diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb
index 32218e9..5f3bb74 100644
--- a/your-code/continuous.ipynb
+++ b/your-code/continuous.ipynb
@@ -35,9 +35,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.7204922  2.39713182 2.69800202 2.25051012 2.64920147 2.69576923\n",
+      " 2.48965268 2.00510096 2.29264729 2.96271501]\n"
+     ]
+    }
+   ],
    "source": [
     "from scipy.stats import uniform\n",
     "x = uniform.rvs(size=10)\n",
@@ -73,11 +91,302 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12.33718905, 11.0750868 , 12.44695416, 13.42867299, 12.72556117,\n",
+       "       14.75525993, 10.73422521, 11.02004993, 14.71446431, 11.41960452,\n",
+       "       10.47393969, 10.13936838, 14.05882394, 11.35581328, 11.05479014,\n",
+       "       13.62117929, 14.70160455, 11.77521545, 11.65239377, 11.49580115,\n",
+       "       10.79398045, 13.19855526, 14.00645604, 13.13409787, 13.91031502,\n",
+       "       12.81199116, 11.21864786, 14.07859636, 12.12023399, 13.79315707,\n",
+       "       11.16549822, 10.18722377, 11.67697482, 11.92461364, 12.57385645,\n",
+       "       11.61218947, 13.85834328, 14.9818424 , 10.32395485, 14.50209583,\n",
+       "       11.15979604, 10.31343541, 13.84592029, 12.41748477, 10.03601332,\n",
+       "       12.99380838, 10.50865484, 12.80472978, 13.7626427 , 14.02294198,\n",
+       "       14.92329703, 10.08525431, 10.0386096 , 14.80119189, 14.957485  ,\n",
+       "       11.99805367, 11.4690796 , 14.17385411, 12.45361509, 10.43011129,\n",
+       "       13.65211688, 10.82144345, 13.06691186, 13.25056319, 10.56597256,\n",
+       "       10.76591179, 12.83497138, 13.05103601, 13.98121636, 13.45076544,\n",
+       "       14.39329797, 12.33460382, 11.55310751, 13.7223972 , 12.92425181,\n",
+       "       11.35063766, 13.02586463, 12.83822955, 14.6678038 , 10.76197709,\n",
+       "       12.63048714, 13.57723538, 11.26198103, 14.12832809, 12.22075911,\n",
+       "       14.27760563, 11.12504762, 11.55824387, 12.67818531, 10.33752062,\n",
+       "       10.06616238, 14.52714773, 12.21119348, 10.58919382, 12.69995967,\n",
+       "       10.79133957, 14.16477129, 13.19357189, 11.8911488 , 10.07504428])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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\n",
+    "func1=random_num(bottom=10,ceiling=15,count=100)\n",
+    "func1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([30.20903741, 59.48760314, 11.0056283 , 14.04664224, 54.25722314,\n",
+       "       11.84317939, 17.02711794, 47.23191433, 39.48302346, 38.52184519,\n",
+       "       52.28500738, 16.22779912, 31.60426425, 39.89118375, 57.46961354,\n",
+       "       15.844262  , 14.21265669, 17.7695539 , 31.42529709, 46.69347804,\n",
+       "       31.48231039, 25.91096105, 24.58474214, 52.00291058, 18.64023198,\n",
+       "       33.3682499 , 45.18766939, 47.62431166, 32.8405772 , 43.98956669,\n",
+       "       52.17554076, 39.15792059, 30.50802647, 58.59361398, 55.79759731,\n",
+       "       45.16169813, 59.32686339, 18.15502242, 38.02550388, 36.92562697,\n",
+       "       12.8271677 , 38.63176714, 20.98445038, 45.4356627 , 20.96286782,\n",
+       "       56.67344997, 15.84824581, 24.77264795, 19.46357489, 29.44422286,\n",
+       "       25.06181104, 54.29392152, 37.31385817, 23.6566519 , 43.35794149,\n",
+       "       47.51290057, 20.51819179, 22.27297977, 31.5923205 , 42.42725978,\n",
+       "       27.88194969, 55.29207863, 28.79121717, 19.13466238, 16.04471054,\n",
+       "       21.48848193, 22.04396524, 11.80166921, 50.4854682 , 34.23133691,\n",
+       "       37.20326701, 56.0344182 , 32.46402533, 52.3535015 , 35.70022254,\n",
+       "       57.5009903 , 22.88507502, 15.54417936, 17.6584492 , 57.04798182,\n",
+       "       24.41201755, 39.20657139, 39.88192707, 51.82862348, 50.48957062,\n",
+       "       34.13383875, 54.52046933, 42.05525167, 11.37466477, 18.73163615,\n",
+       "       59.56015492, 28.1999868 , 35.10742511, 50.64984205, 43.15390863,\n",
+       "       46.38129604, 50.87698714, 47.45559655, 12.49693045, 55.45817171,\n",
+       "       24.24529606, 24.07416638, 23.36060703, 45.57898507, 39.39208617,\n",
+       "       18.88628224, 43.30032831, 34.60333   , 30.87523018, 21.18068876,\n",
+       "       32.77282935, 10.03074517, 24.89623027, 52.96863911, 30.72711573,\n",
+       "       37.48175217, 30.00156145, 52.38122585, 36.93049925, 14.92957084,\n",
+       "       16.66574843, 40.08381428, 31.55746747, 47.39782022, 48.2812376 ,\n",
+       "       42.6310624 , 27.24908695, 31.43299958, 29.34847557, 48.20749493,\n",
+       "       47.27079269, 42.98252442, 21.89423546, 29.34469077, 53.49578502,\n",
+       "       32.59977039, 38.99866242, 21.52893068, 17.09356456, 47.21239925,\n",
+       "       54.74082095, 20.28244292, 40.6032971 , 26.72498953, 49.72550258,\n",
+       "       56.83415065, 38.47258674, 34.73022001, 53.21390151, 35.05453094,\n",
+       "       37.89831698, 58.18333804, 44.66156262, 21.68369176, 46.66979169,\n",
+       "       38.65884228, 13.39031393, 32.20324433, 33.32439764, 23.09926647,\n",
+       "       14.61863697, 55.47483142, 40.68657129, 42.99794834, 27.05548738,\n",
+       "       34.62549236, 22.14119194, 43.8168695 , 37.63406417, 17.96680505,\n",
+       "       55.02768609, 19.14564186, 14.14983195, 14.26272427, 49.16306834,\n",
