From 51c7ec4e9eb0a22b7ef07cd2e704369007b1f93c Mon Sep 17 00:00:00 2001
From: Valeriya Tolmacheva <valeriya.tolm@gmail.com>
Date: Wed, 15 Nov 2023 16:47:39 +0000
Subject: [PATCH] Lab Done

---
 your-code/continuous.ipynb | 535 ++++++++++++++++++++++++++++++++++---
 your-code/discrete.ipynb   | 177 ++++++++++--
 2 files changed, 650 insertions(+), 62 deletions(-)

diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb
index 32218e9..b953f26 100644
--- a/your-code/continuous.ipynb
+++ b/your-code/continuous.ipynb
@@ -35,9 +35,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.3668353  2.28697019 2.48720075 2.2537483  2.43086706 2.67145778\n",
+      " 2.78651867 2.2553763  2.2964616  2.36158472]\n"
+     ]
+    }
+   ],
    "source": [
     "from scipy.stats import uniform\n",
     "x = uniform.rvs(size=10)\n",
@@ -73,27 +82,331 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "def random_num(bottom,ceiling,count):\n",
+    "    counts = uniform.rvs(size=count)\n",
+    "    randoms  = bottom + (ceiling-bottom)*counts\n",
+    "    return randoms"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([13.57818897, 14.83241518, 14.77979791, 14.23096521, 13.82022402,\n",
+       "       11.62675308, 14.56293815, 14.2896679 , 14.11626258, 10.82305468,\n",
+       "       11.37358826, 11.85572052, 12.48876122, 13.98328405, 12.29131821,\n",
+       "       10.34131532, 10.68776635, 10.82233449, 12.61256095, 13.57353121,\n",
+       "       12.56783155, 13.13296575, 14.54432914, 12.49903046, 10.20792791,\n",
+       "       12.53060622, 12.64703692, 10.44557652, 13.48484059, 13.21982222,\n",
+       "       12.42694532, 13.94372927, 12.61141495, 14.35436857, 12.28999708,\n",
+       "       11.36667157, 12.95049461, 13.42085386, 12.40534715, 12.21732479,\n",
+       "       12.7829435 , 13.43635322, 10.49930172, 12.51070209, 14.61185292,\n",
+       "       12.63807089, 11.5719466 , 10.35676077, 11.50527265, 12.24954307,\n",
+       "       10.13370694, 10.24684182, 13.34859509, 14.24199728, 14.65278497,\n",
+       "       11.20385597, 11.32361146, 11.39432995, 11.25497473, 11.01320974,\n",
+       "       11.98960505, 12.31817417, 12.60863576, 12.36362995, 10.07767713,\n",
+       "       12.78249523, 11.32679861, 12.82351001, 11.51474296, 14.79304047,\n",
+       "       10.26185268, 14.30800303, 13.30420771, 12.72987111, 11.97632803,\n",
+       "       13.61223656, 11.51358343, 13.96846983, 12.41433517, 14.08554541,\n",
+       "       13.02314581, 11.68721168, 11.51654078, 14.03312343, 10.47013208,\n",
+       "       11.8167552 , 14.68524694, 12.97078781, 11.97677334, 11.09195317,\n",
+       "       12.59638105, 12.82739019, 12.02744526, 10.86255008, 13.51269711,\n",
+       "       11.54221894, 13.00725338, 12.77392441, 11.51797876, 10.26612151])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "How are the two distributions different?"
+    "func1=random_num(bottom=10,ceiling=15,count=100)\n",
+    "func1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([15.856081  , 48.42109738, 18.09516687, 13.58349979, 22.03717767,\n",
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+       "       20.78738203, 21.51833385, 36.13950134, 16.133883  , 51.1507214 ,\n",
+       "       52.86867605, 48.61753362, 54.70881378, 13.08861618, 12.41780503,\n",
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+       "       25.11252104, 55.20249621, 27.13642112, 38.31584867, 31.34841323,\n",
+       "       16.72646678, 29.03818576, 16.85081563, 33.57592566, 54.72306117,\n",
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+       "       37.75483712, 44.12911001, 54.45413795, 23.10446852, 39.23502101,\n",
+       "       14.15828115, 50.65871148, 29.44585804, 26.58115529, 55.96238733,\n",
+       "       42.78927477, 25.33296704, 59.30414807, 14.55592285, 55.60923417,\n",
