diff --git a/.gitignore b/.gitignore
index e8fe7ce..dbaa9b1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,7 +14,6 @@
 *.toc
 *.xml
 *.pdf
-*.dat
 *.script
 *.table
 
diff --git a/Mathematics/3rd/Statistics/Images/exp.dat b/Mathematics/3rd/Statistics/Images/exp.dat
new file mode 100644
index 0000000..b90b84d
--- /dev/null
+++ b/Mathematics/3rd/Statistics/Images/exp.dat
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diff --git a/Mathematics/3rd/Statistics/Images/normal.dat b/Mathematics/3rd/Statistics/Images/normal.dat
new file mode 100644
index 0000000..157b000
--- /dev/null
+++ b/Mathematics/3rd/Statistics/Images/normal.dat
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diff --git a/Mathematics/3rd/Statistics/Images/qq-plot.pdf b/Mathematics/3rd/Statistics/Images/qq-plot.pdf
index 84dc0fa..ad57e7d 100644
Binary files a/Mathematics/3rd/Statistics/Images/qq-plot.pdf and b/Mathematics/3rd/Statistics/Images/qq-plot.pdf differ
diff --git a/Mathematics/3rd/Statistics/Images/quantiles.ipynb b/Mathematics/3rd/Statistics/Images/quantiles.ipynb
new file mode 100644
index 0000000..ff25927
--- /dev/null
+++ b/Mathematics/3rd/Statistics/Images/quantiles.ipynb
@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8aa31c5850>]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the normal theoretical quantiles against the sample quantiles of a N(3,4) and a Exp(4) distribution\n",
+    "from scipy.stats import norm, expon\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# generate 1000 random numbers from a N(3,4) distribution\n",
+    "x = norm.rvs(loc=3, scale=2, size=99)\n",
+    "# generate 1000 random numbers from a Exp(4) distribution\n",
+    "y = expon.rvs(scale=1/4, size=99)\n",
+    "\n",
+    "# sort the numbers\n",
+    "x.sort()\n",
+    "y.sort()\n",
+    "\n",
+    "# generate the theoretical quantiles\n",
+    "q_theor = norm.ppf(np.arange(0.01, 1, 0.01), loc=0, scale=1)\n",
+    "\n",
+    "# plot the theoretical quantiles against the sample quantiles\n",
+    "# plt.plot(q_theor, x, 'o', color='red', label='N(3,4)')\n",
+    "\n",
+    "# normal quantiles vs sample quantiles of Exp(4)\n",
+    "plt.plot(q_theor, x, 'o', color='blue', label='Exp(4)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-2.3263478740408408 -0.9681043329242129\n",
+      "-2.053748910631823 -0.9483174188484838\n",
+      "-1.880793608151251 -0.9421091449323153\n",
+      "-1.75068607125217 -0.2646177123627633\n",
+      "-1.6448536269514729 -0.11152812303657678\n",
+      "-1.5547735945968535 -0.0199594273507957\n",
+      "-1.475791028179171 0.3779733119980291\n",
+      "-1.4050715603096329 0.6395772189871942\n",
+      "-1.3407550336902165 0.6641556667062574\n",
+      "-1.2815515655446004 0.7731626450400912\n",
+      "-1.2265281200366098 0.855587010993633\n",
+      "-1.1749867920660904 0.9766765127112742\n",
+      "-1.1263911290388007 1.0033035710483886\n",
+      "-1.0803193408149558 1.0121989657029136\n",
+      "-1.0364333894937898 1.2256095963993818\n",
+      "-0.994457883209753 1.3801069681716156\n",
+      "-0.9541652531461943 1.3804188956434726\n",
+      "-0.9153650878428138 1.3804854569596843\n",
+      "-0.8778962950512288 1.542366885270722\n",
+      "-0.8416212335729142 1.709827508847272\n",
+      "-0.8064212470182403 1.818977043266546\n",
+      "-0.7721932141886848 1.8556576449994089\n",
+      "-0.7388468491852137 1.8810561083474209\n",
+      "-0.7063025628400874 1.9067205042081692\n",
+      "-0.6744897501960817 1.9616956425021455\n",
+      "-0.643345405392917 2.0256420681725644\n",
+      "-0.6128129910166272 2.078357603275342\n",
+      "-0.5828415072712162 2.131763806916487\n",
+      "-0.5533847195556727 2.135619239789468\n",
+      "-0.5244005127080409 2.145279980249009\n",
+      "-0.4958503473474533 2.174007839801228\n",
+      "-0.46769879911450823 2.178693832045448\n",
+      "-0.4399131656732338 2.237792446032935\n",
+      "-0.41246312944140473 2.304133884383583\n",
+      "-0.3853204664075676 2.3312436088336432\n",
+      "-0.3584587932511936 2.565812873867984\n",
+      "-0.33185334643681663 2.643574932429921\n",
+      "-0.3054807880993974 2.709939479275819\n",
+      "-0.27931903444745415 2.7135783957453574\n",
+      "-0.2533471031357997 2.7187015742226843\n",
+      "-0.22754497664114934 2.743071271226227\n",
+      "-0.20189347914185074 2.7599134159385534\n",
+      "-0.17637416478086135 2.822972713761037\n",
+      "-0.15096921549677725 2.91676538183042\n",
+      "-0.12566134685507402 2.917864565030486\n",
+      "-0.10043372051146975 3.0415467964159504\n",