+       "       51.67294045, 30.3312386 , 53.88932686, 46.07262539, 38.62096828,\n",
+       "       11.05350668, 33.45997615, 26.61125556, 47.15156549, 55.0550302 ,\n",
+       "       27.05944784, 56.4973989 , 12.2578606 , 35.55227622, 24.16468124,\n",
+       "       32.05048069, 53.61393027, 34.87908073, 31.43587861, 50.72952847,\n",
+       "       41.30648664, 35.47428415, 35.86639487, 24.34630026, 59.43398558,\n",
+       "       46.95446951, 32.35646   , 40.39207276, 18.50805612, 21.52799784,\n",
+       "       16.90294235, 14.48997846, 20.7693001 , 43.3221989 , 40.67393451,\n",
+       "       43.86309687, 53.63437661, 46.22026323, 37.67252001, 49.1122121 ,\n",
+       "       33.29413082, 32.63701127, 32.97597061, 25.34069636, 30.5179409 ,\n",
+       "       18.24260957, 59.30612053, 44.09200828, 36.58223017, 26.86725274,\n",
+       "       56.6868909 , 22.68011752, 45.55174846, 47.30663714, 54.45332879,\n",
+       "       29.89972556, 15.25890905, 44.61363763, 44.39180442, 57.5713431 ,\n",
+       "       20.52820537, 44.6164795 , 58.32447971, 15.04229171, 31.19231753,\n",
+       "       50.8023621 , 31.38150804, 56.02357112, 41.41878834, 22.65985104,\n",
+       "       49.98327138, 12.87007809, 45.23430883, 40.67360852, 53.79043009,\n",
+       "       48.8568581 , 30.40158154, 11.42095358, 54.22222121, 38.51350382,\n",
+       "       15.9462018 , 51.81065227, 36.61215053, 37.39544185, 41.38979471,\n",
+       "       30.06400814, 28.24971038, 25.30576215, 19.59064788, 12.34196534,\n",
+       "       51.71416337, 40.18830362, 23.31567993, 13.65553663, 54.85018746,\n",
+       "       42.87859855, 26.20480894, 29.90110978, 42.76325748, 25.75306161,\n",
+       "       31.01596749, 35.90787842, 31.96205415, 22.42243802, 18.89674255,\n",
+       "       51.84341766, 54.56671009, 55.9815015 , 52.22767535, 33.75626137,\n",
+       "       37.54233748, 58.05379748, 26.56336725, 24.26676055, 28.69979471,\n",
+       "       34.62124715, 31.43153653, 11.35014132, 26.40361722, 31.33524994,\n",
+       "       38.16971769, 57.41008098, 55.69850204, 45.73627148, 30.15986489,\n",
+       "       50.7146258 , 26.03100216, 14.4591861 , 49.62639179, 19.75988503,\n",
+       "       54.22777683, 15.3565549 , 49.37440707, 19.71011187, 43.22261548,\n",
+       "       23.49685077, 11.35312619, 40.69315081, 42.73920055, 22.4099    ,\n",
+       "       35.30018404, 23.52500752, 32.56002401, 50.27806188, 57.94917359,\n",
+       "       43.43946265, 18.3618868 , 36.15980396, 20.47040116, 26.54000388,\n",
+       "       17.3552419 , 39.92067139, 50.87184623, 47.35582505, 11.77292683,\n",
+       "       23.08500442, 12.23202108, 20.4554812 , 11.89203203, 43.43075873,\n",
+       "       42.28799919, 55.92392228, 46.72075561, 57.97420293, 41.18379868,\n",
+       "       50.95519537, 19.0958652 , 56.36929682, 37.23270731, 45.36403975,\n",
+       "       30.07129861, 39.07635991, 13.48582288, 21.77336482, 18.86044232,\n",
+       "       47.78539872, 35.05679338, 27.4870266 , 22.18043275, 19.9863036 ,\n",
+       "       16.59361542, 35.84657137, 26.56391382, 58.08486789, 35.09275503,\n",
+       "       49.69494018, 50.13569387, 48.70718823, 33.70312714, 37.11029368,\n",
+       "       56.82578984, 24.95188772, 44.45726247, 44.13895083, 48.48461733,\n",
+       "       44.95605477, 21.00320868, 54.66852212, 12.85130994, 12.83560997,\n",
+       "       36.27704794, 49.78725775, 59.53722839, 50.16940594, 32.09847337,\n",
+       "       18.83180083, 39.49301947, 45.7206041 , 52.47415688, 42.39212883,\n",
+       "       18.4941861 , 28.79843575, 19.60407875, 14.80161643, 35.90456569,\n",
+       "       55.26240341, 33.89085622, 47.98964319, 12.39802853, 30.44216969,\n",
+       "       45.74521239, 50.80538893, 39.20947095, 14.9002116 , 39.20624829,\n",
+       "       46.71751694, 14.6855242 , 19.70141175, 34.65221215, 41.2759893 ,\n",
+       "       32.34070199, 27.73193783, 33.40292824, 14.83105614, 32.81733846,\n",
+       "       47.65560356, 14.10425477, 40.81827294, 14.38129222, 44.29657259,\n",
+       "       33.93726564, 30.7299549 , 14.09050651, 33.92268969, 11.91242892,\n",
+       "       53.51012515, 46.33014847, 52.17357656, 59.58863867, 17.8393306 ,\n",
+       "       53.50128188, 32.85758223, 50.2785263 , 55.6449144 , 47.16521264,\n",
+       "       59.41305063, 59.56603894, 41.08380777, 23.84581535, 26.84959412,\n",
+       "       49.88330905, 26.13327975, 45.03587264, 46.63120769, 14.9506354 ,\n",
+       "       56.73143232, 16.35133826, 18.67682444, 51.61402007, 25.77657092,\n",
+       "       56.54008942, 30.22137769, 50.3452326 , 22.03678513, 43.31467316,\n",
+       "       59.94586371, 57.85971925, 33.60718533, 37.33812371, 27.18948198,\n",
+       "       43.34405215, 57.18870283, 53.74568316, 19.31059073, 22.66385821,\n",
+       "       43.82320232, 31.90598611, 33.23844482, 54.10110321, 56.34762848,\n",
+       "       52.279368  , 12.15097039, 23.2878073 , 29.26836835, 31.02192616,\n",
+       "       46.61445901, 35.07259963, 10.06670731, 26.55538466, 57.81899843,\n",
+       "       32.96341755, 23.5268922 , 39.27158453, 36.38097544, 22.00337863,\n",
+       "       35.13077663, 32.31982169, 54.64239127, 36.48542158, 37.32774852,\n",
+       "       26.53805823, 55.23283462, 16.07097347, 44.21350636, 59.0698437 ,\n",