+       "       48.47652181, 40.53378228, 47.48222373, 12.77650932, 55.41982016,\n",
+       "       26.86807518, 20.9810904 , 26.51015806, 27.24370303, 20.10409263,\n",
+       "       29.7868598 , 32.89342852, 17.49478189, 19.8813005 , 25.58398556,\n",
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+       "       38.74653142, 32.64579981, 46.85486366, 25.26633528, 23.68496134,\n",
+       "       55.10820632, 59.20058434, 46.50477635, 53.3558551 , 44.32148821,\n",
+       "       19.92610275, 49.97349234, 30.37999641, 50.63715075, 32.29459708,\n",
+       "       20.42257979, 38.08740728, 13.86001576, 55.24141986, 32.99751633,\n",
+       "       59.00082869, 54.01161905, 13.57886007, 50.37261661, 33.07913504,\n",
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+       "       16.0567851 , 53.86153206, 10.23709933, 34.86071744, 55.60472039,\n",
+       "       18.87565291, 18.53976357, 32.12392458, 50.58821145, 22.41348802,\n",
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+       "       49.5106125 , 30.52431402, 51.95306422, 36.72679868, 16.11453526,\n",
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+       "       31.88402643, 40.64735199, 58.01976182, 40.72442128, 26.53805245,\n",
+       "       41.57846105, 48.1579039 , 12.97038532, 30.6247024 , 24.79153292,\n",
+       "       39.68983688, 39.65612439, 17.30996371, 14.11398612, 58.14977598,\n",
+       "       43.72379094, 17.4813238 , 18.55724666, 15.79622051, 20.90144201,\n",
+       "       32.84337634, 25.6683742 , 32.35727175, 21.39468623, 35.7200128 ,\n",
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+       "       15.35076267, 36.09327823, 59.35549755, 50.80715406, 48.37424172,\n",
+       "       33.34314951, 51.74696267, 10.29267778, 19.43666452, 36.43844414,\n",
+       "       39.18004761, 16.10504792, 59.4239296 , 33.84119069, 53.62132876,\n",
+       "       34.98967887, 19.3977085 , 25.67384396, 30.53252059, 54.13851099,\n",
+       "       26.65873228, 54.25472235, 24.534801  , 39.56207962, 30.93897839,\n",
+       "       41.56789603, 50.81954433, 46.52150939, 56.75706225, 31.60594696,\n",
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+       "       18.64533403, 22.34847417, 57.03075525, 23.33765704, 34.50051443,\n",
+       "       29.34365901, 59.68336488, 24.60364178, 28.61981529, 48.56491012,\n",
+       "       29.00332488, 16.19046857, 38.11947025, 53.96210657, 10.02913302,\n",
+       "       25.29693045, 31.68939953, 16.56562152, 36.69573288, 45.60195943,\n",
+       "       54.6209851 , 14.94583801, 20.52294677, 52.2482188 , 27.69810426,\n",
+       "       12.1464582 , 42.26299351, 20.38261528, 41.90964804, 19.83072148,\n",
+       "       22.83547399, 49.52741695, 52.81036503, 35.23153435, 50.86111208,\n",
+       "       28.59242348, 35.85260471, 55.98479336, 34.46122074, 14.06169234,\n",
+       "       46.21788073, 19.01522798, 34.89182113, 37.67726761, 29.73873252,\n",
+       "       26.84868426, 16.37116846, 41.61761786, 29.18302577, 35.33067955,\n",
+       "       25.40523906, 36.34999186, 51.70006849, 17.65289525, 34.5957882 ,\n",
+       "       32.59095671, 48.00932603, 55.53630065, 21.06033124, 41.09872653,\n",
+       "       27.7408284 , 26.65330516, 23.79568138, 53.8966181 , 13.59677498,\n",
+       "       48.35468803, 17.95877291, 46.24270665, 25.50935922, 27.47165878,\n",
+       "       10.1494231 , 19.75580294, 12.3980928 , 26.06018742, 51.6297437 ,\n",
+       "       39.35826926, 51.26852867, 35.46786354, 32.17929136, 22.17957412,\n",
+       "       46.75853323, 51.3742661 , 56.66665392, 27.69867724, 50.17286501,\n",
+       "       48.85067341, 36.52579714, 10.12725607, 37.76790924, 47.74342263,\n",
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+       "       16.80110093, 21.53550711, 28.78811625, 42.4761959 , 27.851848  ,\n",
