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@deepanshululla
Created February 2, 2019 16:39
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## QQ Plots/Probability Plots\n",
"QQ Plots are used for hypothesis/distribution testing.\n",
"We are creating a random normal distribution using mean=0 and standard deviation=1 and we are creating 1000 data points.\n",
"\n",
"#### Finding percentiles in a data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 -3.775243573527152\n",
"1 -2.371028423500342\n",
"2 -2.071092527893455\n",
"3 -1.9012617674697838\n",
"4 -1.7575054895090598\n",
"5 -1.5967431929890588\n",
"6 -1.5157748897481962\n",
"7 -1.452814658418808\n",
"8 -1.4076549374507101\n",
"9 -1.3404821431171918\n",
"10 -1.2737187357863946\n",
"11 -1.2325216947274373\n",
"12 -1.189369450090339\n",
"13 -1.1220035596780744\n",
"14 -1.0676290880024588\n",
"15 -1.0224477368545128\n",
"16 -0.9732442480448237\n",
"17 -0.9437314493401684\n",
"18 -0.9138641847176409\n",
"19 -0.8800722816169246\n",
"20 -0.8440148228129265\n",
"21 -0.8130756087156309\n",
"22 -0.7861473104248968\n",
"23 -0.7492083439028759\n",
"24 -0.7051576660307357\n",
"25 -0.6734445541939682\n",
"26 -0.6465658670674573\n",
"27 -0.61396265973997\n",
"28 -0.576069486727897\n",
"29 -0.5485166156515269\n",
"30 -0.524456565293377\n",
"31 -0.5075580178076415\n",
"32 -0.4768835126229952\n",
"33 -0.45792910480112337\n",
"34 -0.4338846075341644\n",
"35 -0.3968023274609233\n",
"36 -0.378434968876414\n",
"37 -0.32731729193161885\n",
"38 -0.3025869505862371\n",
"39 -0.27666568814863507\n",
"40 -0.25386386195095867\n",
"41 -0.22968391477084232\n",
"42 -0.2056594767403207\n",
"43 -0.18161570453350262\n",
"44 -0.16123907821533276\n",
"45 -0.12424795198217031\n",
"46 -0.08721048654993081\n",
"47 -0.06460665514514168\n",
"48 -0.05150406248506583\n",
"49 -0.02105422600197395\n",
"50 -0.0026823547115730198\n",
"51 0.036163164141410506\n",
"52 0.06655842347279303\n",
"53 0.08479126128032052\n",
"54 0.1046938782907635\n",
"55 0.13256217338909637\n",
"56 0.16435178146067675\n",
"57 0.18698418522683935\n",
"58 0.20764237782754683\n",
"59 0.23700604577125556\n",
"60 0.26799025862775705\n",
"61 0.2843408205877605\n",
"62 0.302840920586405\n",
"63 0.33644615394668453\n",
"64 0.3797565827913117\n",
"65 0.40089269454355203\n",
"66 0.42300409872604305\n",
"67 0.47320821953333503\n",
"68 0.5092402294874592\n",