+       "       20.73886054, 51.31406355, 45.56487034, 20.819641  , 15.31847644,\n",
+       "       52.97480698, 23.7668777 , 35.49091975, 56.51971325, 56.91919653,\n",
+       "       16.95380195, 40.57910603, 34.39467574, 49.92597136, 57.67944961,\n",
+       "       30.11752227, 49.58739369, 13.50504396, 55.22930215, 32.32923816,\n",
+       "       46.90047044, 42.90160551, 21.06898559, 58.73857278, 38.61217599,\n",
+       "       58.32386769, 12.97270625, 30.07203692, 37.57051997, 35.36543534,\n",
+       "       23.10547421, 39.49889125, 28.359124  , 30.44942473, 44.11497829,\n",
+       "       33.55307545, 44.49708705, 56.33821933, 40.17167799, 22.86926759,\n",
+       "       48.13548398, 56.7766269 , 59.64155626, 39.48159146, 43.17775294,\n",
+       "       17.60898769, 24.98993561, 13.91223289, 29.88942631, 39.50288042,\n",
+       "       56.10263994, 14.91845841, 54.31635245, 14.25819046, 55.83546547,\n",
+       "       51.75191893, 40.58767865, 39.85371042, 36.4977462 , 21.22116187,\n",
+       "       57.13558408, 36.53345673, 41.17063378, 17.17220076, 16.76290863,\n",
+       "       48.86343519, 39.29183757, 58.81206817, 42.68864625, 21.20311075,\n",
+       "       22.99266045, 15.01432794, 33.32104659, 36.5814236 , 43.31003644,\n",
+       "       34.33607379, 10.68975186, 52.26857961, 17.62117195, 17.67906458,\n",
+       "       26.30828841, 55.20357623, 28.54506539, 37.13706581, 51.42058119,\n",
+       "       46.21666019, 39.6754824 , 44.60556282, 54.67205034, 29.84573495,\n",
+       "       29.33657579, 26.3800708 , 37.72612886, 55.99018541, 13.48746482,\n",
+       "       46.80316459, 56.42348435, 57.88684699, 32.37596826, 42.62250447,\n",
+       "       16.84517564, 57.91270037, 49.30768931, 33.84798083, 52.08116223,\n",
+       "       21.62421343, 47.52253193, 20.93108027, 21.65978026, 50.36774693,\n",
+       "       59.77489664, 13.15254476, 59.95241031, 11.54655154, 54.44527417,\n",
+       "       37.42290677, 24.1298779 , 23.17766328, 15.3680161 , 33.86058034,\n",
+       "       59.46169027, 27.36972199, 15.56495663, 41.41575253, 14.96450184,\n",
+       "       26.39480167, 37.3109712 , 19.35482481, 58.21671172, 50.72502946,\n",
+       "       46.24836193, 11.02752657, 40.75467094, 46.99427179, 55.32429326,\n",
+       "       53.78086947, 36.99008547, 24.60774675, 20.00639535, 28.3406728 ,\n",
+       "       11.55952766, 17.74767227, 43.99949517, 55.4704404 , 20.00388106,\n",
+       "       36.73919311, 12.00637977, 47.11652999, 26.99836281, 29.6484932 ,\n",
+       "       45.0807013 , 37.78406058, 58.39956641, 11.99939221, 56.19922821,\n",
+       "       44.34022686, 35.96684726, 10.12726588, 20.84388521, 23.69208239,\n",
+       "       39.53335092, 19.93999922, 23.86571462, 20.75148616, 57.1046325 ,\n",
+       "       38.3660594 , 29.31887824, 36.78625972, 10.19592209, 53.21003706,\n",
+       "       19.46435442, 15.89693843, 51.65165567, 24.87471651, 59.85208555,\n",
+       "       35.78866724, 33.66610754, 34.72814318, 34.39681513, 58.66470687,\n",
+       "       37.86862802, 16.18526908, 30.55021669, 41.69310469, 37.88289142,\n",
+       "       34.87975046, 25.29622146, 37.42587467, 59.85341282, 27.38659922,\n",
+       "       51.57496886, 30.79643989, 38.77304903, 51.83630312, 48.52198063,\n",
+       "       32.10579757, 22.72427361, 28.04387837, 28.79542413, 59.31329339,\n",
+       "       58.62971687, 17.53629895, 40.27662207, 42.2153987 , 49.03669925,\n",
+       "       55.41246153, 40.34977435, 58.13092613, 34.31283891, 21.28948964,\n",
+       "       55.2199072 , 37.60247082, 19.45984732, 21.0774901 , 13.34461441,\n",
+       "       15.68970624, 57.1474243 , 13.88974853, 38.48516915, 52.09011405,\n",
+       "       47.15149415, 57.99784867, 42.14838526, 58.92407077, 17.62653544,\n",
+       "       24.23589725, 11.17912566, 11.65230429, 53.06949466, 35.62894742,\n",
+       "       29.91134471, 49.160005  , 43.22319088, 33.47271619, 58.71882962,\n",
+       "       54.65301343, 40.36781949, 52.99955681, 49.64893075, 26.68670167,\n",
+       "       28.87795345, 53.30734635, 18.01106756, 11.24870738, 56.99673993,\n",
+       "       19.80623993, 57.84638357, 43.58738176, 15.90808484, 57.00347528,\n",
+       "       48.81240373, 51.22568855, 46.85825188, 16.36644161, 29.22381378,\n",
+       "       20.7465478 , 58.52403084, 17.83858437, 54.52694155, 34.07189948,\n",
+       "       44.55999223, 11.85165647, 51.75835175, 54.84576735, 47.26847174,\n",
+       "       49.31792047, 22.48556151, 14.66897798, 56.81292208, 54.08524558,\n",
+       "       48.64880841, 16.36956836, 38.42844754, 31.16965978, 51.03787821,\n",
+       "       11.83345827, 46.20370222, 20.61822338, 52.04300604, 10.7366997 ,\n",
+       "       43.65133726, 31.71345611, 34.89672704, 19.02417761, 25.66888871,\n",
+       "       44.26345329, 40.32298186, 26.45515376, 20.46771465, 45.74718993,\n",
+       "       29.02674613, 57.79513955, 55.11645543, 58.57030468, 14.7757644 ,\n",
+       "       20.48672929, 56.12237411, 36.67270985, 54.38638553, 43.82539704,\n",
+       "       36.81757822, 38.55900786, 50.53393246, 41.65996163, 19.31506003,\n",
+       "       47.91836642, 53.89277763, 19.01465879, 54.32678754, 45.47166166,\n",
+       "       31.50975595, 34.94459214, 44.02021467, 34.029116  , 24.53096408,\n",
+       "       18.98781236, 40.00056565, 55.59158169, 54.35120617, 43.92310275,\n",