+       "       43.50404839, 36.9188145 , 24.99949777, 28.82634961, 27.39625777,\n",
+       "       32.72850737, 18.16346154, 11.48781618, 36.11725992, 43.32340037,\n",
+       "       44.21470926, 18.42967463, 47.44717259, 48.10881764, 48.87739977,\n",
+       "       14.97327056, 42.63219977, 33.01832031, 43.88510874, 57.6481936 ,\n",
+       "       34.12779473, 10.05000019, 35.47707391, 50.0106089 , 20.72303505,\n",
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+       "       42.26575242, 49.45056245, 28.22711115, 25.90706214, 48.70164191,\n",
+       "       49.63380773, 19.96704694, 45.12639886, 37.72742906, 44.03658499,\n",
+       "       37.93435197, 34.4217314 , 30.47722106, 32.22388401, 35.40179506,\n",
+       "       36.97635595, 19.3588311 , 46.30951489, 17.79932843, 57.60205485,\n",
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+       "       38.70369727, 40.22465261, 34.38951868, 41.50883258, 30.77319117,\n",
+       "       36.09214716, 37.93764567, 54.25933824, 43.58307217, 57.34219876,\n",
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+       "       31.53686183, 46.10350753, 37.33626381, 18.19096671, 55.9262887 ,\n",
+       "       46.61771046, 25.86239061, 31.20016303, 28.0292519 , 53.45817624,\n",
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+       "       37.32042239, 59.78222218, 53.48073319, 17.26732495, 30.28828335,\n",
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+       "       20.43839668, 55.56753258, 24.15278379, 19.59129612, 48.43630591,\n",
+       "       16.26131814, 55.37793466, 27.85529854, 18.42954878, 23.0182959 ,\n",
+       "       12.70662585, 10.2255562 , 43.39755782, 24.44406883, 28.67696775,\n",
+       "       28.77223934, 52.97971356, 26.63982751, 54.38402306, 24.12300944,\n",
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+       "       14.35434038, 14.03368168, 58.62275096, 48.44567248, 22.71108379,\n",
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+       "       39.08378232, 47.5073012 , 42.01919567, 57.44890336, 24.65361598,\n",
+       "       48.62268724, 58.94290303, 39.37987218, 20.00579798, 27.93836218,\n",
+       "       27.65268048, 15.29303359, 54.76701204, 39.47849105, 42.38967987,\n",
+       "       25.08267234, 24.01700044, 28.83363403, 55.53717757, 15.77412659,\n",
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+       "       35.81476658, 50.15946063, 38.03894432, 52.3112342 , 27.19880141,\n",
+       "       52.68177245, 45.72895283, 23.41268635, 46.38142383, 42.04132959,\n",
+       "       42.85334202, 13.70437636, 16.00938408, 53.7242545 , 41.74603194,\n",
+       "       42.79502398, 21.6296934 , 38.94448733, 23.41924631, 42.67736503,\n",
+       "       17.35738516, 49.86378587, 46.79062513, 35.96546513, 26.08812321,\n",
+       "       36.19048936, 46.89497217, 53.04382365, 46.28839254, 26.4056395 ,\n",
+       "       38.60022036, 32.6928286 , 57.83697074, 33.82955617, 28.48536618,\n",
+       "       56.62661494, 29.14231108, 31.52169537, 29.62761368, 48.19588768,\n",
+       "       19.65170771, 41.29457863, 46.1761432 , 48.10703379, 40.62530373,\n",
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+       "       59.22151327, 34.1205856 , 13.85350747, 40.67132062, 26.59843988,\n",
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+       "       41.11946769, 11.3641891 , 59.49557432, 16.04799896, 10.59586192,\n",
+       "       37.69114086, 24.37902528, 46.51279908, 13.02622408, 21.11377451,\n",
+       "       33.46813195, 29.15645001, 52.71551201, 40.11530329, 40.88034687,\n",
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+       "       35.78732896, 19.47092637, 36.43570973, 24.36354618, 54.93076392,\n",
