58Rv2dPhZqum/FT4xal8cPF+sazNqk3iG60ZNFSKJooY0tobOKWSGCNBEWxFsUtIzFdzMzThfdhOT92Uklb+7GV1FWutW3OTdukUtG51ivenTS1UXe/m4JNu9n7qSgkr9Xe1lH2aiiikMKKKKACiiigDyD9sX9vv4OfsAeBIPEfxh+IGheB9OvGK2cd2zzXuolXjVxbWkKvcXGwyxl/KjbYrBm2rzXl37In/BcX9ln9u34xwfD/wCFnxXtfEnjC6tZby306bQ9T0x7mOIBpPLe7tokdlXLbFYttVmxhWI/KD/guT8RdS/YR/4OOfg/+0B8XvCepeMfgxYWNl/Ymy2W6htxBFMkqwiQLH9qtrqX7YqFgctEwYEgr+tfwP8Ag18Bf23v2kPA/wC2Z8P9fj8WatF4Tn8MaVqNg8QspbaWUuxmjaIXCXcW6SLa7o0YkkRo89DCNVaSrS1V5ppbx5U1G+9+Z26JWejdmPF/u5+yhvywab2lzNcyX+FX6u7WyufUleR/tQfBj4Q6xqXhj4ufFWw0fPwMa88RaTrWqXUkVr4eJg2z3TIHETlUQFTIrlGVWTa4Br1yvlH/AIKx/wDBPTxz/wAFIPhX4W8H+F/jDa/CrSNF1uHXdVin8HQ+JItflt3SS0hmimuIomgjlUyNFIkqSsI9wwmGio5Kzhvda9tfitdXtva6vtdbq4KLbU3o07+ej02fxbbNa6po+ef+CJX7M/iPx3+2l+0T+2HeaRfeBvBXx+uo4vBnhy5h+z3Oo6bEyldYuYcDynuNgkRWG4ieZjlXRn/TSvlj9ij9jb9oD4H/ABjv/FHxm/am1H462DaLJpWm6Mngaz8K2enyPPFI1yyWczRzybYtimSMsgd9rAMwb6nrdqMaVOlT+GEeVLdpJu13ZXb+JtK13ZJJWXPFylUqVKi96Urt9G2lsruyXwq+tld3buyiiiszQKKKKACiiigAooooAKKKKACq+r3o07Srq4cMVt4nkIHJICk1Yrjf2i/EX/CIfs++O9WKmT+y/D2oXe3Gd3l20j4xkZ6eo+tcmYVOTC1JrpGT+5M3wtNVK0Kb6tL72fEX7Bfxh8S/su/8G6OifEXwl4an8U+L9K8Gap4m0vRorG4uRe3s91c3MSGGImVog8qlgjA7FYgr1HxT/wAFA/8Agt/+zJ/wVb/4JJ23gfUdEuvHf7S/irSrW30XwXpPhPUJL7RfEshSMy2VwY3TyxIGIWKd5JYmEbAlmUfp3/wTa8Y+GP2XP+CPPwH1jxz4j8O+C/Dui/D3QX1DVdZv4NO0+yae2gVRJNI4jUtLKqAlvmdwBywFdX/w9i/ZZ/6OW+AH/hw9I/8AkivQzPDL6xXoSfu81tNGuVy2etr3XS6cU0zmwWKbpUq8FaVm+6fMo7rS9rPrrzO54F/wQN/ZH+Ln7DH/AAR28PeFPEWmWa/Eq5Ooa5p/h/Xb57S20lrmQvb2lxLFHK8IPEkgWN3RpnUruBA81/4JY/tCfG3xr/wXG/at+HXxY+J83ju0+H2haZFptpp9i+j6FY+ayXGbbT/OmEbKJzGZZJJZnVF3SEAAfXev/wDBXT9l/RtCvby3/aG+BerXFrA80Vja/EXQ1nvGVSRFGZbtIw7EbQXdFyRllGSPy0/YU/b38O/B3/gtJ+0D8cfFEnw80zwB8b4tP0/TpY/jV4AuL3RhB9ni8+8hTXP9Xsjd2EDSuuAqrITTjWdXMVOStHkmtdk+Xlhq9L7q+/VmU6Xs8ByRd5c8X5tc3NLzt1tsfutRXgH/AA9i/ZZ/6OW+AH/hw9I/+SK6D4W/8FCfgF8cfHdh4W8FfHH4QeMPE+qeZ9j0jRPGWnahf3flxtLJ5cEMzSPtjR3O0HCoxPAJrE3PX6KK8x+Nf7bPwZ/Zr8S22i/Eb4ufDHwBrN5bC9gsPEnimx0q5ngLMglWOeVGZCyOu4DGVYZyDSbS3HZvY7LRfid4b8SeN9b8M6d4h0O/8SeGkt5dX0m2v4pb7SkuFZoGuIVYvEJVVihcAOFJGcGtyvy8/ZL/AOCin7Pvhv8A4LA/tc+JtR+OvwcsPDfiXR/BUWkatc+NNNisdVe3s71Z1t5mmCSmJmUOEJKFgDjIr9Efgn+0b8Pf2lvD11q/w58eeDfiBpNjcG0ub3w3rdtqtvbzBVbynkgd1V9rK20nOGBxgiqSbgpeSb8hTajUlDta3neKk/uba+R2VZnjXxBJ4S8G6vqsVld6nNpllNdpZ2sTSz3bRxswijRQWZ2I2gAEkkAA1p1n+LPFml+AvCup67rup6fouiaLaS3+oahf3CW1pYW8SF5ZpZXISONEVmZ2ICgEkgCs6sXKDjF2bW/bzLhJKSclddu5+K9//wAHHX7N/wC2R/wTJ8XeDf2qNCtl+MVxBqWmal8N7XwpqTC4v45JEtDaSyLKltMMx/PLOksUySEBcJn0v/g1t/4J+fGj9lH/AIJy/EpPGf8AbXwv8SfE3U5bvw3Zarpwe78PFLTyE1CWylwQ7S4byZQpZbdMgBwT93f8PYv2Wf8Ao5b4Af8Ahw9I/wDkisrx1/wV8/Zp8N+CdY1HSfj78CPEWq2FlNcWelW/xK0O3l1OZIyyW6SS3Sxo0jAKGkZVBbJIAJq5TjCNadtakUna9rJ3vFL3lJvrd6WSV7MjlnOdKN9IScle28tLNv3eVJ7W829z4E/4KNfDj4rfsS/FX9kfwR8Hfjp8aPH/AO0j4y8WmXxFFqfi/U7zSfFGnKpa+vbrR5biWxsrON2wiRRoscQfBdot6/sfX8/f7LX/AAVt+I3wZ+I3jr4g658OP2cPEvxu+Jl00F94+1b9p3wXNaaFpwl/0XT7XTo7sPHY20e0tDHcq88itI8m4rs/YRf+CsP7LQUA/tL/ALP5PfHxC0gD/wBKK0V1RV7XcnJ2skrqPupLZJJdEnJy5dNFm7e2aV7JKKvd3s5e8293K/m1FR5tWfQFFeIeE/8Agpt+zd498VaZoWhftB/BDWtb1q7isNP0+w8daXc3d/cSuEihiiScvJI7sqqigliQACTXt9ZmgUUUUAFFFFABRRRQAUUUUAFFFFABXg//AAVJ8UDwX/wTU+P+qGXyWs/h5rrJJ8p2OdPmCn5uPvEda94r5U/4LhX89l/wSZ+OsVuV+0ap4ak0qPccAtdSR2wHQ5J83oBk9BziufGUva0J0v5k196sdWBqKGJpzeykn9zPRfgd8APCfjz9g34d/D3xl4W0DxX4WXwjo1nd6LrmnQahY3P2e3t2jEkMitE+ySNHGVwGRWGCAa+bv+Cl37Mv7Nn/AAT/AP2MPGPxf079j39nDxsvgqKK7vNKuvDOk6OZrdpVjYxzDT5wZAXUhCgDc/MDgH7p8L6X/YfhrTrIFiLO2jgG7r8qBeenPFfCX/Bf29/4WP8ACr4G/BKKRfO+OPxb0DRbyMqzN/Z1tP8AbbpwB/d8iLIPGGNdmYzlXxD9k+V1KiSe9ueaV9d7X1Wl+63POy5RpYaLqK6hBtra/LBtq/Ru1vXozzn/AIIy3/7In/BYL9mbW/Hen/se/s/eCNa8O6zLo9/oMnhTSNS8kiJJYZfPFjESkiuf+WfBRhzipf8Agl98P/gn/wAFCfFXxYh1z9gz9mf4feH/AIV+Jrvwc+rWun6XrY1rU7WTbOsEZ0i3xAq4bzGcHLouz7xT5D/Yv+KC/wDBEf8A4Lz/ALUvwvv/ALPY/Dnx14c1LxvosErGKMm2tptVtliP3Qqwm+gPHJiHPy4P6Pf8G7/wnvfhn/wSZ+Gmpax5kniH4i/bPHGqzyR7ZbqbUrqS5SRyeWYwNCNx6gDtgUUZQqxWIirRdOLt2nJuP4ezqXV9G46bo0qxlSboSd5Ko7PvBR5rrpb36aXV62629b/4dO/ss/8ARtPwA/8ADeaR/wDI9dB8Lf8Agnt8Avgd47sPFPgr4H/CDwf4n0vzPser6J4N07T7+08yNopPLnhhWRN0buh2kZV2B4JFeian8TfDeiePNK8K3viHRLTxRrtvPd6bo819FHf6hDBt86WGAt5kiR703sqkLvXOMitus+l/67fmX1sFeY/Gv9ib4M/tKeJbbWviN8I/hj4/1mzthZQX/iTwtY6rcwQBmcRLJPE7KgZ3baDjLMcZJr06ik0nuPma2Py8/ZL/AOCdf7PviT/gsD+1z4Z1H4FfBy/8N+GtH8FS6RpNz4L02Wx0