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February 27, 2024 03:05
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "id": "233ce4d6", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "x = [10, 20, 40, 40, 40, 60, 70, 20, 40, 40, 40, 40, 40, 60, 10, 20, 30, 40, 50, 60, 70, 70, 20, 30, 40, 50, 70, 20]\n", | |
| "\n", | |
| "N = 28\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "x = [\n", | |
| " 10, 20, 40, 40, 40, 60, 70,\n", | |
| " 20, 40, 40, 40, 40, 40, 60,\n", | |
| " 10, 20, 30, 40, 50, 60, 70,\n", | |
| " 70, 20, 30, 40, 50, 70, 20,\n", | |
| "]\n", | |
| "print('x =', x)\n", | |
| "print()\n", | |
| "print('N =', len(x))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "44f0a788", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[10, 10, 20, 20, 20, 20, 20, 30, 30, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 50, 50, 60, 60, 60, 70, 70, 70, 70]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "y = sorted(x)\n", | |
| "print(y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "id": "e99dfaa1", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "range = 60\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "rng = y[-1] - y[0]\n", | |
| "print('range =', rng)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "id": "dbe3341d", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "range = 10\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "rng = x[-1] - y[0]\n", | |
| "print('range =', rng)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "id": "55cd20a6", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[10, 20, 40, 40, 40, 60, 70, 20, 40, 40, 40, 40, 40, 60, 10, 20, 30, 40, 50, 60, 70, 70, 20, 30, 40, 50, 70, 20]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "x = [10, 20, 40, 40, 40, 60, 70,\n", | |
| " 20, 40, 40, 40, 40, 40, 60,\n", | |
| " 10, 20, 30, 40, 50, 60, 70,\n", | |
| " 70, 20, 30, 40, 50, 70, 20]\n", | |
| "print(x)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "id": "3995e7d5", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "mid value = 35.0\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "mid = (x[13] + x[14]) / 2\n", | |
| "print('mid value =', mid)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "id": "90e16566", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "40.0\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "import statistics as stat\n", | |
| "print(stat.median(x))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "id": "df5376a9", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[10, 10, 20, 20, 20, 20, 20, 30, 30, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 50, 50, 60, 60, 60, 70, 70, 70, 70]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "y = sorted(x)\n", | |
| "print(y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "id": "0a555a2b", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "median = 40.0\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "med = (y[13] + y[14]) / 2\n", | |
| "print('median =', med)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "id": "d6c1200c", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "40.0\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(stat.median(y))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "id": "535673c5", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "mean = 40.714285714285715\n", | |
| "40.714285714285715\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "sx = sum(x)\n", | |
| "N = len(x)\n", | |
| "mean = sx / N\n", | |
| "print('mean =', mean)\n", | |
| "print(stat.mean(x))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "id": "b1b9666a", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "mean = 40.714285714285715\n", | |
| "40.714285714285715\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "sy = sum(y)\n", | |
| "N = len(y)\n", | |
| "mean = sy / N\n", | |
| "print('mean =', mean)\n", | |
| "print(stat.mean(y))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "id": "3c4cd588", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[10, 10, 20, 20, 20, 20, 20, 30, 30, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 50, 50, 60, 60, 60, 70, 70, 70, 70]\n", | |
| "mode = 40\n", | |
| "40\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "y = sorted (x)\n", | |
| "print(y)\n", | |
| "print('mode =', stat.mode(x))\n", | |