+       "       38.34855714, 54.25697737, 19.4394368 , 25.93933666, 50.99389488,\n",
+       "       45.76944682, 29.79412561, 36.13159331, 50.15807176, 13.5576053 ,\n",
+       "       49.74362848, 59.49625253, 36.40203147, 32.4654177 , 36.37686929,\n",
+       "       18.85730543, 25.80259402, 37.69320247, 46.60070149, 24.53047227,\n",
+       "       17.84587841, 22.68958745, 20.4422289 , 41.85442658, 12.15279969,\n",
+       "       52.039448  , 42.29739618, 13.00109157, 40.22094479, 30.56022575,\n",
+       "       10.56494757, 22.51323984, 35.17549603, 19.95738121, 33.07829329,\n",
+       "       55.40751575, 23.50480773, 49.25337836, 39.33424656, 28.49738932,\n",
+       "       47.0267199 , 55.89471749, 57.93969575, 51.1791786 , 35.70263018,\n",
+       "       14.03919041, 35.07368926, 57.61221805, 56.00204568, 41.13203206,\n",
+       "       21.19673275, 37.17563203, 25.47041538, 26.64190253, 52.5248156 ,\n",
+       "       43.13345945, 50.78276205, 21.99149418, 14.51038797, 20.26176436,\n",
+       "       36.16545143, 40.86754744, 22.47688538, 55.56866599, 13.84534534,\n",
+       "       12.75384769, 59.8416066 , 31.46190591, 31.30558013, 17.17515164,\n",
+       "       54.65566366, 57.53742692, 10.85666216, 20.7301139 , 57.43544937,\n",
+       "       58.36657002, 11.91233162, 22.42959797, 49.63846966, 18.51502534,\n",
+       "       10.09082539, 31.13523025, 41.34002646, 35.18176026, 44.17659494,\n",
+       "       41.86317201, 50.17760802, 40.20439578, 33.34974253, 26.95452103,\n",
+       "       30.06203344, 34.52856961, 16.39774794, 51.78045926, 11.15341559,\n",
+       "       11.66023695, 51.55113866, 30.17421534, 50.44697333, 17.05948945,\n",
+       "       41.95559638, 36.23347978, 11.99802249, 56.0757278 , 24.787105  ,\n",
+       "       25.46432005, 17.43177821, 48.13118474, 17.73030037, 52.12107973,\n",
+       "       46.31794975, 47.84592412, 15.88054761, 14.29344032, 50.63009809,\n",
+       "       35.66071429, 18.84592327, 33.32846765, 27.4659636 , 35.30112789,\n",
+       "       23.50307858, 48.83047326, 18.54429376, 23.62786395, 17.34873392,\n",
+       "       47.91072769, 18.91310687, 33.49788453, 25.95866971, 55.42050117,\n",
+       "       45.71253412, 53.00665474, 32.34399503, 55.80478725, 38.61271429,\n",
+       "       24.77196988, 29.83074963, 55.97363163, 49.12288889, 43.55875603,\n",
+       "       57.35694411, 44.11836986, 37.45242251, 10.14697351, 14.6042797 ,\n",
+       "       20.72584936, 46.61124664, 56.33893664, 58.18131563, 51.56187297,\n",
+       "       59.78497026, 18.80154984, 56.93701555, 39.5818871 , 55.46005753,\n",
+       "       40.83552678, 37.72271811, 25.75227398, 47.32329769, 30.57556396,\n",
+       "       18.28524185, 52.48085298, 41.85761247, 34.7278295 , 45.61253032,\n",
+       "       19.4902492 , 51.98111479, 42.27914391, 35.5609033 , 52.771877  ,\n",
+       "       20.96507401, 31.39816752, 10.68096921, 20.30096763, 50.3306528 ,\n",
+       "       13.77376308, 12.43780016, 15.55433308, 17.90095498, 54.60802039,\n",
+       "       49.50072685, 39.80122488, 24.77324745, 13.19605793, 49.97878193,\n",
+       "       49.6825784 , 48.11605129, 35.18812243, 51.95676536, 58.52496469])"
+      ]
+     },
+     "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": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CAIWq+w7Ku971rjz66KPZtWvX2HHBBRfkqquuyq5du/L6178+tVot/f39Yx9z+PDhbN26NYsWLar3OADAJFT3HZSWlpYsWLBg3Lnp06dn5syZY+d7enrS29ubrq6udHV1pbe3N9OmTcuyZcvqPQ4AMAlNyItkT2TVqlU5dOhQVq5cmf3792fhwoXZsmVLWlpaGjEOAFCYpqqqqkYPcbJGRkbS1taW4eHhtLa2NnocAOBlOJmf3/4WDwBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAceoeKOvWrctb3/rWtLS0ZPbs2Xn/+9+fxx57bNw1VVVl7dq16ezszNSpU7NkyZLs3r273qMAAJNU3QNl69atueaaa/Lv//7v6e/vz09+8pN0d3fn2WefHbtmw4YN2bhxY/r6+rJ9+/bUarUsXbo0Bw4cqPc4AMAk1FRVVTWRn+AHP/hBZs+ena1bt+Yd73hHqqpKZ2dnenp6snr16iTJ6Oho2tvbs379+qxYseKEjzkyMpK2trYMDw+ntbV1IscHAOrkZH5+T/hrUIaHh5MkM2bMSJIMDAxkcHAw3d3dY9c0Nzdn8eLF2bZt24s+xujoaEZGRsYdAMDpa0IDpaqqXHvttXn729+eBQsWJEkGBweTJO3t7eOubW9vH7vvhdatW5e2traxY86cORM5NgDQYBMaKB/72Mfy7W9/O3//939/3H1NTU3jbldVddy5Y9asWZPh4eGxY9++fRMyLwBQhikT9cAf//jH85WvfCX3339/Xve6142dr9VqSZ7fSeno6Bg7PzQ0dNyuyjHNzc1pbm6eqFEBgMLUfQelqqp87GMfy913351//ud/zvz588fdP3/+/NRqtfT394+dO3z4cLZu3ZpFixbVexwAYBKq+w7KNddck7vuuiv/8A//kJaWlrHXlbS1tWXq1KlpampKT09Pent709XVla6urvT29mbatGlZtmxZvccBACahugfK7bffniRZsmTJuPOf//zn86EPfShJsmrVqhw6dCgrV67M/v37s3DhwmzZsiUtLS31HgcAmIQm/H1QJoL3QQGAyaeo90EBADhZAgUAKI5AAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKI5AAQCKI1AAgOIIFACgOFMaPUCJzrn+a40e4aR9/1PvbfQIAFA3dlAAgOIIFACgOJ7ioWE8lQbAT2MHBQAojkABAIrjKR6gOJ7+A+ygAADFESgAQHEECgBQHK9BAXgF83qfU8M6nzw7KABAcQQKAFAcT/GcJibj9uFkNBnXudHbtK8Uk/F7Y7Ky1q8MdlAAgOIIFACgOJ7igdOc7XBgMrKDAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxREoAEBxBAoAUByBAgAUR6AAAMURKABAcQQKAFAcgQIAFEegAADFESgAQHEECgBQHIECABRHoAAAxWlooNx2222ZP39+Xv3qV+f888/PAw880MhxAIBCNCxQvvzlL6enpyc33HBDHnnkkfzGb/xG3vOe92Tv3r2NGgkAKETDAmXjxo358Ic/nD/6oz/Km970pmzatClz5szJ7bff3qiRAIBCTGnEJz18+HB27NiR66+/ftz57u7ubNu27bjrR0dHMzo6OnZ7eHg4STIyMjIh8x0dPTghjwsAk8VE/Iw99phVVZ3w2oYEyg9/+MMcOXIk7e3t4863t7dncHDwuOvXrVuXm2+++bjzc+bMmbAZAeCVrG3TxD32gQMH0tbW9pLXNCRQjmlqahp3u6qq484lyZo1a3LttdeO3T569Gh+9KMfZebMmS96/ckaGRnJnDlzsm/fvrS2tv7cj8dPZ61PHWt96ljrU8danzoTsdZVVeXAgQPp7Ow84bUNCZRZs2bljDPOOG63ZGho6LhdlSRpbm5Oc3PzuHOvec1r6j5Xa2urb/hTxFqfOtb61LHWp461PnXqvdYn2jk5piEvkj3rrLNy/vnnp7+/f9z