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+       "       43.83317641, 55.57521188, 46.57163307, 56.83056939, 27.4665966 ,\n",
+       "       19.74652598, 15.78120863, 43.82925743, 49.05974639, 23.57402574,\n",
+       "       28.72603226, 50.49000734, 53.75045614, 50.77570034, 35.81257826,\n",
+       "       51.06319369, 46.35711022, 28.04747502, 57.29779318, 50.99419172,\n",
+       "       24.45084343, 29.07838846, 28.93877473, 41.46461923, 33.64198355,\n",
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+       "       45.61578162, 40.52218853, 33.94130039, 20.7055974 , 56.57735426,\n",
+       "       26.35741459, 44.78097637, 40.3098514 , 49.47406735, 23.84187433,\n",
+       "       24.56482235, 15.42850447, 13.66808021, 38.23250971, 21.84484659,\n",
+       "       23.01662103, 15.06527675, 53.90679437, 15.21902826, 49.85956106,\n",
+       "       40.23152916, 53.61514858, 46.79335751, 56.51384264, 55.25415733,\n",
+       "       10.49664932, 10.89806267, 22.53885537, 12.59482259, 51.13599329,\n",
+       "       49.46091515, 49.17413083, 40.97349932, 33.59361264, 47.44898401,\n",
+       "       23.41147078, 35.22250411, 38.56434955, 30.29013612, 40.92819565,\n",
+       "       54.958987  , 53.77997011, 46.18693986, 42.24950655, 57.47806411,\n",
+       "       20.00348785, 59.28861209, 19.47107834, 21.84006573, 37.54588418,\n",
+       "       19.95922374, 13.30851549, 50.84040807, 58.13533394, 42.10864159,\n",
+       "       59.51883857, 59.02360764, 56.6036967 , 38.13473993, 30.80093331,\n",
+       "       33.89450764, 56.06218854, 17.61093768, 42.74938013, 42.78739017,\n",
+       "       51.28426209, 54.13829299, 43.2009258 , 24.65668474, 36.2774699 ,\n",
+       "       21.15723706, 45.82153643, 17.29703999, 14.28695028, 30.95546985,\n",
+       "       41.69798346, 21.6301621 , 24.83748218, 27.49002368, 16.72420333,\n",
+       "       34.27447245, 24.63057392, 48.97020603, 27.10772771, 38.17602881,\n",
+       "       53.66867917, 17.80447303, 22.61253097, 47.84859872, 10.11440883,\n",
+       "       51.22632544, 55.15594249, 51.89900726, 45.05455905, 22.29111428,\n",
+       "       44.50709805, 55.65210273, 37.43882963, 23.45295315, 48.74544405,\n",
+       "       23.40135436, 40.94988039, 49.02571586, 26.73590768, 47.4554722 ,\n",
+       "       53.717986  , 16.39876276, 25.88365546, 44.19173657, 42.42401719,\n",
+       "       32.1912588 , 49.47344355, 41.02691782, 37.93189944, 12.24197098,\n",
+       "       41.07919111, 13.06498356, 56.63054535, 36.94046831, 52.98506976,\n",
+       "       21.21607179, 14.74273376, 59.73583957, 58.63601235, 52.92232567,\n",
+       "       50.70839022, 26.26485577, 46.39933364, 50.31877697, 38.7939338 ,\n",
+       "       33.12716966, 30.82609997, 14.6213069 , 28.98809072, 41.37309602,\n",
+       "       41.26843355, 18.93037636, 54.66228483, 27.55930027, 49.54060062,\n",
+       "       34.20693647, 52.3358897 , 10.71750651, 22.9376181 , 19.0552787 ,\n",
+       "       15.75651128, 24.19268679, 22.81524792, 38.69116768, 30.74869359,\n",
+       "       18.23646949, 38.41844176, 19.08630178, 47.70581577, 23.57447602,\n",
+       "       59.26792411, 53.08000025, 40.99522996, 20.55267045, 33.83576575,\n",
+       "       47.15757212, 33.34400652, 38.84336052, 23.1656987 , 33.88689102,\n",
+       "       28.77907298, 47.80907895, 14.93197006, 32.24467714, 57.29371839,\n",
+       "       59.53678381, 50.13243608, 36.76506263, 20.69389331, 35.60250886,\n",
+       "       43.73813535, 30.4129173 , 28.53247498, 17.10139675, 21.86934881,\n",
+       "       40.66828151, 28.77321851, 40.96593415, 52.34648256, 54.16176195,\n",
+       "       23.48102469, 10.26534236, 41.50667416, 16.31745799, 17.70650924,\n",
+       "       37.37817084, 23.61130468, 54.24462896, 49.2304957 , 53.14140015,\n",