+      "-0.07526986209982976 3.134401663010697\n",
+      "-0.05015358346473353 3.215579648265324\n",
+      "-0.02506890825871106 3.2299982651326977\n",
+      "0.0 3.26855549133778\n",
+      "0.02506890825871106 3.3464856647999284\n",
+      "0.05015358346473367 3.394243599534885\n",
+      "0.0752698620998299 3.514956130458809\n",
+      "0.10043372051146988 3.568593494783627\n",
+      "0.12566134685507416 3.589278208393522\n",
+      "0.1509692154967774 3.6101080442078324\n",
+      "0.1763741647808615 3.6513288001299844\n",
+      "0.20189347914185107 3.7161179951637706\n",
+      "0.22754497664114934 3.7944699457696127\n",
+      "0.2533471031357997 3.8610580033235102\n",
+      "0.27931903444745415 3.8651595744650127\n",
+      "0.3054807880993974 3.880317861920223\n",
+      "0.33185334643681663 4.037918567379938\n",
+      "0.3584587932511938 4.070646373432687\n",
+      "0.38532046640756773 4.126646238763533\n",
+      "0.41246312944140495 4.183776696909108\n",
+      "0.4399131656732339 4.3103267494988575\n",
+      "0.4676987991145084 4.368403949975774\n",
+      "0.49585034734745354 4.393047586088812\n",
+      "0.524400512708041 4.424797125042938\n",
+      "0.5533847195556731 4.425578587542548\n",
+      "0.5828415072712162 4.504790049704627\n",
+      "0.6128129910166272 4.676461286198656\n",
+      "0.643345405392917 4.693998033126645\n",
+      "0.6744897501960817 4.703004127096631\n",
+      "0.7063025628400874 4.7605315070102705\n",
+      "0.7388468491852137 4.7970050681736005\n",
+      "0.7721932141886848 4.800805939859362\n",
+      "0.8064212470182404 4.82396120650056\n",
+      "0.8416212335729143 4.872733861264402\n",
+      "0.8778962950512289 5.051392761520157\n",
+      "0.9153650878428143 5.089899851202905\n",
+      "0.9541652531461948 5.090685777502749\n",
+      "0.9944578832097535 5.273937823912279\n",
+      "1.0364333894937898 5.451511529907174\n",
+      "1.0803193408149558 5.50782795323656\n",
+      "1.1263911290388007 5.861020125536784\n",
+      "1.1749867920660904 5.872761362925223\n",
+      "1.2265281200366105 5.942584519352975\n",
+      "1.2815515655446004 5.992944615380574\n",
+      "1.3407550336902165 6.083465171963848\n",
+      "1.4050715603096329 6.116909885717236\n",
+      "1.475791028179171 6.174497584344344\n",
+      "1.554773594596854 6.330998693852813\n",
+      "1.6448536269514733 6.367221383706758\n",
+      "1.7506860712521708 6.677104617461088\n",
+      "1.8807936081512509 7.740946791561063\n",
+      "2.0537489106318225 8.014174254387822\n",
+      "2.3263478740408408 9.876203717194436\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(0, 99):\n",
+    "    print(q_theor[i], x[i])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.05453459,  0.07299602,  0.08623709,  0.10446233,  0.15851448,\n",
+       "        0.18458375,  0.20150273,  0.21028346,  0.27249451,  0.28125064,\n",
+       "        0.29580443,  0.32709507,  0.3772033 ,  0.38166116,  0.42811036,\n",
+       "        0.45137928,  0.50723761,  0.55478604,  0.56334899,  0.64950454,\n",
+       "        0.76474072,  0.76521235,  0.87307912,  0.89899011,  0.95043329,\n",
+       "        1.12808854,  1.14610091,  1.25851736,  1.33770233,  1.39219435,\n",
+       "        1.52274895,  1.59343191,  1.69085299,  1.7327203 ,  1.74018174,\n",
+       "        1.90914414,  1.98399318,  2.00937525,  2.04086769,  2.14261983,\n",
+       "        2.16636581,  2.30135756,  2.51698663,  2.53122317,  2.64698368,\n",
+       "        2.69677329,  2.76986921,  2.79985468,  3.02528142,  3.11727708,\n",
+       "        3.12076442,  3.12616163,  3.34400693,  3.4631832 ,  3.57913146,\n",
+       "        3.66436618,  3.73356895,  3.9490533 ,  3.97848235,  4.30003369,\n",
+       "        4.38542813,  4.50024736,  4.54757554,  4.56262388,  4.60967049,\n",
+       "        4.83027588,  5.73410685,  5.99658262,  6.07982919,  6.31885239,\n",
+       "        6.35774348,  6.55218942,  6.63466106,  6.99661702,  7.11240163,\n",
+       "        7.2458384 ,  7.25495054,  7.33338257,  7.54725061,  7.68067597,\n",
+       "        7.77566533,  8.09540064,  8.36807223,  8.68848875,  8.89880271,\n",
+       "        9.58245374,  9.8951606 , 10.22759712, 10.4546703 , 10.55138448,\n",
+       "       10.72725332, 10.79667305, 10.94364297, 11.63699725, 15.52094494,\n",
+       "       15.71362589, 17.47801989, 18.72355641, 21.83713049])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "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": "3.11.3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}