| "print(stat.mode(y))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "id": "30c36e9c", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Exercise 1\n", | |
| "\n", | |
| "# - Explain the difference between median and middle value?\n", | |
| "# Answer :\n", | |
| "# The median is the value that separates the upper half of a dataset from the lower half. \n", | |
| "# It is the value where 50% of the numbers in the set are larger, and 50% are smaller.\n", | |
| "# The middle value is the middle number in a set of numbers when assorted in order \n", | |
| "# (either ascending or descending). The median and the middle value are often the same, especially in \n", | |
| "# sets with an odd number of values. The median is different from the middle value when there's an \n", | |
| "# even number of items in the set. In this case, the median is the average of the two middle values.\n", | |
| "# The median and the middle value are two different ways of measuring the center of a set of data. \n", | |
| "# The median is more robust and less affected by outliers, while the middle value is a quick and easy \n", | |
| "# way to find the center of a set of data.\n", | |
| "\n", | |
| "# - Are mean and mode values always the same for unsorted and sorted dataset? Why?\n", | |
| "# Answer :\n", | |
| "# No, the mean (average) and mode (most frequent value) of a dataset do not necessarily change when \n", | |
| "# the data is sorted or unsorted. Here's why:\n", | |
| "# > Mean\n", | |
| "# -- The mean is the sum of all the values in a dataset divided by the number of values. It represents the \n", | |
| "# \"average\" value.\n", | |
| "# -- Regardless of the order, each data point contributes the same value to the sum used to calculate the \n", | |
| "# mean.\n", | |
| "# -- Therefore, rearranging the data doesn't change the sum or the number of values, and consequently, \n", | |
| "# the mean remains the same.\n", | |
| "# > Mode\n", | |
| "# -- The mode is the most frequent value in a dataset.\n", | |
| "# -- Whether the data is sorted or not, the frequency of each value remains the same.\n", | |
| "# -- Even if the order changes, the number of times each value appears doesn't change, so the \n", | |
| "# most frequent value (mode) remains the same.\n", | |
| "\n", | |
| "# - If range is caculate with last datapoint – first datapoint, should the dataset \n", | |
| "# is sorted first or not? Why?\n", | |
| "# Answer :\n", | |
| "# When calculating the range of a dataset, sorting the data beforehand is not necessary. \n", | |
| "# The range itself focuses purely on the difference between the two most extreme values, \n", | |
| "# regardless of their position within the data. Here's why sorting doesn't affect the range:\n", | |
| "# -- Range: It represents the spread of data, calculated as the difference between the maximum (largest)\n", | |
| "# and minimum (smallest) values.\n", | |
| "# -- Sorting: Arranging the data in ascending or descending order only changes the relative position \n", | |
| "# of the values, not their actual values.\n", | |
| "# Therefore, sorting doesn't affect the range because:\n", | |
| "# -- The calculation only involves the extreme values, not their position.\n", | |
| "# -- Sorting only changes the relative order, not the actual values themselves.\n", | |
| "# So, we can calculate the range accurately without sorting the data first, as it doesn't impact the \n", | |
| "# difference between the highest and lowest values in the set.\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "id": "599a84ae", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Histogram of x\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "plt.hist(x, bins=7, range=[10, 80])\n", | |
| "plt.yticks(range(0, 11,1))\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "id": "38cf19f8", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Histogram of y\n", | |
| "plt.hist(y, bins=7, range=[10, 80])\n", | |
| "plt.yticks(range(0, 11,1))\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 28, | |
| "id": "c4079638", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# 2nd dataset, bins = 7\n", | |
| "x = [ 10, 11, 12, 13, 14,\n", | |
| " 30, 50, 59, 70, 72, 73]\n", | |
| "plt.hist(x, bins=7, range=[10, 80])\n", | |
| "plt.yticks(range(0, 7,1))\n", | |
| "plt.xticks(range(10, 90, 10))\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 29, | |