+       "       19.84938533, 54.13705416, 50.1190635 , 25.64596809, 18.06807626,\n",
+       "       17.66292571, 27.13051778, 52.23491876, 50.71178341, 15.63487027,\n",
+       "       34.88932859, 43.27124153, 13.08116142, 16.29197337, 23.6698139 ,\n",
+       "       31.70468634, 16.494397  , 34.92631775, 18.93092401, 27.48297984,\n",
+       "       18.14073137, 36.54768619, 54.90622704, 42.85626232, 43.02173168,\n",
+       "       24.5006856 , 31.67738552, 46.43847288, 44.23861557, 52.80967792,\n",
+       "       26.22077439, 32.03373926, 33.88628724, 56.75675308, 54.95608526,\n",
+       "       24.76158329, 18.27942038, 12.44925556, 58.96503673, 59.04111562,\n",
+       "       26.10643046, 15.2075358 , 24.65875351, 39.02466664, 17.88799351,\n",
+       "       38.08554154, 12.87487967, 32.41720683, 23.25192714, 52.84140458,\n",
+       "       48.65892585, 19.08500767, 16.87170429, 20.21580422, 55.31928069,\n",
+       "       48.60248664, 27.27626253, 59.646894  , 28.36894782, 47.43567439,\n",
+       "       29.40063529, 48.63338103, 53.81572493, 21.26873707, 21.13538259,\n",
+       "       52.69119098, 20.66992668, 10.25379005, 18.95950378, 54.90950342,\n",
+       "       17.76843476, 49.15416625, 13.83027082, 12.51346377, 51.32191855,\n",
+       "       39.51189775, 47.12659192, 30.09833601, 19.50964264, 24.53645491,\n",
+       "       28.60556108, 18.04032663, 49.33501874, 44.98532234, 50.16238076,\n",
+       "       32.40007889, 13.98482925, 22.69052697, 17.17819296, 57.56160035,\n",
+       "       46.00667865, 21.75801716, 43.59912333, 28.14702049, 39.74949477,\n",
+       "       15.49427004, 49.55551056, 29.32710986, 24.44568791, 36.2187007 ,\n",
+       "       41.86265135, 19.18908998, 15.94062032, 39.12235442, 36.76552522,\n",
+       "       37.86357105, 52.12128384, 24.10749894, 49.61458447, 36.63743932,\n",
+       "       38.53866667, 41.54575513, 35.24742833, 53.06056702, 54.8409377 ,\n",
+       "       30.07677238, 31.52859659, 33.85145695, 45.08006332, 51.08998948,\n",
+       "       13.97103327, 58.77889568, 25.74917864, 25.69962069, 34.11732099,\n",
+       "       47.59666147, 42.41340793, 31.68101381, 16.75054821, 27.69934746,\n",
+       "       45.05402159, 12.92446958, 32.47950122, 16.8603244 , 57.89061137,\n",
+       "       43.86876418, 30.5353224 , 39.48535877, 36.51274428, 32.37846137,\n",
+       "       49.70011718, 26.19362388, 34.31681695, 45.79907906, 21.03915095,\n",
+       "       42.67461536, 33.82276993, 57.39044188, 27.82024205, 32.96690105,\n",
+       "       13.74316105, 14.88701736, 17.34166728, 35.38992584, 30.32703597])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "func2=random_num(bottom=10,ceiling=60,count=1000)\n",
+    "func2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here:\n"
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(func1,bins=10)\n",
+    "plt.ylim((0,110))\n",
+    "plt.show()\n",
+    "plt.hist(func2,bins=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How are the two distributions different?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Two distributions are very similar - with uniform distribution, you will always have natural fluctuations, but more or less both plots are flat.*"
    ]
   },
   {
@@ -114,11 +427,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "import numpy as np\n",
+    "def random_num_norm(avg,std,count):\n",
+    "    return np.random.normal(avg, std, count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "func1=random_num_norm(avg=10,std=1,count=1000)\n",
+    "func2=random_num_norm(avg=10,std=50,count=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(func1)\n",
+    "plt.show()\n",
+    "plt.hist(func2)\n",
+    "plt.ylim((0,250))\n",
+    "plt.show()"
    ]
   },
   {