p7izvWna3haEpEZWVS5QAuVBOcCv0R+Cf7OXw9/Zp8PXWkfDnwH4N8AaTfXBu7my8N6JbaVb3ExVV8144ERWfaqruIzhQM4ArW0X4ZeG/DfjfW/E2neHtDsPEniVLeLV9WtrCKK+1VLdWWBbiZVDyiJWYIHJCBiBjJrcqlJqCj5JPzFO0qkp97W8rRUX97TfzCs/xZ4T0vx74V1PQtd0zT9a0TWrSWw1DT7+3S5tL+3lQpLDLE4KSRujMrIwIYEggg1oUUgPnbWv+CUf7Mlvo93JYfsxfs73V/HC7W0Nx4D0mCGWUKdivItq5RS2AWCMQCTtbGD+bX/BI/8AbP8A2Sf+CnP7YPiv4M6v+wz+zz8N/EHh7T7m8tLiLR9H1uDVXtp1iniT/iWW+3AbeDltyq3AxX6k/wDBRP4+r+y3+wh8XviCZI45vCnhPUL21LglWuRA6wKcc/NM0a8etfgN8ffgHf8A/BFX4o/8E7/2jtKjW1tNQ8NadpfjSRkZY2mlHnXgkxyXe01CdATn/j1BxxilhXzYtQn8LcYekqiqcsr72UoJNa3uu1m8QmsM5Q+K0pLu1TcOaNvNTun0s9+n6Qa74B+C1t/wVvtf2YNK/YK/Zl1XTW8NxeMbzxibDS4hp2lNIYXaSyGkMftHnqUSITbXDIxkQFtn1/8A8Onf2Wf+jafgB/4bzSP/AJHr50/4JeSj9of/AIKs/tp/GfeLvTdL1nSvhjoUw/eJHFptqHvFjk/uvPJG5UdCcnPGPrT4w/t3fA/9njxgfDvxA+Mvwp8DeIBAlydM8QeLdP0y8ET52SeVPKr7GwcNjBwacWlQpOXxSjzP/t68or1UHG+m9xS1rVOT4U0l6pJS/wDJ+ZdrWtpvjeE/+CZX7N3gLxVpmu6F+z58ENF1vRbuK/0/ULDwLpdtd2FxE4eKaKVIA8ciOqsrqQVIBBBFe31FYahBqthBdWs8VzbXMaywzROHjlRhlWVhwQQQQRwQalptNOzBNNXQUUUUgCiiigAooooAKKKKACiiigDzX4y/tjfCv9nrx/4S8J+NvH/hfw34p8d38Om+H9HvL5Fv9Vmmk8uMRQAmQoZPk8wgIGKqWBIB8P8A+C4ZXUP+CfepaK5m/wCKo8X+E9FxFv3MJ/EWnIy4Xk5XcMZGQetY/wDwVv8A+CS3g79tv9kb4h2Xg/wz4f8AC/xbvLmHxbo/iPSrCCx1K912xjf7K9xcookkLIzw73JKLKSCCBXzT4U/b9uP+Cl//BMb9mzU9WkS38fr8cPCHhPxxp8sOySLVNP1FLiffF8oQSrbpNtIwvmFQMrijDe/UhCXxKrSv/glOEbr/DK8ZPzg7LmsGIfs4yqL4eSo15ShGUrP1STS8pK7sfrRXwD+1H8Jfjb8Y/8Agt1+z54vi+EOvXvwO+DdpqiS+Il1/R0SbUtStDAbsWb3guGggXy1JMQlJMpVGAQv9/UU4Plqxq/y306aprX5N27Oz3SCfvU509uZWv13T09bWd7pptPRn48f8HOn/BFz4sf8FB/ib8KfiD8DPD6az4s0y1ufDXiLbq1rpjx6e53wzF55Y96KZLlGVNzFZQNpGa/WH4U/DCz+Efwc8N+DNJZrOw8M6NbaNZtFgtDHBAsKFd4IJAUH5gffNdNQc4PrU017OjKhH4ZScn3u/wDgttdbyetrJOq/aVY1pbxXKvTT9El2slpvf8avhx8BdN/Z8/4O0fDOmWOr+KfEV7qvwgl1PVdX8Q6tLqWoareMZ43ndnPlxBliQCG3SK3jACxRRqAo/ZWvz48Q/wDBIf4263/wU4sv2p4/2hPh3B4z03QW8M2ujn4T3T6QNPIkAR1/twTNJmVnLiVcsBwF+Sv0EtllS2jEzpJMFAdkQorNjkgEkgZ7ZOPU1UH/ALPTi9GubTtepOStbS1mtFtsTNWr1JLVPl19IQi99d0/zH0UUUhhRRRQAUUUUAfCX/BwV8IfjX+0/wDsUL8J/gz8NNW8ezeNtWsz4jubfXtM0mLT9MtriO4ki3XdzCzyzMiIoRWUKJCxU7Q1j/gud/wT51r/AIKVf8ErtV8FeFvDTx/EDS/sGv8AhnR7y5tYZre8hwr2