5/v7+LFq0qBEjAQAFadhTPNdee22uvvrqXHDBBfn1X//1bN68OXv37s1HP/rRRo0EABSiYYFyxRVX5Omnn84tt9ySp556KgsWLMh9992XefPmnfJZmpubc9NNNx33NBL1Z61PHWt96ljrU8danzqNXuum6uX8rg8AwCnkb/EAAMURKABAcQQKAFAcgQIAFOcVEyj3339/Lr744nR2dqapqSn33nvvuPurqsratWvT2dmZqVOnZsmSJdm9e3djhp3kTrTWd999d9797ndn1qxZaWpqyq5duxoy5+nipdb7ueeey+rVq3Puuedm+vTp6ezszB/8wR/kySefbNzAk9iJvrfXrl2bN77xjZk+fXrOPvvsXHTRRXnooYcaM+wkd6K1/v9WrFiRpqambNq06ZTNdzo50Vp/6EMfSlNT07jjbW9724TP9YoJlGeffTbnnXde+vr6XvT+DRs2ZOPGjenr68v27dtTq9WydOnSHDhw4BRPOvmdaK2fffbZXHjhhfnUpz51iic7Pb3Ueh88eDA7d+7MjTfemJ07d+buu+/O448/nksuuaQBk05+J/refsMb3pC+vr48+uijefDBB3POOeeku7s7P/jBD07xpJPfidb6mHvvvTcPPfTQy3rrdF7cy1nr3/qt38pTTz01dtx3330TP1j1CpSkuueee8ZuHz16tKrVatWnPvWpsXM//vGPq7a2tuqzn/1sAyY8fbxwrf+/gYGBKkn1yCOPnNKZTmcvtd7HPPzww1WSas+ePadmqNPUy1nr4eHhKkn1jW9849QMdZr6aWv9xBNPVL/wC79Qfec736nmzZtXffrTnz7ls51uXmytly9fXr3vfe875bO8YnZQXsrAwEAGBwfT3d09dq65uTmLFy/Otm3bGjgZ1N/w8HCampom5O9Z8X8OHz6czZs3p62tLeedd16jxzntHD16NFdffXWuu+66vPnNb270OKe9f/mXf8ns2bPzhje8IR/5yEcyNDQ04Z+zoX/NuBTH/mjhC/9QYXt7e/bs2dOIkWBC/PjHP87111+fZcuW+UNrE+SrX/1qrrzyyhw8eDAdHR3p7+/PrFmzGj3WaWf9+vWZMmVKPvGJTzR6lNPee97znlx22WWZN29eBgYGcuONN+Y3f/M3s2PHjgl9l1mB8v80NTWNu11V1XHnYLJ67rnncuWVV+bo0aO57bbbGj3Oaeud73xndu3alR/+8Ie54447cvnll+ehhx7K7NmzGz3aaWPHjh35m7/5m+zcudP/0afAFVdcMfbvBQsW5IILLsi8efPyta99LZdeeumEfV5P8SSp1WpJ/m8n5ZihoaHjdlVgMnruuedy+eWXZ2BgIP39/XZPJtD06dPzi7/4i3nb296Wz33uc5kyZUo+97nPNXqs08oDDzyQoaGhzJ07N1OmTMmUKVOyZ8+efPKTn8w555zT6PFOex0dHZk3b16++93vTujnEShJ5s+fn1qtlv7+/rFzhw8fztatW7No0aIGTgY/v2Nx8t3vfjff+MY3MnPmzEaP9IpSVVVGR0cbPcZp5eqrr863v/3t7Nq1a+zo7OzMddddl69//euNHu+09/TTT2ffvn3p6OiY0M/zinmK55lnnsn3vve9sdsDAwPZtWtXZsyYkblz56anpye9vb3p6upKV1dXent7M23atCxbtqyBU09OJ1rrH/3oR9m7d+/Ye3E89thjSZ7fyTq2m8XL91Lr3dnZmQ9+8IPZuXNnvvrVr+bIkSNjO4UzZszIWWed1aixJ6WXWuuZM2fm1ltvzSWXXJKOjo48/fTTue222/LEE0/ksssua+DUk9OJ/h95YWifeeaZqdVq+aVf+qVTPeqk91JrPWPGjKxduza/+7u/m46Ojnz/+9/Pn/3Zn2XWrFn5wAc+MLGDnfLfG2qQb37zm1WS447ly5dXVfX8rxrfdNNNVa1Wq5qbm6t3vOMd1aOPPtrYoSepE6315z//+Re9/6abbmro3JPVS633sV/lfrHjm9/8ZqNHn3Reaq0PHTpUfeADH6g6Ozurs846q+ro6KguueSS6uGHH2702JPSif4feSG/Zvyze6m1PnjwYNXd3V299rWvrc4888xq7ty51fLly6u9e/dO+FxNVVVVE1Y/AAA/A69BAQCKI1AAgOIIFACgOAIFACiOQAEAiiNQAIDiCBQAoDgCBQAojkABAIojUACA4ggUAKA4AgUAKM7/AjooTpojTBjHAAAAAElFTkSuQmCC",
+      "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": [
-    "# your code here\n"
+    "import matplotlib.pyplot as plt\n",
+    "plt.hist(func1,bins=10)\n",
+    "plt.ylim((0,110))\n",
+    "plt.show()\n",
+    "plt.hist(func2,bins=10)\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -89,11 +398,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'with uniform distribution, you will always have natural fluctuations, but more or less both plots are flat.'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here:\n"
+    "# your answer here:\n",
+    "\"\"\"with uniform distribution, you will always have natural fluctuations, but more or less both plots are flat.\"\"\""
    ]
   },
   {
@@ -114,11 +435,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "# 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": 10,
+   "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": [
+    "func1=random_num_norm(avg=10,std=1,count=1000)\n",
+    "func2=random_num_norm(avg=10,std=50,count=1000)\n",
+    "\n",
+    "\n",
+    "plt.hist(func1)\n",
+    "plt.show()\n",
+    "plt.hist(func2)\n",
+    "plt.ylim((0,250))\n",
+    "plt.show()"
    ]
   },
   {
@@ -158,11 +520,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "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"
+    "# 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 +551,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "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"
+    "# your code here\n",
+    "vehicles[\"CO2 Emission Grams/Mile\"].hist()\n",
+    "plt.show()"
    ]
   },
   {
@@ -190,11 +580,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zNGfOnOtuc7lcqq6uDhkrLS3Vgw8+qDNnzmjUqFHy+/3aunWrtm/frpkzZ0qSysvLlZKSor179yorK0snTpxQZWWlDh48qEmTJkmStmzZoszMTJ08eVKpqamqqqrSBx98oLNnz8rj8UiSXnjhBT3yyCNat26dhg4dGu6lAQAAw4QddMLl9/vlcDh01113SZLq6uoUDAbl9XrtGo/Ho7S0NNXW1iorK0sHDhyQy+WyQ44kTZ48WS6XS7W1tUpNTdWBAweUlpZmhxxJysrKUiAQUF1dnR5++OEucwkEAgoEAvbjlpYWSZ885RYMBiN96VHTOfe+fA13whljRfd8A6yQr93RX/+suqO/39/RRr+ji353Tzj96tGg86c//UlPPvmkcnNz7RWWxsZGxcfHa9iwYSG1ycnJamxstGuSkpK6HC8pKSmkJjk5OWT7sGHDFB8fb9dcq7i4WGvXru0yXlVVpYSEhPAv8FPm2tW0/qLkwd457zMTO7q97549eyI4k/6hv97fvYV+Rxf9Ds+VK1duu7bHgk4wGNRXvvIVdXR06Ic//OEt6y3LksPhsB//5e/vpOYvrVmzRvn5+fbjlpYWpaSkyOv19umnuoLBoKqrqzVr1izFxcX19nSiLq3wraiezznA0jMTO/T00QEKdFz/XruVY4VZEZ6Vufr7/R1t9Du66Hf3dD4jczt6JOgEg0EtXLhQp06d0ttvvx0SItxut9ra2tTc3ByyqtPU1KQpU6bYNefPn+9y3AsXLtirOG63W4cOHQrZ3tzcrGAw2GWlp5PT6ZTT6ewyHhcXZ8QNZsp1hCvQ3r2wccfn7XB0+9z98c/pTvXX+7u30O/oot/hCadXEf8cnc6Q85vf/EZ79+7V8OHDQ7ZnZGQoLi4uZJmuoaFBx44ds4NOZmam/H6/Dh8+bNccOnRIfr8/pObYsWNqaGiwa6qqquR0OpWRkRHpywIAAH1Q2Cs6ra2t+uijj+zHp06dUn19vRITE+XxePRP//RPeu+99/TGG2+ovb3dfr1MYmKi4uPj5XK5tHjxYq1evVrDhw9XYmKiCgoKlJ6ebr8La+zYsZo9e7aWLFmizZs3S5KWLl2q7OxspaamSpK8Xq/GjRsnn8+n9evX6+OPP1ZBQYGWLFnSp5+GAgAAkRN20Dl69GjIO5o6X/OyaNEiFRYW6vXXX5ckff7znw/Z75133tH06dMlSRs3blRsbKwWLlyoq1evasaMGdq2bZtiYmLs+h07dmjVqlX2u7NycnJCPrsnJiZGb775ppYvX66pU6dq0KBBys3N1fPPPx/uJQEAAEOFHXSmT58uy7rx22pvtq3TwIEDVVpaqtLS0hvWJCYmqry8/KbHGTVqlN54441bng8AAPRP/KwrAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADBW2EFn//79mjdvnjwejxwOh1577bWQ7ZZlqbCwUB6PR4MGDdL06dN1/PjxkJpAIKCVK1dqxIgRGjx4sHJycnTu3LmQmubmZvl8PrlcLrlcLvl8Pl28eDGk5syZM5o3b54GDx6sESNGaNWqVWprawv3kgAAgKHCDjqXL1/W+PHjVVZWdt3tJSUl2rBhg8rKynTkyBG53W7NmjVLly5dsmvy8vK0e/duVVRUqKamRq2trcrOzlZ7e7tdk5ubq/r6elVWVqqyslL19fXy+Xz29vb2ds2dO1eXL19WTU2NKioqtGvXLq1evTrcSwIAAIaKDXeHOXPmaM6cOdfdZlmWNm3apKeeekoLFiyQJL366qtKTk7Wzp07tWzZMvn9fm3dulXbt2/XzJkzJUnl5eVKSUnR3r17lZWVpRMnTqiyslIHDx7UpEmTJElbtmxRZmamTp48qdTUVFVVVemDDz7Q2bNn5fF4JEkvvPCCHnnkEa1bt05Dhw7tVkMAAIA5wg46N3Pq1Ck1NjbK6/XaY06nU9OmTVNtba2WLVumuro6BYPBkBqPx6O0tDTV1tYqKytLBw4ckMvlskOOJE2ePFkul0u1tbVKTU3VgQMHlJaWZoccScrKylIgEFBdXZ0efvjhLvMLBAIKBAL245aWFklSMBhUMBiMZCuiqnPuffka7oQzxoru+QZYIV+7o7/+WXVHf7+/o41+Rxf97p5w+hXRoNPY2ChJSk5ODhlPTk7W6dOn7Zr4+HgNGzasS03n/o2NjUpKSupy/KSkpJCaa88zbNgwxcfH2zXXKi4u1tq1a7uMV1VVKSEh4XYu8VOturq6t6fQK0oe7J3zPjOxo9v77tmzJ4Iz6R/66/3dW+h3dNHv8Fy5cuW2ayMadDo5HI6Qx5ZldRm71rU116vvTs1fWrNmjfLz8+3HLS0tSklJkdfr7dNPdQWDQVVXV2vWrFmKi4vr7elEXVrhW1E9n3OApWcmdujpowMU6Lj5fX0jxwqzIjwrc/X3+zva6Hd00e/u6XxG5nZENOi43W5Jn6y2jBw50h5vamqyV1/cbrfa2trU3NwcsqrT1NSkKVOm2DXnz5/vcvwLFy6EHOfQoUMh25ubmxUMBrus9HRyOp1yOp1dxuPi4oy4wUy5jnAF2rsXNu74vB2Obp+7P/453an+en/3FvodXfQ7POH0KqKfozN69Gi53e6QJbi2tjbt27fPDjEZGRmKi4sLqWloaNCxY8fsmszMTPn9fh0+fNiuOXTokPx+f0jNsWPH1NDQYNdUVVXJ6XQqIyMjkpcFAAD6qLBXdFpbW/XRRx/Zj0+dOqX6+nolJiZq1KhRysvLU1FRkcaMGaMxY8aoqKhICQkJys3NlSS5XC4tXrxYq1ev1vDhw5WYmKiCggKlp6fb78IaO3asZs+erSVLlmjz5s2SpKVLlyo7O1upqamSJK/Xq3Hjxsnn82n9+vX6+OOPVVBQoCVLlvTpp6EAAEDkhB10jh49GvKOps7XvCxatEjbtm3T448/rqtXr2r58uVqbm7WpEmTVFVVpSFDhtj7bNy4UbGxsVq4cKGuXr2qGTNmaNu2bYqJibFrduzYoVWrVtnvzsrJyQn57J6YmBi9+eabWr58uaZOnapBgwYpNzdXzz//fPhdAAAARgo76EyfPl2WdeO31TocDhUWFqqwsPCGNQMHDlRpaalKS0tvWJOYmKjy8vKbzmXUqFF64403bjlnAADQP/GzrgAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYKzY3p4APl0+8+SbvT0FAAAihhUdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxIh50/vznP+s//uM/NHr0aA0aNEj33nuvvve976mjo8OusSxLhYWF8ng8GjRokKZPn67jx4+HHCcQCGjlypUaMWKEBg8erJycHJ07dy6kprm5WT6fTy6XSy6XSz6fTxcvXoz0JQEAgD4q4kHnueee00svvaSysjKdOHFCJSUlWr9+vUpLS+2akpISbdiwQWVlZTpy5IjcbrdmzZqlS5cu2TV5eXnavXu3KioqVFNTo9bWVmVnZ6u9vd2uyc3NVX19vSorK1VZWan6+nr5fL5IXxIAAOijIv7JyAcOHNCXvvQlzZ07V5L0mc98Rj/+8Y919OhRSZ+s5mzatElPPfWUFixYIEl69dVXlZycrJ07d2rZsmXy+/3aunWrtm/frpkzZ0qSysvLlZKSor179yorK0snTpxQZWWlDh48qEmTJkmStmzZoszMTJ08eVKpqamRvjQAANDHRDzoPPTQQ3rppZf04Ycf6rOf/ax+9atfqaamRps2bZIknTp1So2NjfJ6vfY+TqdT06ZNU21trZYtW6a6ujoFg8GQGo/Ho7S0NNXW1iorK0sHDhyQy+WyQ44kTZ48WS6XS7W1tdcNOoFAQIFAwH7c0tIiSQoGgwoGg5FuRdR0zj0S1+CMse74GKZzDrBCvnZHX77foi2S9zdujX5HF/3unnD6FfGg88QTT8jv9+tzn/ucYmJi1N7ernXr1ulf/uVfJEmNjY2SpOTk5JD9kpOTdfr0absmPj5ew4YN61LTuX9jY6OSkpK6nD8pKcmuuVZxcbHWrl3bZbyqqkoJCQlhXumnT3V19R0fo+TBCEykn3hmYseti25gz549EZxJ/xCJ+xu3j35HF/0Oz5UrV267NuJB5yc/+YnKy8u1c+dO3Xfffaqvr1deXp48Ho8WLVpk1zkcjpD9LMvqMnata2uuV3+z46xZs0b5+fn245aWFqWkpMjr9Wro0KG3dX2fRsFgUNXV1Zo1a5bi4uLu6FhphW9FaFbmcg6w9MzEDj19dIACHTe/Z01yrDCrV84byfsbt0a/o4t+d0/nMzK3I+JB51vf+paefPJJfeUrX5Ekpaen6/Tp0youLtaiRYvkdrslfbIiM3LkSHu/pqYme5XH7Xarra1Nzc3NIas6TU1NmjJlil1z/vz5Lue/cOFCl9WiTk6nU06ns8t4XFycETdYJK4j0N5//uG+U4EOR7/qV29/j5jyfdpX0O/oot/hCadXEX/X1ZUrVzRgQOhhY2Ji7LeXjx49Wm63O2SZrq2tTfv27bNDTEZGhuLi4kJqGhoadOzYMbsmMzNTfr9fhw8ftmsOHTokv99v1wAAgP4t4is68+bN07p16zRq1Cjdd999+uUvf6kNGzbo0UcflfTJ0015eXkqKirSmDFjNGbMGBUVFSkhIUG5ubmSJJfLpcWLF2v16tUaPny4EhMTVVBQoPT0dPtdWGPHjtXs2bO1ZMkSbd68WZK0dOlSZWdn844rAAAgqQeCTmlpqZ5++mktX75cTU1N8ng8WrZsmb7zne/YNY8//riuXr2q5cuXq7m5WZMmTVJVVZWGDBli12zcuFGxsbFauHChrl69qhkzZmjbtm2KiYmxa3bs2KFVq1bZ787KyclRWVlZpC8JAAD0UREPOkOGDNGmTZvst5Nfj8PhUGFhoQoLC29YM3DgQJWWloZ80OC1EhMTVV5efgezBQAAJuNnXQEAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsXok6Pz+97/Xv/7rv2r48OFKSEjQ5z//edXV1dnbLctSYWGhPB6PBg0apOnTp+v48eMhxwgEAlq5cqVGjBihwYMHKycnR+fOnQupaW5uls/nk8vlksvlks/n08WLF3vikgAAQB8U8aDT3NysqVOnKi4uTr/4xS/0wQcf6IUXXtBdd91l15SUlGjDhg0qKyvTkSNH5Ha7NWvWLF26dMmuycvL0+7du1VRUaGamhq1trYqOztb7e3tdk1ubq7q6+tVWVmpyspK1dfXy+fzRfqSAABAHxUb6QM+99xzSklJ0SuvvGKPfeYzn7F/b1mWNm3apKeeekoLFiyQJL366qtKTk7Wzp07tWzZMvn9fm3dulXbt2/XzJkzJUnl5eVKSUnR3r17lZWVpRMnTqiyslIHDx7UpEmTJElbtmxRZmamTp48qdTU1EhfGgAA6GMiHnRef/11ZWVl6Z//+Z+1b98+/c3f/I2WL1+uJUuWSJJOnTqlxsZGeb1eex+n06lp06aptrZWy5YtU11dnYLBYEiNx+NRWlqaamtrlZWVpQMHDsjlctkhR5ImT54sl8ul2tra6wadQCCgQCBgP25paZEkBYNBBYPBSLciajrnHolrcMZYd3wM0zkHWCFf+4ve+h6J5P2NW6Pf0UW/uyecfkU86Pz2t7/Viy++qPz8fH3729/W4cOHtWrVKjmdTn3ta19TY2OjJCk5OTlkv+TkZJ0+fVqS1NjYqPj4eA0bNqxLTef+jY2NSkpK6nL+pKQku+ZaxcXFWrt2bZfxqqoqJSQkhH+xnzLV1dV3fIySByMwkX7imYkdvT2FqNqzZ0+vnj8S9zduH/2OLvodnitXrtx2bcSDTkdHhyZOnKiioiJJ0oQJE3T8+HG9+OKL+trXvmbXORyOkP0sy+oydq1ra65Xf7PjrFmzRvn5+fbjlpYWpaSkyOv1aujQobe+uE+pYDCo6upqzZo1S3FxcXd0rLTCtyI0K3M5B1h6ZmKHnj46QIGOm9+zJjlWmNUr543k/Y1bo9/RRb+7p/MZmdsR8aAzcuRIjRs3LmRs7Nix2rVrlyTJ7XZL+mRFZuTIkXZNU1OTvcrjdrvV1tam5ubmkFWdpqYmTZkyxa45f/58l/NfuHChy2pRJ6fTKafT2WU8Li7OiBssEtcRaO8//3DfqUCHo1/1q7e/R0z5Pu0r6Hd00e/whNOriL/raurUqTp58mTI2Icffqh77rlHkjR69Gi53e6QZbq2tjbt27fPDjEZGRmKi4sLqWloaNCxY8fsmszMTPn9fh0+fNiuOXTokPx+v10DAAD6t4iv6Hzzm9/UlClTVFRUpIULF+rw4cN6+eWX9fLLL0v65OmmvLw8FRUVacyYMRozZoyKioqUkJCg3NxcSZLL5dLixYu1evVqDR8+XImJiSooKFB6err9LqyxY8dq9uzZWrJkiTZv3ixJWrp0qbKzs3nHFQAAkNQDQeeBBx7Q7t27tWbNGn3ve9/T6NGjtWnTJn31q1+1ax5//HFdvXpVy5cvV3NzsyZNmqSqqioNGTLErtm4caNiY2O1cOFCXb16VTNmzNC2bdsUExNj1+zYsUOrVq2y352Vk5OjsrKySF8SAADooyIedCQpOztb2dnZN9zucDhUWFiowsLCG9YMHDhQpaWlKi0tvWFNYmKiysvL72SqAADAYPysKwAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwVo8HneLiYjkcDuXl5dljlmWpsLBQHo9HgwYN0vTp03X8+PGQ/QKBgFauXKkRI0Zo8ODBysnJ0blz50Jqmpub