"69 0.5463647594653929\n",
"70 0.570832142715836\n",
"71 0.5956375430800731\n",
"72 0.6263174703612876\n",
"73 0.6486761721801368\n",
"74 0.6805887405086214\n",
"75 0.708893119347729\n",
"76 0.7433053743764718\n",
"77 0.7682243744413277\n",
"78 0.8099058231234665\n",
"79 0.8413794674276277\n",
"80 0.8816230825622737\n",
"81 0.9152818802795254\n",
"82 0.9427167723226839\n",
"83 0.9717814193963727\n",
"84 1.019234552795083\n",
"85 1.0597894463744417\n",
"86 1.0961919870099377\n",
"87 1.1634761691234363\n",
"88 1.2076562192117435\n",
"89 1.2449426481490538\n",
"90 1.3463490483650447\n",
"91 1.3819003561509353\n",
"92 1.4592707156567541\n",
"93 1.5243075323676138\n",
"94 1.600277713581979\n",
"95 1.6859925906608337\n",
"96 1.7428526760440206\n",
"97 1.9249773915997397\n",
"98 2.0638452721877\n",
"99 2.2563935867204807\n",
"100 2.8394736191795356\n"
]
}
],
"source": [
"#Q-Q plot\n",
"import numpy as np \n",
"import pylab \n",
"import scipy.stats as stats\n",
"\n",
"# N(0,1)\n",
"std_normal = np.random.normal(loc = 0, scale = 1, size=1000)\n",
"\n",
"# 0 to 100th percentiles of std-normal\n",
"for i in range(0,101):\n",
" print(i, np.percentile(std_normal,i))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, the set of intervals for the quantiles is chosen. A point (x, y) on the plot corresponds to one of the quantiles of the second distribution (y-coordinate) plotted against the same quantile of the first distribution (x-coordinate). Thus the line is a parametric curve with the parameter which is the number of the interval for the quantile.\n",
"\n",
"If the two distributions being compared are similar, the points in the Q–Q plot will approximately lie on the line y = x. If the distributions are linearly related, the points in the Q–Q plot will approximately lie on a line, but not necessarily on the line y = x. Q–Q plots can also be used as a graphical means of estimating parameters in a location-scale family of distributions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plotting probability plots\n",
"\n",
"**In this we are creating another random variable whose mean=20 and standard deviation=5 and the number of data points is 50000.**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(50000,)\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# generate 50000 sanples from N(20,5)\n",