jTeb9nVzBJPEWEpj3EEOVwa+5aKynSUqMqL6y5r9U1a1ullyppNPW/d31jVlGrGqvspxt0ab1v5u9m1a6t2Vvir/AIIAfsO+Mf2Bf+Cbfh3wh8SNOOl/EfV9V1HXvEsDX8d/ItzPcMse+eN3jdvs8cGSjsM55Jya8x/4K+fCr4a/AT9lf4pfCL4WfD201745/tn6nPFa6HbyPc3mt6hIEFxqs7zM4t7WyjzNu+SGJ8EBC7PX6R1+ePxV/wCCRn7SGv8A7cPxA+N/gX9si08Aav42gi0q3tD8INN1t9F0iB2aDT4Z7y6dlQMxeQxrGJpDvZchQumLl9Yq/vF7slZ2/lVvc3vZ2iutkr35kjPDR9hTvTfvRfMr/wAzbvJ200vLtdu2zbX1F/wTh/ZVv/2If2F/hf8ACfVNbPiLU/A+hxafd343eXNNku6x7ufKRnKJnB2IuQOle2V5x+yV8HvFPwE/Z98P+FfGvj/Uvil4r077RJqfim+tTaTavNNcSzF/I8yQQqokCLGrlEVFVQqgKPR66MTUc6spt3bbd1s7vpfWxhhoKFKMEmkklZ76LrbS/e2gUUUVgbBRRRQAUUUUAFFFFABRRRQAV+Mf7VP7BXiD9lz/AIOG/gr4i8IQT23wb/aE8YweJ9csLeMLZ23ibSrG+k3MMbVaVJXuBn5ndrgj/Vgj9nKKKfuYiniFvBp+q3s/K6T9Un0HU9+hUoPacWvRtWuvPVrzTa0vcKKKKBBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='D:\\python\\Interview-Prepartion-Data-Science-master\\Lesson116-t-test-formula.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where,\n",
    "\n",
    "x̄1 is the sample mean of the first group\n",
    "\n",
    "x̄2 is the sample mean of the second group\n",
    "\n",
    "S1 is the sample-1 standard deviation\n",
    "\n",
    "S2 is the sample-2 standard deviation\n",
    "\n",
    "n is the sample size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Student’s t-test \n",
    "\n",
    "William Sealy Gosset who first published it in English in 1908 in the scientific journal Biometrika using his pseudonym \"Student\" because his employer preferred staff to use pen names when publishing scientific papers instead of their real name, so he used the name \"Student\" to hide his identity\n",
    "\n",
    "Tests whether the means of two independent samples are significantly different.\n",
    "\n",
    "Assumptions\n",
    "\n",
    "Observations in each sample are independent and identically distributed (iid).\n",
    "Observations in each sample are normally distributed.\n",
    "Observations in each sample have the same variance.\n",
    "Interpretation\n",
    "\n",
    "H0: the means of the samples are equal.\n",