@@ -129,12 +488,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# your answer here:\n"
+    "*Very similar, normal distribution.*"
    ]
   },
   {
@@ -158,11 +515,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "import pandas as pd\n",
+    "vehicles=pd.read_csv(\"vehicles.csv\")\n",
+    "vehicles[\"Fuel Barrels/Year\"].hist()\n",
+    "plt.show()"
    ]
   },
   {
@@ -174,11 +545,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "vehicles[\"CO2 Emission Grams/Mile\"].hist()\n",
+    "plt.show()"
    ]
   },
   {
@@ -190,11 +573,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "vehicles[\"Combined MPG\"].hist()\n",
+    "plt.show()"
    ]
   },
   {
@@ -206,11 +601,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# you answer here:\n"
+    "#Looks like all of them are? Combined MPG is the closest though."
    ]
   },
   {
@@ -237,27 +632,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "def random_num_exp(mean,count):\n",
+    "    return np.random.exponential(mean,size=count)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 47,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "How are the two distributions different?"
+    "func1=random_num_exp(mean=1,count=1000)\n",
+    "func2=random_num_exp(mean=100,count=1000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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vn8+n+Ph45ebmqqWlJeJFw64pq7eHDAAABiOs8DFp0iStW7dOb731lt566y3dc889+sEPfuAEjPXr16u0tFTl5eVqaGiQ1+tVXl6euru7h6R4AAAQfcIKH/Pnz9f3vvc93XDDDbrhhhv07LPP6oorrtCePXtkjFFZWZmKi4tVUFCgjIwMVVZW6tSpU6qqqhqq+gEAQJQZ8JmP06dPq7q6WidPntTs2bPV2tqqYDCo/Px8Z43b7VZOTo7q6+sjUiwAAIh+Yd9krLm5WbNnz9bnn3+uK664Qlu3btXUqVOdgOHxeELWezweHTly5ILP19vbq97eXudxV1dXuCUBAIAoEnb4uPHGG9XU1KQTJ05o8+bNWrRokerq6pzrLpcrZL0xpt/cV5WUlOjpp58Ot4wRjUOdAICRLOxfu4wePVrXX3+9MjMzVVJSohkzZui5556T1+uVJAWDwZD17e3t/XZDvqqoqEidnZ3OaGtrC7ckAAAQRQZ9nw9jjHp7e5WWliav16va2lrnWl9fn+rq6pSdnX3Bn3e73c5Hd88OAAAwcoX1a5c1a9Zozpw5Sk1NVXd3t6qrq7Vr1y7t2LFDLpdLhYWFCgQCSk9PV3p6ugKBgBISErRgwYKhqh8AAESZsMLHf/7zHz366KM6fvy4kpOTNX36dO3YsUN5eXmSpFWrVqmnp0dLly5VR0eHsrKyVFNTo8TExCEpHgAARJ+wwscLL7zwtdddLpf8fr/8fv9gagIAACMY3+0CAACsInwAAACrCB8AAMCqsG8yhuhx7s3KDq+bO0yVAADwP+x8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArIob7gIwvKas3h7y+PC6ucNUCQAgVrDzAQAArCJ8AAAAqwgfAADAKsIHAACwigOnMeTcw6UDXQMAwGCw8wEAAKwifAAAAKsIHwAAwCrCBwAAsIoDp7CGu6kCAKQwdz5KSkp0++23KzExURMnTtT999+vAwcOhKwxxsjv98vn8yk+Pl65ublqaWmJaNEAACB6hRU+6urqtGzZMu3Zs0e1tbX64osvlJ+fr5MnTzpr1q9fr9LSUpWXl6uhoUFer1d5eXnq7u6OePEAACD6hPVrlx07doQ83rRpkyZOnKjGxkZ95zvfkTFGZWVlKi4uVkFBgSSpsrJSHo9HVVVVWrx4ceQqBwAAUWlQB047OzslSePGjZMktba2KhgMKj8/31njdruVk5Oj+vr68z5Hb2+vurq6QgYAABi5Bhw+jDFauXKl7rrrLmVkZEiSgsGgJMnj8YSs9Xg8zrVzlZSUKDk52RmpqakDLQkAAESBAYeP5cuX6+2339Zf/vKXftdcLlfIY2NMv7mzioqK1NnZ6Yy2traBlgQAAKLAgD5q++STT2rbtm3avXu3Jk2a5Mx7vV5JX+6ApKSkOPPt7e39dkPOcrvdcrvdAykDAABEobB2PowxWr58ubZs2aLXX39daWlpIdfT0tLk9XpVW1vrzPX19amurk7Z2dmRqRgAAES1sHY+li1bpqqqKv3tb39TYmKic44jOTlZ8fHxcrlcKiwsVCAQUHp6utLT0xUIBJSQkKAFCxYMyRsAAADRJazwUVFRIUnKzc0Nmd+0aZMef/xxSdKqVavU09OjpUuXqqOjQ1lZWaqpqVFiYmJECsalibuXAgAuVljhwxjzjWtcLpf8fr/8fv9AawIAACMYXywHAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsCutbbYGLNWX19uEuIcT56jm8bu4wVAIAYOcDAABYRfgAAABWET4AAIBVhA8AAGAVB04RNpuHSTkoCgAjDzsfAADAKsIHAACwivABAACsInwAAACrOHCKS8qldmdUAEDksfMBAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArOIOpxg23M0UAGITOx8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwKqww8fu3bs1f/58+Xw+uVwuvfzyyyHXjTHy+/3y+XyKj49Xbm6uWlpaIlUvAACIcmGHj5MnT2rGjBkqLy8/7/X169ertLRU5eXlamhokNfrVV5enrq7uwddLAAAiH5h3+djzpw5mjNnznmvGWNUVlam4uJiFRQUSJIqKyvl8XhUVVWlxYsXD65aAAAQ9SJ65qO1tVXBYFD5+fnOnNvtVk5Ojurr68/7M729verq6goZAABg5Ipo+AgGg5Ikj8cTMu/xeJxr5yopKVFycrIzUlNTI1kSAAC4xAzJp11cLlfIY2NMv7mzioqK1NnZ6Yy2trahKAkAAFwiIvrdLl6vV9KXOyApKSnOfHt7e7/dkLPcbrfcbnckywAAAJewiO58pKWlyev1qra21pnr6+tTXV2dsrOzI/lSAAAgSoW98