| "id": "e51facf7", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# 2nd dataset, bins = 4\n", | |
| "x = [ 10, 11, 12, 13, 14,\n", | |
| " 30, 50, 59, 70, 72, 73]\n", | |
| "plt.hist(x, bins=4, range=[10, 80])\n", | |
| "plt.yticks(range(0, 7,1))\n", | |
| "plt.xticks([10, 27, 45, 62])\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 30, | |
| "id": "8dfe8e0e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# 2nd dataset, bins = 2\n", | |
| "x = [ 10, 11, 12, 13, 14,\n", | |
| " 30, 50, 59, 70, 72, 73]\n", | |
| "plt.hist(x, bins=2, range=[10, 80])\n", | |
| "plt.yticks(range(0, 7,1))\n", | |
| "plt.xticks([10, 45])\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 31, | |
| "id": "c0edf6fe", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# 2nd dataset, bins = 19\n", | |
| "x = [ 10, 11, 12, 13, 14,\n", | |
| " 30, 50, 59, 70, 72, 73]\n", | |
| "plt.hist(x, bins=19, range=[10, 80])\n", | |
| "plt.yticks(range(0, 7,1))\n", | |
| "plt.xticks(range(10, 80, 4))\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 43, | |
| "id": "142b5cc6", | |
| "metadata": { | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[25, 70, 16, 22, 33, 65, 27, 24, 67, 37, 52, 73, 49, 71, 47, 43, 35, 62, 70, 36, 73, 61, 40, 47, 54, 26, 32, 65, 36, 44, 40, 70, 27, 42, 41, 22, 11, 48, 43, 11, 73, 37, 52, 16, 66, 27, 40, 78, 76, 29, 66, 58, 76, 19, 45, 66, 36, 74, 13, 18, 52, 47, 30, 27, 34, 16, 24, 57, 53, 10, 74, 51, 26, 47, 44, 72, 15, 76, 12, 44, 19, 72, 60, 32, 12, 36, 29, 32, 43, 75, 28, 36, 49, 30, 56, 63, 48, 25, 66, 19]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "import random as rnd\n", | |
| "x = [\n", | |
| " rnd.randint(10, 80)\n", | |
| " for i in range(0, 100)\n", | |
| "]\n", | |
| "print(x)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 50, | |
| "id": "c5fc920d", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Exercise 2\n", | |
| "# Create histogram for above datasets.\n", | |
| "# Which value for bins should be used? Why?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 53, | |
| "id": "80b4656e", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# bins 12\n", | |
| "plt.hist(x, bins=12, range=[10, 80])\n", | |
| "plt.yticks(range(0, 25, 5))\n", | |
| "plt.xticks(range(10, 90, 10))\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 57, | |
| "id": "b13c8779", | |
| "metadata": { | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# bins 15\n", | |
| "plt.hist(x, bins=15, range=[10, 80])\n", | |
| "plt.yticks(range(0, 25, 5))\n", | |
| "plt.xticks(range(10, 90, 10))\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 58, | |
| "id": "9ea1d085", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# bins 10\n", | |
| "plt.hist(x, bins=10, range=[10, 80])\n", | |
| "plt.yticks(range(0, 25, 5))\n", | |
| "plt.xticks(range(10, 90, 10))\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 59, | |
| "id": "b04c146e", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Which value for bins should be used? Why?\n", | |
| "# 10 bins is a good choice for the given dataset because it allows for a balance between detail and \n", | |
| "# readability.\n", | |
| "# With fewer bins, the data would be overgeneralized and important patterns could be lost. \n", | |
| "# With more bins, the data would be too granular and it would be difficult to see any meaningful trends.\n", | |
| "# Here are some additional things to consider when choosing the number of bins for a histogram:\n", | |
| "# --The number of data points: The more data points you have, the more bins you can use without \n", | |
| "# losing detail.\n", | |
| "# -- The range of the data: The wider the range of the data, the more bins you will need to accurately \n", | |
| "# represent the distribution.\n", | |
| "# -- The skewness of the data: If the data is skewed, you may want to use more bins on the side of \n", | |
| "# the distribution with more data points." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 60, | |
| "id": "dc4df225", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Exercise 3\n", | |
| "# N = 1000; a= 20; b=60;\n", | |
| "# What is n in this case?\n", | |
| "# Answer :\n", | |
| "# In the context of a probability mass function (PMF), \"N\" typically represents the total number of \n", | |
| "# possible outcomes, while \"a\" and \"b\" represent specific values or intervals within the sample space. \n", | |