    "H1: the means of the samples are unequal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=-0.326, p=0.748\n",
      "Probably the same distribution\n"
     ]
    }
   ],
   "source": [
    "# Example of the Student's t-test\n",
    "from scipy.stats import ttest_ind\n",
    "data1 = [0.873, 2.817, 0.121, -0.945, -0.055, -1.436, 0.360, -1.478, -1.637, -1.869]\n",
    "data2 = [1.142, -0.432, -0.938, -0.729, -0.846, -0.157, 0.500, 1.183, -1.075, -0.169]\n",
    "stat, p = ttest_ind(data1, data2)\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "\tprint('Probably the same distribution')\n",
    "else:\n",
    "\tprint('Probably different distributions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of Variance Test (ANOVA)\n",
    "\n",
    "Tests whether the means of three or more independent samples are significantly different.\n",
    "\n",
    "Assumptions\n",
    "\n",
    "Observations in each sample are independent and identically distributed (iid).\n",
    "Observations in each sample are normally distributed.\n",
    "Observations in each sample have the same variance.\n",
    "Interpretation\n",
    "\n",
    "H0: the means of the samples are equal.\n",
    "H1: one or more of the means of the samples are unequal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=0.096, p=0.908\n",
      "Probably the same distribution\n"
     ]
    }
   ],
   "source": [
    "# Example of the Analysis of Variance Test\n",
    "from scipy.stats import f_oneway\n",
    "data1 = [0.873, 2.817, 0.121, -0.945, -0.055, -1.436, 0.360, -1.478, -1.637, -1.869]\n",
    "data2 = [1.142, -0.432, -0.938, -0.729, -0.846, -0.157, 0.500, 1.183, -1.075, -0.169]\n",
    "data3 = [-0.208, 0.696, 0.928, -1.148, -0.213, 0.229, 0.137, 0.269, -0.870, -1.204]\n",
    "stat, p = f_oneway(data1,data2, data3)\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "\tprint('Probably the same distribution')\n",
    "else:\n",
    "\tprint('Probably different distributions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='D:\\python\\Interview-Prepartion-Data-Science-master\\Lesson116-2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}