5fP55HK55HK55PP5dPHixZ6+JAAA0Ef0aNA5cuSIXn75Zd1///0h4yUlJdqwYYPKysp05MgRud1uzZo1S5cuXbJr8vLytHv3blVUVKimpkatra3Kzs5We3u7XZObm6v6+npVVlaqsrJS9fX18vl8PXlJAACgD+mxoNPa2qqvfvWr2rJli4YNG2aPW5alTZs26amnntKCBQuUlpamV199VVeuXNHOnTslSX6/X1u3btULL7ygmTNnasKECSovL9f777+vvXv3SpJOnDihyspK/ehHP1JmZqYyMzO1ZcsWvfHGGzp58mRPXRYAAOhDeizorFixQnPnztXMmTNDxk+dOqXGxkZ5vV57zOl0atq0aaqtrZUk1dXVKRgMhtR4PB6lpaXZNQcOHJDL5dKkSZPsmsmTJ8vlctk1AACgf4vtiYNWVFTovffe05EjR7psa2xslCQlJyeHjCcnJ+v06dN2TXx8fMhKUGdN5/6NjY1KSkrqcvykpCS75lqBQECBQMB+3NLSIkkKBoMKBoO3e3mfOp1zj8Q1OGOsOz6G6ZwDrJCv/UVvfY9E8v7GrdHv6KLf3RNOvyIedM6ePatvfOMbqqqq0sCBA29Y53A4Qh5bltVl7FrX1lyv/mbHKS4u1tq1a7uMV1VVKSEh4abn7guqq6vv+BglD0ZgIv3EMxM7ensKUbVnz55ePX8k7m/cPvodXfQ7PFeuXLnt2ogHnbq6OjU1NSkjI8Mea29v1/79+1VWVma/fqaxsVEjR460a5qamuxVHrfbrba2NjU3N4es6jQ1NWnKlCl2zfnz57uc/8KFC11WizqtWbNG+fn59uOWlhalpKTI6/Vq6NChd3DVvSsYDKq6ulqzZs1SXFzcHR0rrfCtCM3KXM4Blp6Z2KGnjw5QoOPm4dwkxwqzeuW8kby/cWv0O7rod/d0PiNzOyIedGbMmKH3338/ZOzrX/+6Pve5z+mJJ57QvffeK7fbrerqak2YMEGS1NbWpn379um5556TJGVkZCguLk7V1dVauHChJKmhoUHHjh1TSUmJJCkzM1N+v1+HDx/Wgw9+sgxx6NAh+f1+Owxdy+l0yul0dhmPi4sz4gaLxHUE2vvPP9x3KtDh6Ff96u3vEVO+T/sK+h1d9Ds84fQq4kFnyJAhSktLCxkbPHiwhg8fbo/n5eWpqKhIY8aM0ZgxY1RUVKSEhATl5uZKklwulxYvXqzVq1dr+PDhSkxMVEFBgdLT0+0XN48dO1azZ8/WkiVLtHnzZknS0qVLlZ2drdTU1EhfFgAA6IN65MXIt/L444/r6tWrWr58uZqbmzVp0iRVVVVpyJAhds3GjRsVGxurhQsX6urVq5oxY4a2bdummJgYu2bHjh1atWqV/e6snJwclZWVRf16AADAp1NUgs67774b8tjhcKiwsFCFhYU33GfgwIEqLS1VaWnpDWsSExNVXl4eoVkCAADT8LOuAACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYK7a3JwCgb/jMk2/2ynmdMZZKHpTSCt9SoN0R1r6/e3ZuD80KQF/Big4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGNFPOgUFxfrgQce0JAhQ5SUlKT58+fr5MmTITWWZamwsFAej0eDBg3S9OnTdfz48ZCaQCCglStXasSIERo8eLBycnJ07ty5kJrm5mb5fD65XC65XC75fD5dvHgx0pcEAAD6qIgHnX379mnFihU6ePCgqqur9ec//1ler1eXL1+2a0pKSrRhwwaVlZXpyJEjcrvdmjVrli5dumTX5OXlaffu3aqoqFBNTY1aW1uVnZ2t9vZ2uyY3N1f19fWqrKxUZWWl6uvr5fP5In1JAACgj4r45+hUVlaGPH7llVeUlJSkuro6ffGLX5RlWdq0aZOeeuopLViwQJL06quvKjk5WTt37tSyZcvk9/u1detWbd++XTNnzpQklZeXKyUlRXv37lVWVpZOnDihyspKHTx4UJMmTZIkbdmyRZmZmTp58qRSU1MjfWkAAKCP6fEPDPT7/ZKkxMRESdKpU6fU2Ngor9dr1zidTk2bNk21tbVatmyZ6urqFAwGQ2o8Ho/S0tJUW1urrKwsHThwQC6Xyw45kjR58mS5XC7V1tZeN+gEAgEFAgH7cUtLiyQpGAwqGAxG9sKjqHPukbgGZ4x1x8cwnXOAFfIVPetO+t2Xv697SyT/PsGt0e/uCadfPRp0LMtSfn6+HnroIaWlpUmSGhsbJUnJyckhtcnJyTp9+rRdEx8fr2HDhnWp6dy/sbFRSUlJXc6ZlJRk11yruLhYa9eu7TJeVVWlhISEMK/u06e6uvqOj1HyYAQm0k88M7Gjt6fQr3Sn33v27OmBmfQPkfj7BLePfofnypUrt13bo0Hnscce069//WvV1NR02eZwhH6Uu2VZXcaudW3N9epvdpw1a9YoPz/fftzS0qKUlBR5vV4NHTr0puf+NAsGg6qurtasWbMUFxd3R8dKK3wrQrMyl3OApWcmdujpowMU6AjvRxIgfHfS72OFWT00K3NF8u8T3Br97p7OZ2RuR48FnZUrV+r111/X/v37dffdd9vjbrdb0icrMiNHjrTHm5qa7FUet9uttrY2NTc3h6zqNDU1acqUKXbN+fPnu5z3woULXVaLOjmdTjmdzi7jcXFxRtxgkbiOcH+WUH8W6HDQryjqTr9N+L7uLab8vdhX0O/whNOriL/ryrIsPfbYY/rpT3+qt99+W6NHjw7ZPnr0aLnd7pBlura2Nu3bt88OMRkZGYqLiwupaWho0LFjx+yazMxM+f1+HT582K45dOiQ/H6/XQMAAPq3iK/orFixQjt37tTPfvYzDRkyxH69jMvl0qBBg+RwOJSXl6eioiKNGTNGY8aMUVFRkRISEpSbm2vXLl68WKtXr9bw4cOVmJiogoICpaen2+/CGjt2rGbPnq0lS5Zo8+bNkqSlS5cqOzubd1wBAABJPRB0XnzxRUnS9OnTQ8ZfeeUVPfLII5Kkxx9/XFevXtXy5cvV3NysSZMmqaqqSkOGDLHrN27cqNjYWC1cuFBXr17VjBkztG3bNsXExNg1O3bs0KpVq+x3Z+Xk5KisrCzSlwQAAPqoiAcdy7r1W0AdDocKCwtVWFh4w5qBAweqtLRUpaWlN6xJTExUeXl5d6YJAAD6AX7WFQAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYKzY3p6AyT7z5JtROY8zxlLJg1Ja4VsKtDuick6gL4jW92Ak/e7Zub09BcAorOgAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGP1+U9G/uEPf6j169eroaFB9913nzZt2qQvfOELvT0tAOiW3v405+580jqf5oxPsz69ovOTn/xEeXl5euqpp/TLX/5SX/jCFzRnzhydOXOmt6cGAAA+Bfp00NmwYYMWL16sf/u3f9PYsWO1adMmpaSk6MUXX+ztqQEAgE+BPvvUVVtbm+rq6vTkk0+GjHu9XtXW1l53n0AgoEAgYD/2+/2SpI8//ljBYDDic4z98+WIH/O65+mwdOVKh2KDA9TewQ/17Gn0O7rod3R1p99/V/DfPTyryDu0ZkZvT0GSFAwGdeXKFf3xj39UXFxcb0+nz7h06ZIkybKsW9b22aDzf//3f2pvb1dycnLIeHJyshobG6+7T3FxsdauXdtlfPTo0T0yx2jK7e0J9DP0O7rod3T1h36PeKG3Z4BIuHTpklwu101r+mzQ6eRwhP6Pw7KsLmOd1qxZo/z8fPtxR0eHPv74Yw0fPvyG+/QFLS0tSklJ0dmzZzV06NDeno7x6Hd00e/oot/RRb+7x7IsXbp0SR6P55a1fTbojBgxQjExMV1Wb5qamrqs8nRyOp1yOp0hY3fddVdPTTHqhg4dyjdKFNHv6KLf0UW/o4t+h+9WKzmd+uyLkePj45WRkaHq6uqQ8erqak2ZMqWXZgUAAD5N+uyKjiTl5+fL5/Np4sSJyszM1Msvv6wzZ87o3//933t7agAA4FOgTwedL3/5y/rjH/+o733ve2poaFBaWpr27Nmje+65p7enFlVOp1Pf/e53uzwth55Bv6OLfkcX/Y4u+t3zHNbtvDcLAACgD+qzr9EBAAC4FYIOAAAwFkEHAAAYi6ADAACMRdDpQ/bv36958+bJ4/HI4XDotddeC9luWZYKCwvl8Xg0aNAgTZ8+XcePH++dyfZxxcXFeuCBBzRkyBAlJSVp/vz5OnnyZEgN/Y6cF198Uffff7/9oWmZmZn6xS9+YW+n1z2ruLhYDodDeXl59hg9j6zCwkI5HI6QX263295Ov3sOQacPuXz5ssaPH6+ysrLrbi8pKdGGDRtUVlamI0eOyO12a9asWfYPP8Pt27dvn1asWKGDBw+qurpaf/7zn+X1enX58v//Qa30O3LuvvtuPfvsszp69KiOHj2qv//7v9eXvvQl+y96et1zjhw5opdffln3339/yDg9j7z77rtPDQ0N9q/333/f3ka/e5CFPkmStXv3bvtxR0eH5Xa7rWeffdYe+9Of/mS5XC7rpZde6oUZmqWpqcmSZO3bt8+yLPodDcOGDbN+9KMf0esedOnSJWvMmDFWdXW1NW3aNOsb3/iGZVnc3z3hu9/9rjV+/PjrbqPfPYsVHUOcOnVKjY2N8nq99pjT6dS0adNUW1vbizMzg9/vlyQlJiZKot89qb29XRUVFbp8+bIyMzPpdQ9asWKF5s6dq5kzZ4aM0/Oe8Zvf/EYej0ejR4/WV77yFf32t7+VRL97Wp/+ZGT8f50/3PTaH2ianJys06dP98aUjGFZlvLz8/XQQw8pLS1NEv3uCe+//74yMzP1pz/9SX/1V3+l3bt3a9y4cfZf9PQ6sioqKvTee+/pyJEjXbZxf0fepEmT9F//9V/67Gc/q/Pnz+v73/++pkyZouPHj9PvHkbQMYzD4Qh5bFlWlzGE57HHHtOvf/1r1dTUdNlGvyMnNTVV9fX1unjxonbt2qVFixZp37599nZ6HTlnz57VN77xDVVVVWngwIE3rKPnkTNnzhz79+np6crMzNTf/u3f6tVXX9XkyZMl0e+ewlNXhuh89X7n/ww6NTU1dflfAm7fypUr9frrr+udd97R3XffbY/T78iLj4/X3/3d32nixIkqLi7W+PHj9YMf/IBe94C6ujo1NTUpIyNDsbGxio2N1b59+/Sf//mfio2NtftKz3vO4MGDlZ6ert/85jfc4z2MoGOI0aNHy+12q7q62h5ra2vTvn37NGXKlF6cWd9kWZYee+wx/fSnP9Xbb7+t0aNHh2yn3z3PsiwFAgF63QNmzJih999/X/X19faviRMn6qtf/arq6+t177330vMeFggEdOLECY0cOZJ7vIfx1FUf0traqo8++sh+fOrUKdXX1ysxMVGjRo1SXl6eioqKNGbMGI0ZM0ZFRUVKSEhQbm5uL866b1qxYoV27typn/3sZxoyZIj9Py2Xy6VBgwbZnzlCvyPj29/+tubMmaOUlBRdunRJFRUVevfdd1VZWUmve8CQIUPs15t1Gjx4sIYPH26P0/PIKigo0Lx58zRq1Cg1NTXp+9//vlpaWrRo0SLu8Z7We2/4QrjeeecdS1KXX4sWLbIs65O3KH73u9+13G635XQ6rS9+8YvW+++/37uT7qOu12dJ1iuvvGLX0O/IefTRR6177rnHio+Pt/76r//amjFjhlVVVWVvp9c97y/fXm5Z9DzSvvzlL1sjR4604uLiLI/HYy1YsMA6fvy4vZ1+9xyHZVlWL2UsAACAHsVrdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAw1v8DvRtdwYly01EAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "vehicles[\"Combined MPG\"].hist()\n",
