diff --git a/notebook/problems.ipynb b/notebook/problems.ipynb index d725f0e9..c8c1dceb 100644 --- a/notebook/problems.ipynb +++ b/notebook/problems.ipynb @@ -1,163 +1 @@ -{ -"cells": [ -{ -"cell_type": "markdown", -"id": "b967ebc8", -"metadata": {}, -"source": [ -"# Probability Distribution Problems" -] -}, -{ -"cell_type": "markdown", -"id": "c9b71587", -"metadata": {}, -"source": [ -"**Binomial Distribution**\n", -"\n", -"In probability theory and statistics, the binomial distribution is the discrete probability distribution that gives only two possible results in an experiment, for example, when you flip a coin it can by either Head or Tails." -] -}, -{ -"cell_type": "code", -"execution_count": 2, -"id": "4eb16d6e", -"metadata": {}, -"outputs": [], -"source": [ -"# Probability functions, density and distribution\n", -"\n", -"def dbinom(x,size,prob=0.5):\n", -" \"\"\"\n", -" Calculates the point estimate of the binomial distribution\n", -" \"\"\"\n", -" from scipy.stats import binom\n", -" result=binom.pmf(k=x,n=size,p=prob,loc=0)\n", -" return result\n", -"\n", -"def pbinom(q,size,prob=0.5):\n", -" \"\"\"\n", -" Calculates the cumulative of the binomial distribution\n", -" \"\"\"\n", -" from scipy.stats import binom\n", -" result=binom.cdf(k=q,n=size,p=prob,loc=0)\n", -" return result\n", -"\n", -"def qbinom(p, size, prob=0.5):\n", -" \"\"\"\n", -" Calculates the quantile function from the binomial distribution\n", -" \"\"\"\n", -" from scipy.stats import binom\n", -" result=binom.ppf(q=p,n=size,p=prob,loc=0)\n", -" return result\n", -"\n", -"def rbinom(n,size,prob=0.5):\n", -" \"\"\"\n", -" Generates random variables from the binomial distribution\n", -" \"\"\"\n", -" from scipy.stats import binom\n", -" result=binom.rvs(n=size,p=prob,size=n)\n", -" return result" -] -}, -{ -"cell_type": "code", -"execution_count": 3, -"id": "6cf6313a", -"metadata": {}, -"outputs": [ -{ -"data": { -"text/plain": [ -"array([1, 1, 1, ..., 1, 4, 2])" -] -}, -"execution_count": 3, -"metadata": {}, -"output_type": "execute_result" -} -], -"source": [ -"dbinom(2, size = 10, prob = 0.2) #Probability that a binomial (10,0.2) takes the value 2,\n", -"pbinom(2, size = 10, prob = 0.2) #Probability that a binomial (10,0.2) will take a value less than 2\n", -"qbinom(0.9, size = 10, prob = 0.2) # what value of a binomial (10,0.2) has a cumulative probability of 0.9?\n", -"rbinom(2000, size = 10, prob = 0.2) # generate 2000 random values from a binomial distribution (10,0.2)" -] -}, -{ -"cell_type": "code", -"execution_count": 6, -"id": "6a6fb6ed", -"metadata": {}, -"outputs": [ -{ -"data": { -"image/png": "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", -"text/plain": [ -"
" -] -}, -"metadata": { -"needs_background": "light" -}, -"output_type": "display_data" -} -], -"source": [ -"import numpy as np\n", -"import matplotlib.pyplot as plt\n", -"\n", -"# Discrete distributions\n", -"xs = np.arange( 1, 11 )\n", -"ys = dbinom(xs,size=10,prob=0.5) # compute the shape of the distribution\n", -"plt.plot( xs, ys, 'o' ) # plot circles...\n", -"plt.vlines( xs, 0, ys ) # ...and lines\n", -"plt.ylim( bottom=0 ) # ensure sensible bottom border\n", -"plt.title('Probability function binomial distribution')\n", -"plt.show()" -] -}, -{ -"cell_type": "markdown", -"id": "8aa7eaa0", -"metadata": {}, -"source": [ -"Exercise 1: Create the same functions but for the normal and uniform distribution." -] -}, -{ -"cell_type": "code", -"execution_count": 1, -"id": "98d5f012", -"metadata": {}, -"outputs": [], -"source": [ -"#Code here" -] -} -], -"metadata": { -"interpreter": { -"hash": "0f2f6c680cf484319d387fabac80ca4ff4fc33965036e02100b3fe02600f1423" -}, -"kernelspec": { -"display_name": "Python 3.10.1 64-bit", -"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": "3.10.1" -} -}, -"nbformat": 4, -"nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"b967ebc8","metadata":{},"source":["# Probability Distribution Problems"]},{"cell_type":"markdown","id":"c9b71587","metadata":{},"source":["**Binomial Distribution**\n","\n","In probability theory and statistics, the binomial distribution is the discrete probability distribution that gives only two possible results in an experiment, for example, when you flip a coin it can by either Head or Tails."]},{"cell_type":"code","execution_count":1,"id":"4eb16d6e","metadata":{},"outputs":[],"source":["# Probability functions, density and distribution\n","\n","def dbinom(x,size,prob=0.5):\n"," \"\"\"\n"," Calculates the point estimate of the binomial distribution\n"," \"\"\"\n"," from scipy.stats import binom\n"," result=binom.pmf(k=x,n=size,p=prob,loc=0)\n"," return result\n","\n","def pbinom(q,size,prob=0.5):\n"," \"\"\"\n"," Calculates the cumulative of the binomial distribution\n"," \"\"\"\n"," from scipy.stats import binom\n"," result=binom.cdf(k=q,n=size,p=prob,loc=0)\n"," return result\n","\n","def qbinom(p, size, prob=0.5):\n"," \"\"\"\n"," Calculates the quantile function from the binomial distribution\n"," \"\"\"\n"," from scipy.stats import binom\n"," result=binom.ppf(q=p,n=size,p=prob,loc=0)\n"," return result\n","\n","def rbinom(n,size,prob=0.5):\n"," \"\"\"\n"," Generates random variables from the binomial distribution\n"," \"\"\"\n"," from scipy.stats import binom\n"," result=binom.rvs(n=size,p=prob,size=n)\n"," return result"]},{"cell_type":"code","execution_count":2,"id":"6cf6313a","metadata":{},"outputs":[{"data":{"text/plain":["array([1, 4, 0, ..., 1, 2, 2])"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["dbinom(2, size = 10, prob = 0.2) #Probability that a binomial (10,0.2) takes the value 2,\n","pbinom(2, size = 10, prob = 0.2) #Probability that a binomial (10,0.2) will take a value less than 2\n","qbinom(0.9, size = 10, prob = 0.2) # what value of a binomial (10,0.2) has a cumulative probability of 0.9?\n","rbinom(2000, size = 10, prob = 0.2) # generate 2000 random values from a binomial distribution (10,0.2)"]},{"cell_type":"code","execution_count":3,"id":"6a6fb6ed","metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","\n","# Discrete distributions\n","xs = np.arange( 1, 11 )\n","ys = dbinom(xs,size=10,prob=0.5) # compute the shape of the distribution\n","plt.plot( xs, ys, 'o' ) # plot circles...\n","plt.vlines( xs, 0, ys ) # ...and lines\n","plt.ylim( bottom=0 ) # ensure sensible bottom border\n","plt.title('Probability function binomial distribution')\n","plt.show()"]},{"cell_type":"markdown","id":"8aa7eaa0","metadata":{},"source":["Exercise 1: Create the same functions but for the normal and uniform distribution."]},{"cell_type":"code","execution_count":4,"id":"98d5f012","metadata":{},"outputs":[],"source":["#Code here\n","def dnorm(x, mean = 0, sd = 1):\n"," \"\"\"\n"," Calculates the probability density function\n"," \"\"\"\n"," from scipy.stats import norm\n"," result = norm.pdf(x, loc = mean, scale = sd)\n"," return result\n","\n","def pnorm(q, mean = 0, sd = 1):\n"," \"\"\"\n"," Calculates the cumulative normal distribution\n"," \"\"\"\n"," from scipy.stats import norm\n"," result = norm.cdf(x = q, loc = mean, scale = sd)\n"," return result\n","\n","def qnorm(p, mean = 0, sd = 1):\n"," \"\"\"\n"," Calculates the quantile function from the binomial distribution\n"," \"\"\"\n"," from scipy.stats import norm\n"," result = norm.ppf(q = p, loc = mean, scale = sd)\n"," return result\n","\n","def rnorm(n = 0, mean = 1, sd = 1):\n"," \"\"\"\n"," Generates random variables from the binomial distribution\n"," \"\"\"\n"," from scipy.stats import norm\n"," result = norm.rvs(loc = n, scale = mean, size = sd)\n"," return result"]},{"cell_type":"code","execution_count":5,"id":"b2e58716","metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["#Import numpy\n","import numpy as np\n","\n","#norm.pdf()\n","x = np.arange(-10,10,0.1)\n","y = dnorm(x, 0, 2)\n","\n","plt.plot(x, y)\n","plt.show()"]},{"cell_type":"code","execution_count":7,"id":"2c783165","metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["#norm.cdf()\n","x = np.arange(-10,10,0.1)\n","y = pnorm(x, 0, 2)\n","\n","plt.plot(x, y)\n","plt.show()"]},{"cell_type":"code","execution_count":9,"id":"3492c3a7","metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["#norm.ppf()\n","x = np.arange(0,1,0.01)\n","y = qnorm(x, 0, 2)\n","\n","plt.plot(x, y)\n","plt.show()"]},{"cell_type":"code","execution_count":11,"id":"56fc4589","metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["#norm.rvs\n","np.random.seed(10)\n","\n","y = rnorm(0, 1, 10000)\n","\n","bin = np.arange(-4, 4, 0.1)\n","\n","plt.hist(y, bins = bin, edgecolor = 'white')\n","plt.show()"]}],"metadata":{"interpreter":{"hash":"0f2f6c680cf484319d387fabac80ca4ff4fc33965036e02100b3fe02600f1423"},"kernelspec":{"display_name":"Python 3.10.1 64-bit","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}