| "# The value of \"n\" generally refers to the number of possible outcomes within the specified interval \n", | |
| "# or range.\n", | |
| "# Given:\n", | |
| "# N = 1000 (total number of possible outcomes)\n", | |
| "# a = 20 (minimum value or starting point of the interval)\n", | |
| "# b = 60 (maximum value or endpoint of the interval)\n", | |
| "# In this case, \"n\" represents the number of outcomes within the interval [a, b].\n", | |
| "\n", | |
| "# Explain how the average value about 100 can be found from 1/n?\n", | |
| "# Answer :\n", | |
| "# n = b - a + 1 = 60 - 20 = 40 + 1 = 41\n", | |
| "# Average Value = N * 1/n * the width of the bins = 1000 * 1/41 * 4 = 97,5609\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 61, | |
| "id": "7dc10ee4", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "N = 1000; a = 20; b = 60; x = []\n", | |
| "for i in range(N):\n", | |
| " xi = rnd.randint(a,b)\n", | |
| " x.append(xi)\n", | |
| "plt.hist(x, bins=10)\n", | |
| "plt.xticks(range(20, 64,4))\n", | |
| "plt.xlim([20,60])\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 66, | |
| "id": "569cc36b", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Exercise 4\n", | |
| "# Throw a dice 6000 times.\n", | |
| "# What would the histogram look like?\n", | |
| "# What is the value in each bins, if we choose bins = 6?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 67, | |
| "id": "d8f6ed43", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "N = 6000; a=1; b=6; dice = []\n", | |
| "for i in range(N):\n", | |
| " xi = rnd.randint(a, b)\n", | |
| " dice.append(xi)\n", | |
| "plt.hist(dice, bins=6)\n", | |
| "plt.grid()\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 68, | |
| "id": "dd47ad6c", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# if we choose bin = 6, The value is 1000" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 69, | |
| "id": "4b368bb8", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 6 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# Exercise 5\n", | |
| "import numpy as np\n", | |
| "mu = 100\n", | |
| "si = 10\n", | |
| "N = 100000\n", | |
| "values = np.random.normal(mu, si, N)\n", | |
| "\n", | |
| "plt.subplot(3,3,1)\n", | |
| "plt.hist(values, 100)\n", | |
| "plt.axvline(values.mean(), color='r')\n", | |
| "plt.xlim([60,340])\n", | |
| "plt.title(\"mu = 100\")\n", | |
| "\n", | |
| "mu = 200\n", | |
| "si = 10\n", | |
| "N = 100000\n", | |
| "values = np.random.normal(mu, si, N)\n", | |
| "\n", | |
| "plt.subplot(3,3,4)\n", | |
| "plt.hist(values, 100)\n", | |
| "plt.axvline(values.mean(), color='r')\n", | |
| "plt.xlim([60,340])\n", | |
| "plt.title(\"mu = 200\")\n", | |
| "\n", | |
| "mu = 300\n", | |
| "si = 10\n", | |
| "N = 100000\n", | |
| "values = np.random.normal(mu, si, N)\n", | |
| "\n", | |
| "plt.subplot(3,3,7)\n", | |
| "plt.hist(values, 100)\n", | |
| "plt.axvline(values.mean(), color='r')\n", | |
| "plt.xlim([60,340])\n", | |
| "plt.title(\"mu = 300\")\n", | |
| "\n", | |
| "mu = 100\n", | |
| "si = 5\n", | |
| "N = 100000\n", | |
| "values = np.random.normal(mu, si, N)\n", | |
| "\n", | |
| "plt.subplot(3,3,3)\n", | |
| "plt.hist(values, 100)\n", | |
| "plt.axvline(values.mean(), color='r')\n", | |
| "plt.xlim([60,340])\n", | |
| "plt.title(\"si = 5\")\n", | |
| "\n", | |
| "mu = 100\n", | |
| "si = 10\n", | |
| "N = 100000\n", | |
| "values = np.random.normal(mu, si, N)\n", | |
| "\n", | |
| "plt.subplot(3,3,6)\n", | |
| "plt.hist(values, 100)\n", | |
| "plt.axvline(values.mean(), color='r')\n", | |
| "plt.xlim([60,340])\n", | |
| "plt.title(\"si = 10\")\n", | |
| "\n", | |
| "mu = 100\n", | |
| "si = 20\n", | |
| "N = 100000\n", | |
| "values = np.random.normal(mu, si, N)\n", | |
| "\n", | |
| "plt.subplot(3,3,9)\n", | |
| "plt.hist(values, 100)\n", | |
| "plt.axvline(values.mean(), color='r')\n", | |
| "plt.xlim([60,340])\n", | |
| "plt.title(\"si = 20\")\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "id": "382e01bb", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# Mazam Ganendra\n", | |
| "# 21181192" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3 (ipykernel)", | |
| "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": "3.11.4" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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