+    "plt.show()"
    ]
   },
   {
@@ -210,7 +613,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# you answer here:\n"
+    "# you answer here:\n",
+    "\"\"\"#Looks like all of them are. Combined MPG is the closest though.\"\"\""
    ]
   },
   {
@@ -237,11 +641,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "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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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "#your code here:\n",
+    "def random_num_exp(mean,count):\n",
+    "    return np.random.exponential(mean,size=count)\n",
+    "\n",
+    "func1=random_num_exp(mean=1,count=1000)\n",
+    "func2=random_num_exp(mean=100,count=1000)\n",
+    "\n",
+    "\n",
+    "plt.hist(func1,bins=100)\n",
+    "plt.show()\n",
+    "plt.hist(func2,bins=100)\n",
+    "plt.show()"
    ]
   },
   {
@@ -273,12 +709,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7768698398515702"
+      ]
+     },
+     "execution_count": 15,
+     "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)"
    ]
   },
   {
@@ -290,17 +742,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2231301601484298"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here\n"
+    "# your answer here\n",
+    "1-expon_dist.cdf(15)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -314,7 +785,7 @@
    "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..9769e01 100644
--- a/your-code/discrete.ipynb
+++ b/your-code/discrete.ipynb
@@ -33,9 +33,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the probability that the fruit is an apple is 0.6 and the probability of that the fruit is and oragne is 0.4\n"
+     ]
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "Calculate:\n",
@@ -43,7 +51,11 @@
     "q = probability that the fruit is an orange\n",
     "\"\"\"\n",
     "\n",
-    "# your code here\n"
+    "# your code here\n",
+    "p = 60/100\n",
+    "q = 40/100\n",
+    "\n",
+    "print(f\"the probability that the fruit is an apple is {p} and the probability of that the fruit is and oragne is {q}\")\n"
    ]
   },
   {
@@ -61,11 +73,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.07775999999999998\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "8.349416423424006e-08"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "P1 = p ** 5\n",
+    "print(P1)\n",
+    "P2 = p ** 5 * q **15\n",
+    "P2\n"
    ]
   },
   {
@@ -83,11 +117,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0012944935222876583"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "from scipy.stats import binom\n",
+    "import numpy as np\n",
+    "\n",
+    "binom.pmf(5, 20, 0.6)"
    ]
   },
   {
@@ -101,11 +150,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00031703112116863004"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "\n",
+    "binomial_dist = binom.cdf(4, 20, 0.6)\n",
+    "binomial_dist"
    ]
   },
   {
@@ -119,12 +182,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "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"
+    "# Please label the axes and give a title to the plot\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "X = np.arange(0,20)\n",
+    "\n",
+    "plt.plot(X, binomial_dist.pmf(X), \"o\")\n",
+    "\n",
+    "plt.title('Binomial Distribution PDF')\n",
+    "plt.xlabel('Number of Apples')\n",
+    "plt.show()"
    ]
   },
   {
@@ -151,11 +234,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of scoring 5 goals: 0.0537750255819468\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "\n",
+    "import math\n",
+    "math.factorial(n) # n is the number you want to find the factorial of\n",
+    "average_goals = 2.3\n",
+    "goals = 5\n",
+    "\n",
+    "# Calculate the probability using the Poisson PMF formula\n",
+    "probability_5_goals = (math.exp(-average_goals) * average_goals**goals) / math.factorial(goals)\n",
+    "\n",
+    "# Print the result\n",
+    "print(\"Probability of scoring 5 goals:\", probability_5_goals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.053775025581946814\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.stats import poisson\n",
+    "mu = 2.3\n",
+    "poisson_dist = poisson(mu)\n",
+    "\n",
+    "print(poisson_dist.pmf(5))"
    ]
   },
   {
@@ -167,18 +290,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "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"
+    "# Please label the axes and give a title to the plot \n",
+    "X = np.arange(0, 10)\n",
+    "plt.plot(X, poisson_dist.pmf(X), \"o\")\n",
+    "plt.title('Poisson Distribution')\n",
+    "plt.ylabel('Probability')\n",
+    "plt.xlabel('Number of Tries')\n",
+    "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -192,7 +339,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,