/HZZ5/pgw8+cB63traqqalJ48aN0zXXXKPCwkIFAgGlp6crPT1dgUBACQkJWrBgQUQLBwAA0Sns8PHWW2/p7rvvdh6vXLlSkrRo0SL96U9/0qpVq9TT06OlS5eqo6NDWVlZqqmpUWJiYuSqBobJlNXb+80dXjd32J4HAKJR2OEjNzdXxpgLXne5XPL7/fL7/YOpCwAAjFB8twsAALCK8AEAAKwifAAAAKsiep8PYDic7/DmpfbcQ1kjAEQbdj4AAIBVhA8AAGAV4QMAAFhF+AAAAFZx4BRRJ1YOb17MXVC5UyqAaMTOBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqbjIGXCKi8eZp59bMDc4AXAx2PgAAgFWEDwAAYBXhAwAAWEX4AAAAVnHgFPj/Bnrg81I7KDpUh0Aj1R8OpQJg5wMAAFhF+AAAAFYRPgAAgFWEDwAAYBUHThGzLrWDohfDZs0Dea1YOpR6vvd6bt0XswaIRex8AAAAqwgfAADAKsIHAACwivABAACs4sApMMIN9GDkULmY17rYg5oDOag63P24mJojdQA3Gg/yjlT8uwjFzgcAALCK8AEAAKwifAAAAKsIHwAAwCoOnAIxaKTe3XWgdxS91PtxMfVFw2Fb7gA7PC7FPg/Zzsfzzz+vtLQ0jRkzRrNmzdKbb745VC8FAACiyJCEj7/+9a8qLCxUcXGx9u/fr//7v//TnDlzdPTo0aF4OQAAEEWGJHyUlpbqxz/+sX7yk5/o5ptvVllZmVJTU1VRUTEULwcAAKJIxM989PX1qbGxUatXrw6Zz8/PV319fb/1vb296u3tdR53dnZKkrq6uiJdmiTpTO+pIXleAJemc/8uGcq/AwbyWuf7u24gNQ70eS61/gzV3/3D7dz3avN92urz2ec0xnzzYhNhx44dM5LMv/71r5D5Z5991txwww391q9du9ZIYjAYDAaDMQJGW1vbN2aFIfu0i8vlCnlsjOk3J0lFRUVauXKl8/jMmTP69NNPNX78+POuH4yuri6lpqaqra1NSUlJEX3uaEQ/+qMnoehHf/QkFP3oL1Z7YoxRd3e3fD7fN66NePi4+uqrdfnllysYDIbMt7e3y+Px9FvvdrvldrtD5q688spIlxUiKSkppv5AfBP60R89CUU/+qMnoehHf7HYk+Tk5ItaF/EDp6NHj9asWbNUW1sbMl9bW6vs7OxIvxwAAIgyQ/Jrl5UrV+rRRx9VZmamZs+erQ0bNujo0aNasmTJULwcAACIIkMSPh588EF98skneuaZZ3T8+HFlZGTolVde0eTJk4fi5S6a2+3W2rVr+/2aJ1bRj/7oSSj60R89CUU/+qMn38xlzMV8JgYAACAy+GI5AABgFeEDAABYRfgAAABWET4AAIBVMRM+nn/+eaWlpWnMmDGaNWuW3nzzzeEuacjs3r1b8+fPl8/nk8vl0ssvvxxy3Rgjv98vn8+n+Ph45ebmqqWlJWRNb2+vnnzySV199dUaO3asvv/97+vDDz+0+C4ip6SkRLfffrsSExM1ceJE3X///Tpw4EDImljqSUVFhaZPn+7cAGn27Nl69dVXneux1IvzKSkpkcvlUmFhoTMXaz3x+/1yuVwhw+v1OtdjrR+SdOzYMT3yyCMaP368EhISdOutt6qxsdG5Hos9GZRBfpVLVKiurjajRo0yGzduNO+++65ZsWKFGTt2rDly5MhwlzYkXnnlFVNcXGw2b95sJJmtW7eGXF+3bp1JTEw0mzdvNs3NzebBBx80KSkppqury1mzZMkS861vfcvU1taaffv2mbvvvtvMmDHDfPHFF5bfzeB997vfNZs2bTLvvPOOaWpqMnPnzjXXXHON+eyzz5w1sdSTbdu2me3bt5sDBw6YAwcOmDVr1phRo0aZd955xxgTW7041969e82UKVPM9OnTzYoVK5z5WOvJ2rVrzS233GKOHz/ujPb2dud6rPXj008/NZMnTzaPP/64+fe//21aW1vNzp07zQcffOCsibWeDFZMhI9vf/vbZsmSJSFzN910k1m9evUwVWTPueHjzJkzxuv1mnXr1jlzn3/+uUlOTjZ/+MMfjDHGnDhxwowaNcpUV1c7a44dO2Yuu+wys2PHDmu1D5X29nYjydTV1Rlj6Ikxxlx11VXmj3/8Y0z3oru726Snp5va2lqTk5PjhI9Y7MnatWvNjBkzznstFvvxq1/9ytx1110XvB6LPRmsEf9rl76+PjU2Nio/Pz9kPj8/X/X19cNU1fBpbW1VMBgM6Yfb7VZOTo7Tj8bGRv33v/8NWePz+ZSRkTEietbZ2SlJGjdunKTY7snp06dVXV2tkydPavbs2THdi2XLlmnu3Lm67777QuZjtScHDx6Uz+dTWlqaHnroIR06dEhSbPZj27ZtyszM1AMPPKCJEydq5syZ2rhxo3M9FnsyWCM+fHz88cc6ffp0vy+183g8/b78Lhacfc9f149gMKjRo0frqquuuuCaaGWM0cqVK3XXXXcpIyNDUmz2pLm5WVdccYXcbreWLFmirVu3aurUqTHZC0mqrq7Wvn37VFJS0u9aLPYkKytLL774ol577TVt3LhRwWBQ2dnZ+uSTT2KyH4cOHVJFRYXS09P12muvacmSJXrqqaf04osvSorNPyODNSS3V78UuVyukMfGmH5zsWQg/RgJPVu+fLnefvtt/fOf/+x3LZZ6cuONN6qpqUknTpzQ5s2btWjRItXV1TnXY6kXbW1tWrFihWpqajRmzJgLroulnsyZM8f552nTpmn27Nm67rrrVFlZqTvuuENSbPXjzJkzyszMVCAQkCTNnDlTLS0tqqio0GOPPeasi6WeDNaI3/m4+uqrdfnll/dLlu3t7f1Saiw4e2L96/rh9XrV19enjo6OC66JRk8++aS2bdumN954Q5MmTXLmY7Eno0eP1vXXX6/MzEyVlJRoxowZeu6552KyF42NjWpvb9esWbMUFxenuLg41dXV6Xe/+53i4uKc9xRLPTnX2LFjNW3aNB08eDAm/4ykpKRo6tSpIXM333yzjh49Kik2/w4ZrBEfPkaPHq1Zs2aptrY2ZL62tlbZ2dnDVNXwSUtLk9frDelHX1+f6urqnH7MmjVLo0aNCllz/PhxvfPOO1HZM2OMli9fri1btuj1119XWlpayPVY7Mm5jDHq7e2NyV7ce++9am5uVlNTkzMyMzO1cOFCNTU16dprr425npyrt7dX7733nlJSUmLyz8idd97Z7+P577//vvNlqbHYk0Gzf8bVvrMftX3hhRfMu+++awoLC83YsWPN4cOHh7u0IdHd3W32799v9u/fbySZ0tJSs3//fuejxevWrTPJyclmy5Ytprm52Tz88MPn/UjYpEmTzM6dO82+ffvMPffcE7UfCfvZz35mkpOTza5du0I+Onjq1ClnTSz1pKioyOzevdu0traat99+26xZs8ZcdtllpqamxhgTW724kK9+2sWY2OvJL37xC7Nr1y5z6NAhs2fPHjNv3jyTmJjo/J0Za/3Yu3eviYuLM88++6w5ePCg+fOf/2wSEhLMSy+95KyJtZ4MVkyED2OM+f3vf28mT55sRo8ebW677TbnY5Yj0RtvvGEk9RuLFi0yxnz5sbC1a9car9dr3G63+c53vmOam5tDnqOnp8csX77cjBs3zsTHx5t58+aZo0ePDsO7Gbzz9UKS2bRpk7MmlnryxBNPOP8tTJgwwdx7771O8DAmtnpxIeeGj1jrydl7VIwaNcr4fD5TUFBgWlpanOux1g9jjPn73/9uMjIyjNvtNjfddJPZsGFDyPVY7MlguIwxZnj2XAAAQCwa8Wc+AADApYXwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwKr/B+6WQhTeeb9oAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your answer here:\n"