"measurements = np.random.normal(loc = 20, scale = 5, size=50000) \n",
"#try size=1000\n",
"print(np.shape(measurements))\n",
"\n",
"\n",
"stats.probplot(measurements, dist=\"norm\", plot=pylab)\n",
"pylab.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1000,)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# generate 1000 sanples from N(20,5)\n",
"measurements = np.random.normal(loc = 20, scale = 5, size=1000) \n",
"\n",
"print(np.shape(measurements))\n",
"\n",
"\n",
"stats.probplot(measurements, dist=\"norm\", plot=pylab)\n",
"pylab.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1000,)\n"
]
},
{
"data": {
"image/png": 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IWy7iDsbSjzYs4nmOLXJ9NAGsXasP/1RQ05CIVJjoqKCG/MgDXMyZTOZjdufvzORt/h63jOYFpJ5qBCLyl0S//ZvBjBnOqTzKYlrTiwjXczUdmVtsEsjJURKoClQjEJFyiZ0TALADXzOKXI7hBd5nb7rxGgtoX2z5nBx47bVKCFRKpRqBiJRJtAYQOzEsl/vII5uuvM6FjGQ/3ik2CTRrFnQGKwlUHaoRiEjCokNBo/ZgMRPowwH8l1c5nP6MYQk7FVu+fn349ttKCFTKRDUCESlVJBLUAqJJoBbr+DfXMZeO7MHHnM6DdOeVEpNATg78/HMlBSxlohqBiJSocC1gH95lAn1oyyIepieDuZMVbFdsefUFVH2qEYhIXN26FawFbMVv3Mkg/sv+bMsvHM3z9OLhYpNATo76AtKFagQiUkDhGgDAEbzCWPqxI19zH+dxFTfyG1sXKVuzJkyapCGh6UaJQEQ2ycqC/PzNx41YyZ0MpjcR8mjNgbzNO+wft9zkyUoA6UpNQyKyqTN4cxJw/kmExbTmFB5nGEPpxEdxk0BODmzYoCSQzsqUCMysgZkVP0NERNJOt27Qu/fm4xYs5UWOIkJvvmAX9uRDrmUY69iyQDmzYHkI9QGkv1ITgZm9aWbbmFlDYB7wgJmNTH5oIpJM0VpAdGJYDTYykLtZRBsOYiYDuZsDmMUi2hYpm50d1B5UC6geEukj2NbdfzWzPsAD7j7UzOYnOzARSZ4GDWDVqs3H2SxiAn3Yj3d5iX8wgNEso2WRcuoLqJ4SaRqqaWZNgVOAFxJ9YTPb0czeMLPFZrbIzAaF54eZ2bdmNjf8ObKcsYtIOZhtTgJb8CfDGMpHdGJXPqMXUziKF4skgZo1g2Yg9QVUT4nUCK4DXgVmufsHZrYz8FkC5TYAF7v7h2a2NTDHzKaHz93h7reVL2QRKavoVpGx9uO/TKAP2SzmIXpzESNZSZMC19SoARs3VmKgkhKlJgJ3fwJ4Iub4S+DEBMp9B3wXPl5tZouB5uUPVUTKqnlzWL684Ll6rOYmriSXUXzNjnTnZV6le5GytWrBunWVFKikVCKdxbuZ2QwzWxgetzezq8tyEzNrBXQC3gtPnW9m883sfjNrUEyZvmY228xmr1ixoiy3ExGCJqDCSeBIXiSPbHIZxd1cQBsWxU0CdeooCWSSRPoIxgNXAusB3H0+cFqiNzCzesBUYLC7/wqMBnYBOhLUGG6PV87dx7l7Z3fv3KRJk3iXiEgxttii4HETfuBhevIiR/ML27I//+VC7uR36hUpm5MDa9ZUUqBSJSSSCOq6+/uFzm1I5MXNrBZBEoi4+1MA7v69u29093yCJNOlLAGLSPHatAlqAuvXR884p/Mgi2lND57i31zHnnzIe+xbpGx2ttYGylSJJIKVZrYL4ABmdhJh239JzMyAicBidx8Zc75pzGUnAAvLFLGIFBHdLCZ2jaBWfMUrdOdBzuBj9qATH3ED/2Y9WxQp7w6LFlViwFKlJDJq6DxgHLCHmX0LfAX0LrkIAAcApwMLzGxueO4qoKeZdSRILEuAfmUNWkQ2K7xIXA02cgF3cwNXk08NcrmPMfTH43zva9ZMG8VIYqOGvgS6mdlWQA13X53IC7v724DFeeqlsoUoIvEU3jMYoB3zmUAfuvABL3AUAxjNN+xYpKz2CJBYpSYCM7um0DEA7n5dkmISkRLESwBb8gdXcwOXcws/04DTeITHOJXC38WmTNGEMCkqkaah32Me1waOBhYnJxwRKUnhZaIBDuQ/jOdc9uATJnEGF3M7P9GoSNmcHCUBiS+RpqECwzvN7DbguaRFJCJxWaGG1m34hZu5ggGM4StacTivMp3D45ZVU5CUpDz7EdQFdq7oQESkeIWTwDE8xyLa0Jdx3M5FtGVh3CQwYICGhErpEukjWEA4dBTIApoQrD8kIklWeImI7fieu7mAU3mc+bSjB0/xQZypOFoeQsoikT6Co2MebwC+d/eEJpSJSPkVrAU4ZzKJ27mYuqzhKoYzgkvZQK0i5bKzNSdAyqbYRBBuRANQeLjoNmaGu/+UvLBEMltsEtiZLxhLP7oxg5n8nXMZz6fsXqSMEoCUV0k1gjkETULx5gI46icQqXCxk8Oy2MBg7uQ6rmEDNenPaMbRt8jEME0Kk7+q2ETg7jtVZiAimSwSKbhvcAfmMoE+dGYOz3IsuYxieZxV3AcMgFGjKjFQqZYS6SMgXCp6V4J5BAC4+8xkBSWSSerWhbVrg8e1Wcs1XMeljGAljTmJJ5jKicSrmOfkKAlIxUhkP4I+wEyCXcquDX8PS25YItVfdKXQaBI4iLeYRweu5GYmcwbZ5DGVk4iXBAYM0JBQqTiJzCMYBOwNLHX3Qwk2mNFOMSLl1K1bwZVCt2UVY+jHWxxCFhvJ4TX6MJGfaVikbLNmwbwA1QSkIiWSCP5w9z8AzGxLd/8Y4gxZEJFSNWhQcJ2g43maPLLpwwRu5VLasYDXyYlbNidHncKSHIkkgm/MrD7wDDDdzJ4FlpdSRkRiRGsBq1YFx3/jO57kRJ6mB9+zPV14n8u5lbXUjVt+yhQ1BUnyJLLW0Anhw2Fm9gawLfBKUqMSqSZyc2H06NgzzjlM5DYuYUv+5HJuZiQXxZ0YBlojSCpHSRPKXgQeBp5x998B3P2tygpMJN01aLC5BgCwC58zjr505Q3e5GDOZTyfs2vcsloiQipTSU1D4wiWl1hiZo+Z2fFmVnSPOxEpoHAzUBYbuIxbWEA79uRD+jCerrxebBKYMkVJQCpXSRPKngWeNbM6wLHAGcAYM3sJeMTdp1dSjCJpofCWkQCd+JAJ9GFPPmIqPRjIPXxHs7jltUSEpEqpncXuvtbdHwv7Cg4nGD6qPgKRGM2bF0wCdVjDLVzG+3ShKd/Rg6mcxNRik4A2j5dUSmRC2fZmNtDMZhGMHJoG7JX0yETSRCRScKnoQ3md+bTnMkZwP2fTmsU8TY+4ZaPzAkRSqaTO4nOBngRzBp4CLnP3WZUVmEg6iN0/uD4/cxuXcA738xn/x6G8zpscGrecFoqTqqSk4aP7AzcDr7l7fgnXiWScghvGOCcylXs5n8as5Cau4Dqu4Q/qxC2rGoBUNcU2Dbn7We4+rbxJwMx2NLM3zGyxmS0ys0Hh+YZmNt3MPgt/Nyhv8CKpYLY5CTTjW57mBJ7kZL6lOXvzAVdxU9wkkJ2tJCBVU3n2LE7UBuBid28N7AucZ2bZwBXADHffFZgRHotUedFhoQBGPn0ZSx7ZHM40LmEE+/Aec+lUpFz9+uoMlqotoWWoy8PdvwO+Cx+vNrPFQHPgOOCQ8LLJwJvA5cmKQ+Sviu0HANiNTxhHXw5mJjPoSl/G8SW7xC2rGoCkg0S2qoyrLFtVmlkrgmGn7wHbh0kCd//OzLYrpkxfoC9AixYtEr2VSIUpvHF8TdZzKSO4hutYSx3OZiIPcBbxN/ELmoJE0kGiW1W2AH4OH9cHlgEJ7WBmZvWAqcBgd//VLP7/NIW5+ziC2c107txZ36ukUhX+Z7oXs5nIOXRgPo9zMhdwN9/zt2LLa3KYpJOSOot3cvedCTaiOcbdG7t7I4JlJ55K5MXNrBZBEoi4e7TM92bWNHy+KfDDX3kDIhVpiy0KJoG6/M4ILuE99qExKzmOZziVx4tNAtF5AUoCkk4S6Sze291fih64+8vAwaUVsuCr/0RgsbuPjHnqOYLlKgh/P5t4uCLJEU0A69dvPteN6SygHZdwO+PoSzZ5PMdxcctHRwRpboCko0QSwUozu9rMWplZSzMbAvyYQLkDgNOBrmY2N/w5kmBuwmFm9hlwWHgskhLR7SJjE0BDfuQBzmQ6h7OeWhzEW+Qyml/ZNu5rTJmiGoCkt0RGDfUEhgJPE/QZzAzPlcjd36a4XjSK2YJJpBLFbhofcE7hce7mAhryEzcwhBu4mj+pHbe8ZgdLdZHIxjQ/AYPMrJ67/1YJMYkkXfPmBZNAc75hFLkcy/N8QGcOZxrz6RC3rBKAVDeJLDq3v5nlAXnhcQcz09bZkpZycwvODDbyGcAo8sgmhxlcyEj25d24SUD9AFJdJdI0dAdwBEEnL+4+z8wOSmpUIkmQlQX5MQum7M7HTKAPBzKLaRxGP8ayJM6oaO0WJtVdQktMuPvXhU5tTEIsIkkRrQVEk0At1jGEG5hHB7LJ4wwmcQSvFkkCtWoFNQAlAanuEqkRfG1m+wMeblV5AbA4uWGJVIzCy0N04T0m0Id2LOQRTmMwd/ID2xcpp6UhJJMkUiPoD5xHsE7QN0DH8FikSmvefHMS2IrfuIPBvMN+1GcVR/M8/+SRIkkgWgsQySQl1gjMLAs43d17VVI8IhUidmjo4bzKWPrRiqXcy3lcxY2sZpu45dQMJJmoxBqBu2+EYqZSilRB0aWi166FRqxkMv/iVbqzljocwNsM5N64SSC6VLRIJkqkj2CWmd0LPAb8Hj3p7h8mLSqRcthcC3B68gh3MYht+YVruYYbuYp1bFmkTJ06sGZNpYcqUqUkkgj2D39fF3POga4VH45I2UUi0Lt38HhHljGaARzFS7zLPvRhAotoG7fclCnQS42eIgnNLI6/+7ZIisUmgBpsJJdR3MSVAFzAXdzHeeSTFbesmoFENis1EZjZ9sCNQDN3/0e43eR+7j4x6dGJFKNBA1i1KniczSLGcy778w6vcAT9GcNSWhVbVklApKBEho9OItiToFl4/CkwOFkBiZQkulroqlWwBX8ylGF8RCd241N6MYV/8HKxSUBDQ0XiSyQRNHb3x4F8AHffgGYWSyVr0CBIAHl5wfG+vMOH7MkwruVxTqE1i3mYXpS0baSGhorEl0gi+N3MGhF0EGNm+wK/JDUqkVBsDQCgHqu5m4HM4gC2ZjVH8iKnM4WVNIlbvk4d7RgmUppERg1dRLDg3C5mNgtoApyU1KhECHYNi90w5h+8xBj6swPfcA8DuZob+I2tiy2fkwOvvVYJgYqkuURGDX1oZgcDuxPUuz9x9/WlFBP5S+rW3ZwEGrOCuxjEP3mERWRzALN4l/2KLVu/Pvz8cyUFKlINFJsIzKxHMU/tZmbEbEYvUqFiJ4b1Zgp3cCHb8CtDGcZNXMl6tohbTpPDRMqnpBrBMeHv7Qgmlb0eHh8KvAkoEUiFil0ptCVLGEN/uvMq/2U/+jCBxWQXW3bAABil7ZJEyqXYzmJ3P8vdzyLoJM529xPd/USgTaVFJxkhEgk6hGfMCCaGDeJOFtGGA5jFedzLgbxdbBIYMCDoDFYSECm/RDqLW7n7dzHH3wO7JSkeyTCxtYC2LGACfdiH93mRIxnAaL6mRdxyWh5CpOIkkgjeNLNXgUcIagenAW8kNSrJCNG+gC34k6u5gSu4mVXUpycP8yinEW9OQHa2hoKKVLRS5xG4+/nAGKADwaY049x9YLIDk+opEoEaNTYvFX0AbzOXjvybG3iYf9KaxTxKT+IlgWbNlAREkiGRjWledfduwNNleWEzux84GvjB3duG54YB5wIrwsuucveXyhq0pKfmzWH58uDx1vzKTVzJeYxiCS05gleYxhHFlm3WDL79tpICFckwiWxMs8bMti3Ha08Cusc5f4e7dwx/lAQyQLQzOJoEjuZ58simP2MYyYW0ZWGJSWDAACUBkWRKpI/gD2CBmU2n4MY0F5RUyN1nmlmrvxSdpLXCG8dvx/fczQWcyuMsoC09eIoP6FJsec0MFqkciSSCF8OfinK+mf0LmA1c7O5x54CaWV+gL0CLFvFHjkjVlJsLo0fHnnHOYDIjuYit+J0h3MAILi12YlitWlogTqQymZeyLq+Z1Qb+j2DE0Bfu/kfCLx7UCF6I6SPYHlgZvtb1QFN3P7u01+ncubPPnj070dtKisRuFBO1E18yln4cxmv8hwM5l/F8wh7FvoZGBYlUHDOb4+6dS7uu2D4CM6tpZrcC3wCTgSnA12Z2q5nVKk9Q7v69u29093xgPJTQLiBppXASyGIDF3E7C2nLPrxHf0ZzMG8VmwSaNdMqoSKpUlJn8QigIbCTu+/l7p2AXYD6wG3luZmZNY05PAFYWJ7XkaojNzfoCI5NAu2Zxzvsx+1cwmt0I5s8xtIfj/PPLTs7SADqDBb6K7F3AAASTklEQVRJnZL6CI4GdvOYtiN3/9XMBgAfA4NKemEzewQ4BGhsZt8AQ4FDzKwjQdPQEqDfX4peUqZwRzDAlvzBv7mey7iVn2jIKTzGE5xMcZvFaHawSNVQUiJwj9OB4O4bzazUDf/cvWec09rnOM0V7QgOHMRbjKMvu/Mp93MWl3AbP9Mw7mtoNJBI1VJS01BeOLqnADPrTVAjkAzTpk3RJLANvzCGfrzFIdRkA92YzjncXyQJmAU1AHclAZGqpqQawXnAU2Z2NjCHoDlnb6AOQfu+ZIjiagHH8QyjyGV7vmcElzCUa1lL3SLXaYlokaqt2ETg7t8C+5hZV4Klpw142d1nFFdGqp82bTZvGB+1Pf/jHgZyMk8ylw4cy3PMoegINW0UI5IeEtmq8nU2b0ojGaRbt8JJwDmb+7mNS6jDWq7gJm7nYjZQdDSx5gOIpI9SVx+VzBMdEho7KmgXPmcGOUykD/NpT3vmcwtXFEkC0Y1ilARE0ocSgWwSiUDNmgX7A7LYwKXcygLasRdz6MtYDuUNPovZm6hmzc0dweoLEEk/iaw1JBkgXodwRz5iIuewJx/xNMdzHvfxHc0KXKOOYJH0p0QgRSaH1WYtwxjGxdzOCprQg6k8TY8CZWrWhEmTNCFMpDpQ01AGi9cXcAhvsIB2XM6tPMBZZJNXJAkMGADr1ysJiFQXqhFkqMLDQuvzMyO4lD5M5HN2oSszeIOuRcrl5KgpSKS6UY0gg0RrAGYFk0APppJHNmcyiZu5nHYsiJsEBgzQrGCR6kg1ggwRb5G4piznXs6nB0/zIZ04ihf5iD0LXKO+AJHqTzWCaiwSgcaNi/YDGPmcyzgW05p/8DKXcQtdeL9IEsjJUV+ASCZQIqiGIhGoVy/YI+DHHws+tyuf8jpdGUc/5rAX7VjACC5jY0zlMDovQM1AIplBiaCayc0NEsDvvxc8X5P1XMFNzKc9HZjH2Uwkhxl8wf8VuE4jgkQyj/oIqoniVggF2IvZTKAPHZnHE5zEBdzN/2ha4BqtDSSSuVQjSFOx7f9m8ZNAHdYwgkt4j33Yjh84nqc5hScKJAGzoBagJCCSuVQjSEORCJx1VtCEU5wcXmMs/diFLxlDP67gZn6h/qbnzeChh9QEJCKqEaSlIUOKTwIN+ImJnM1rHMZGsjiYNxnAmAJJoF49JQER2UyJIA0tXRrvrHMyj7OY1pzOQwznKtozn5kcDAQf/tEVQlevVhIQkc3UNJRmcnOLnmvON4wil2N5ntnsxeFMYz4dgCABjBmjD34RKZ5qBGmk8MggI5/+jCaPbLrxGhdxO/vyLvPpQO3aQQ1A3/5FpDRJSwRmdr+Z/WBmC2PONTSz6Wb2Wfi7QbLuX13Ejg6KTQK78zFvcTCjyeU99qEtC7mDi6jfqCZTpsDatUoAIpKYZNYIJgHdC527Apjh7rsCM8JjKUZ0dFDs7OBarGMINzCPDrRhEWcwicOZRn7LnXGHlSuVAESkbJKWCNx9JvBTodPHAZPDx5OB45N1/+pg0KCCo4P25n1m05kb+DdPcwKtWcyDnIGZMXx46uIUkfRW2X0E27v7dwDh7+2Ku9DM+prZbDObvWLFikoLMNVim4KiNYG6/M7tXMQ77EdDfuJYnqUnj/ID2wPQv79qASJSflW2s9jdx7l7Z3fv3KRJk1SHk1SxH/6FF4o7nFdZSFsu4g7G0J9s8nieYzc9rz2DReSvquxE8L2ZNQUIf/9QyfevUqIJIN4qoQ35kcn8i1fpzp9syYH8h/O5j9Vss+kaJQERqQiVnQieA84IH58BPFvJ968yIhHo27doAgDnNB5hMa3pySNcz9V0ZC6zOHDTFY0aBUNDlQREpCIkbUKZmT0CHAI0NrNvgKHAzcDjZnYOsAw4OVn3r+qGDIE1awqe25FljCKXo3mR9+hCDjNYSLtNz9etC+PGqT9ARCpW0hKBu/cs5qmcZN0znSxbtvmxkU8uo7iJK6lBPoO4k3s5n3yyNl3TqBHcdZeSgIhUPC0xkQKRCNSoARs3QmvymEAf9ucdXuVw+jGWpbTadK0SgIgkW5UdNVQdxXYO19i4jmu4lrl0ZHc+4XQepDuvsJRWm/oANEFMRCqDagSVIBIJJodFO4b35R3Gcy5tWUSEf3Ihd/BT1nZMmawPfRGpfKoRJFns6KB6rOYuLmAWB7ANv3IkL9KbCCvYjvx8JQERSQ0lgiSLjg7qzssspC3ncy/3cj5tWMTLHLnpuhYtUhikiGQ0NQ0l2ZqlK3iIC+lNhDxacyBv8w77F7imbl20VpCIpIxqBEkSmeIMbjyFPFpzCo8zlGF04qMiSaBRI80NEJHUUiKoQJEItGoFLW0pDU8/kjt/PJ3P2JVOfMR1DGUdW266NjoySKOCRCTVlAgqSCQC/c/dyHFL72IRbfg7/2Egd3Mgb5NHm03XtWypBCAiVYv6CP6CSCToDF62DNrZQqbn92Ff3uMl/kF/xvA1BXuAzWDJktTEKiJSHNUIyik6LPS7pX8yzK/hg/w92YUv6MnDHMWLRZIAaGSQiFRNqhGU05Ah0HHNLCbQh9Z8zIOczkWM5Ecax71eI4NEpKpSjaA8fv2Vy5aexywOpA5r6c7LnMGDRZKAWfC7ZUuNDBKRqkuJIEHREUHH2Assb9iG/ozmDgbTloW8SvdN12VlBQmgZUt46KFgvaAlS5QERKTqUtNQAiIRGHLuD9y0dhA9eZQFG9tyctaTzM7ah3XrNl+n/QJEJB2pRlAadz4cNJk5a1vTg6e4muvZizn8d+M+bL118M0/WgNQEhCRdKQaQUm++gr69eP2H6fzNgdwLuP5mNabnv7pp2A+gIhIOlONIJ6NG2HkSGjbFt59l6sbjuIgZhZIAqDhoCJSPSgRFDZ/Puy3H1x8MXTtCosW0fruAdSpW/BPpeGgIlJdKBFE/fFHMDlgr72CYT6PPgrPPQc77kivXkH7v/oDRKQ6UiIAmDkTOnSAG2/kiS1703jFYlpdfiqRh23TJb16BfkhP1/DQUWkesnsRPDLLzBgABx8MKt/Xs8xW07jlN8f4EcasXRpsIREJJLqIEVEkitzE8Gzz0J2dtDGc/HFdKm9gBf+PKzAJWvWBK1FIiLVWUoSgZktMbMFZjbXzGZX6s3/9z84+WQ4/nho3BjefRduu41Pvtkq7uXLllVqdCIilS6VNYJD3b2ju3eulLu5w/33Q+vW8PzzwZCf2bNh772B4oeCaoioiFR3mdE09MUX0K0bnHMOtG8P8+bBVVdBrVqbLhk+PBgSGktDREUkE6QqETgwzczmmFnfeBeYWV8zm21ms1esWFG+u2zYACNGQLt2wbf/sWPhjTdg992LXKohoiKSqczdK/+mZs3cfbmZbQdMBwa6+8ziru/cubPPnl22roRIBLbsdyYn/T6ZaXWO47db7qPHwOZ/MXIRkfRhZnMSaX5PyVpD7r48/P2DmT0NdAGKTQRlFd09bLc1g3iUo5m69kTqXmGsbahv+CIihVV605CZbWVmW0cfA4cDCyvyHkOGBEM/59KJqZwEmIaCiogUIxU1gu2Bpy3Yvqsm8LC7v1KRNyhuyKeGgoqIFFXpicDdvwQ6JPMeLVrA0qXxz4uISEHVcviohoKKiCSuWiYCDQUVEUlctd2hrFcvffCLiCSiWtYIREQkcUoEIiIZTolARCTDKRGIiGQ4JQIRkQyXkkXnysrMVgBxpohVqMbAyiTfI1nSOXZI7/jTOXZQ/KlUGbG3dPcmpV2UFomgMpjZ7ErbJKeCpXPskN7xp3PsoPhTqSrFrqYhEZEMp0QgIpLhlAg2G5fqAP6CdI4d0jv+dI4dFH8qVZnY1UcgIpLhVCMQEclwSgQiIhlOiSBkZteb2Xwzm2tm08ysWapjKgszG2FmH4fv4Wkzq5/qmBJlZieb2SIzyzezKjGcLhFm1t3MPjGzz83silTHUxZmdr+Z/WBmFbpNbGUwsx3N7A0zWxz+uxmU6pjKwsxqm9n7ZjYvjP/alMekPoKAmW3j7r+Gjy8Ast29f4rDSpiZHQ687u4bzOwWAHe/PMVhJcTMWgP5wFjgEnefneKQSmVmWcCnwGHAN8AHQE93z0tpYAkys4OA34AH3b1tquMpCzNrCjR19w/D/c/nAMen0d/egK3c/TczqwW8DQxy93dTFZNqBKFoEghtBaRVhnT3ae6+ITx8F9ghlfGUhbsvdvdPUh1HGXUBPnf3L919HfAocFyKY0qYu88Efkp1HOXh7t+5+4fh49XAYqB5aqNKnAd+Cw9rhT8p/bxRIohhZsPN7GugF3BNquP5C84GXk51ENVcc+DrmONvSKMPo+rCzFoBnYD3UhtJ2ZhZlpnNBX4Aprt7SuPPqERgZq+Z2cI4P8cBuPsQd98RiADnpzbaokqLP7xmCLCB4D1UGYnEnmYszrm0qkWmOzOrB0wFBheq0Vd57r7R3TsS1Ny7mFlKm+eq7VaV8bh7twQvfRh4ERiaxHDKrLT4zewM4Gggx6tY508Z/vbp4htgx5jjHYDlKYol44Rt61OBiLs/lep4ysvdV5nZm0B3IGUd9xlVIyiJme0ac3gs8HGqYikPM+sOXA4c6+5rUh1PBvgA2NXMdjKzLYDTgOdSHFNGCDtbJwKL3X1kquMpKzNrEh3VZ2Z1gG6k+PNGo4ZCZjYV2J1g9MpSoL+7f5vaqBJnZp8DWwI/hqfeTZdRT2Z2AnAP0ARYBcx19yNSG1XpzOxI4E4gC7jf3YenOKSEmdkjwCEESyF/Dwx194kpDSpBZnYg8B9gAcH/rwBXuftLqYsqcWbWHphM8O+mBvC4u1+X0piUCEREMpuahkREMpwSgYhIhlMiEBHJcEoEIiIZTolARCTDKRFIpTKzRuEKr3PN7H9m9m34eJWZVeqiYWbWMRwCGj0+tryriJrZEjNrXHHRleneZ8aulmtmE8wsO9VxSfpQIpBK5e4/unvHcHr9GOCO8HFHNo8JrzBmVtLs+Y7ApkTg7s+5+80VHUMlOBPYlAjcvU+6rMQpVYMSgVQlWWY2PlyjfVo46xIz28XMXjGzOWb2HzPbIzzf0sxmhHswzDCzFuH5SWY20szeAG4xs63C9fc/MLOPzOy4cDbwdcCpYY3k1PCb9b3ha2xvwb4O88Kf/cPzz4RxLDKzvqW9ITM7y8w+NbO3wvcWff1JZnZSzHW/hb/rhe/lQzNbEF2LycxaWbD+foG/T/ganYFI+D7qmNmbFmdfBzPrbcE6+HPNbKwFC59lhbEsDO934V/47ydpSolAqpJdgfvcvQ3BDOMTw/PjgIHuvhdwCTAqPH8vwXr67QkW2bs75rV2A7q5+8XAEIK9GvYGDgVGECz9ew3wWFhDeaxQLHcDb7l7B2BPYFF4/uwwjs7ABWbWqLg3Y8G6+dcCBxDsW5CdwN/gD+AEd98zjPX2cEmFuH8fd38SmA30Ct/H2mJiaQ2cChwQ1sA2Eqyy2xFo7u5t3b0d8EACMUo1k1GLzkmV95W7zw0fzwFahStM7g88sfnzkC3D3/sBPcLHDwG3xrzWE+6+MXx8OHCsmV0SHtcGWpQSS1fgXxCsFAn8Ep6/IFwSA4JF53Zl87Iehe0DvOnuKwDM7DGCBFUSA260YOOYfIKlrbcPnyvy9ynltWLlAHsBH4R/xzoESyA/D+xsZvcQLLQ4rQyvKdWEEoFUJX/GPN5I8GFVA1gVfostTex6Kb/HPDaCb88FNr8xs33KEpyZHUKwQNh+7r4mXDWydhliirWBsEYefuPfIjzfi2DNpb3cfb2ZLYm5R7y/T8LhA5Pd/coiT5h1AI4AzgNOIdjPQjKImoakSgvXmf/KzE6G4EMz/OAC+C/Bqp8QfIC+XczLvAoMjDaxmFmn8PxqYOtiyswABoTXZ5nZNsC2wM9hEtgD2LeU8N8DDglHStUCTo55bgnBN3QIdjarFT7eFvghTAKHAi1LuUdp7yP2/ZxkZtuF76lh2MfSGKjh7lOBfxM0g0mGUSKQdNALOMfM5hG01Uc3s7kAOMvM5gOnA8VtYn49wQftfAs2a78+PP8GkB3tLC5UZhBwqJktIGiGaQO8AtQM73c9wZagxXL374BhwDvAa8CHMU+PBw42s/cJmpCiNZgI0NnMZofvO5HliScBY6KdxcXEkgdcDUwL458ONCVoenrTgt2yJgFFagxS/Wn1UZFKYmZnAp3dvcrtfieZTTUCEZEMpxqBiEiGU41ARCTDKRGIiGQ4JQIRkQynRCAikuGUCEREMtz/AwYIZ2BgCtqhAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# generate 100 sanples from N(20,5)\n",
"measurements = np.random.normal(loc = 20, scale = 5, size=1000) \n",
"\n",
"print(np.shape(measurements))\n",
"\n",
"\n",
"stats.probplot(measurements, dist=\"norm\", plot=pylab)\n",
"pylab.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Observation**\n",
"\n",
"1) In this we observed that the more number of points we have the more we have an overlap.\n",
"\n",
"2) there is usually an overlap at the center and the external points are somewhat outside..\n",
"\n",
"3) If there is a major overlap we can conclude the distribution of graph is gausian"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plotting uniform distribution\n",
"\n",
"Here below we are creating a uniform random variable and try to plot the qq plot for it. For normal distribution it doesn't overlap much with the line.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"measurements = np.random.uniform(low=-1, high=1, size=10000) \n",
"\n",
"stats.probplot(measurements, dist=\"uniform\", plot=pylab)\n",
"pylab.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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