+    "plt.hist(func1,bins=100)\n",
+    "plt.show()\n",
+    "plt.hist(func2,bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How are the two distributions different?"
    ]
   },
   {
@@ -273,12 +703,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7768698398515702"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# your answer here\n",
-    "# Hint: This is same as saying P(x<15)\n"
+    "# Hint: This is same as saying P(x<15)\n",
+    "from scipy.stats import expon\n",
+    "\n",
+    "lambda_inv=10\n",
+    "expon_dist = expon(scale=lambda_inv)\n",
+    "expon_dist.cdf(15)"
    ]
   },
   {
@@ -290,11 +736,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2231301601484298"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here\n"
+    "1-expon_dist.cdf(15)"
    ]
   }
  ],
@@ -314,7 +771,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb
index 310a3c6..83b4999 100644
--- a/your-code/discrete.ipynb
+++ b/your-code/discrete.ipynb
@@ -17,6 +17,19 @@
     "- Round the final answer to three decimal places."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from scipy.stats import bernoulli, binom, geom, poisson, uniform, expon, norm"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,9 +46,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6\n",
+      "0.4\n"
+     ]
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "Calculate:\n",
@@ -43,7 +65,10 @@
     "q = probability that the fruit is an orange\n",
     "\"\"\"\n",
     "\n",
-    "# your code here\n"
+    "p=60/100\n",
+    "print(p)\n",
+    "q=40/100\n",
+    "print(q)"
    ]
   },
   {
@@ -61,11 +86,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.078"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#independent events -- so getting first 5 apples is multiplying probability of getting an apple 5 times\n",
+    "round(p**5,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "#independent events: first event is taking first 5 apples, then second event is taking 15 oranges\n",
+    "round((p**5)*(q**15),3)"
    ]
   },
   {
@@ -83,11 +141,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0012944935222876583"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "n=20\n",
+    "#let's define success as picking an apple. fail then means picking an orange\n",
+    "# so we are looking at pmf(5) - within 20 trials, 5 successes and 15 fails\n",
+    "binomial_dist=binom(n,p)\n",
+    "binomial_dist.pmf(5)"
    ]
   },
   {
@@ -101,11 +174,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00031703112116863004"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "#probability of less than 5 successes => 4 or less\n",
+    "binomial_dist.cdf(4)"
    ]
   },
   {
@@ -119,12 +204,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n",
-    "# Please label the axes and give a title to the plot\n"
+    "X=np.arange(0,n)\n",
+    "plt.plot(X,binomial_dist.pmf(X),\"o\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -151,11 +248,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "#did not understand why we need to do factorials for this?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.053775025581946814"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mu=2.3 #avg goals per match\n",
+    "poisson_dist=poisson(mu)\n",
+    "poisson_dist.pmf(5)"
    ]
   },
   {
@@ -167,12 +286,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n",
-    "# Please label the axes and give a title to the plot \n"
+    "X=np.arange(0,10)\n",
+    "plt.plot(poisson_dist.pmf(X),\"o\")\n",
+    "plt.show()"
    ]
   }
  ],
@@ -192,7 +323,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,