From 0cced9b080a02372da0f02f98b08db1f2685d5d1 Mon Sep 17 00:00:00 2001 From: Carlos Silva Date: Tue, 8 Aug 2023 22:30:14 +0100 Subject: [PATCH] /Users/barbaragdias/Desktop/Week 5/lab-probability-distributions --- your-code/continuous.ipynb | 351 ++++++++++++++++++++++++++++++++++--- your-code/discrete.ipynb | 185 ++++++++++++++++--- 2 files changed, 483 insertions(+), 53 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..f1df06f 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -35,9 +35,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.97726326 2.92587894 2.44820814 2.59887369 2.02680464 2.38459236\n", + " 2.53359772 2.17217559 2.7228578 2.49643141]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", @@ -47,6 +56,16 @@ "print(randoms)" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -73,11 +92,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_uniform_random(bottom, ceiling, count):\n", + " x = np.random.rand(count)\n", + " randoms = bottom + (ceiling - bottom) * x\n", + " return randoms" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "bottom1 = 10\n", + "ceiling1 = 15\n", + "count1 = 100\n", + "\n", + "bottom2 = 10\n", + "ceiling2 = 60\n", + "count2 = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "randoms1 = generate_uniform_random(bottom1, ceiling1, count1)\n", + "randoms2 = generate_uniform_random(bottom2, ceiling2, count2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "\n", + "plt.subplot(2, 1, 1)\n", + "plt.hist(randoms1, bins=10, color='blue', alpha=0.7)\n", + "plt.title('Uniform Distribution\\n(bottom=10, ceiling=15, count=100)')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.hist(randoms2, bins=10, color='blue', alpha=0.7)\n", + "plt.title('Uniform Distribution\\n(bottom=10, ceiling=60, count=1000)')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -93,7 +175,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "#the key difference lies in the range and the count of the generated random numbers, which affects how evenly the values are spread out and how smooth the distribution appears in the histograms." ] }, { @@ -114,11 +196,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_normal_random(mean, std_dev, count):\n", + " randoms = np.random.normal(mean, std_dev, count)\n", + " return randoms" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "mean1 = 10\n", + "std_dev1 = 1\n", + "count1 = 1000\n", + "\n", + "mean2 = 10\n", + "std_dev2 = 50\n", + "count2 = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "randoms1 = generate_normal_random(mean1, std_dev1, count1)\n", + "randoms2 = generate_normal_random(mean2, std_dev2, count2)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "\n", + "plt.subplot(2, 1, 1)\n", + "plt.hist(randoms1, bins=30, color='blue', alpha=0.7)\n", + "plt.title('Normal Distribution\\n(mean=10, std_dev=1, count=1000)')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.hist(randoms2, bins=30, color='green', alpha=0.7)\n", + "plt.title('Normal Distribution\\n(mean=10, std_dev=50, count=1000)')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -134,7 +278,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "#the standard deviation controls the spread or variability of data points in a normal distribution. A larger standard deviation leads to a wider spread of values, while a smaller standard deviation results in a narrower distribution around the mean.\n" ] }, { @@ -158,11 +302,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "import pandas as pd\n", + "df = pd.read_csv('vehicles.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.hist(df['Fuel Barrels/Year'], bins=20, color='blue', edgecolor='black')\n", + "plt.title('Histogram of Fuel Barrels/Year')\n", + "plt.xlabel('Fuel Barrels/Year')\n", + "plt.ylabel('Frequency')\n", + "plt.grid(True)\n", + "plt.show()" ] }, { @@ -174,11 +345,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.figure(figsize=(10, 6))\n", + "plt.hist(df['CO2 Emission Grams/Mile'], bins=20, color='green', edgecolor='black')\n", + "plt.title('Histogram of CO2 Emission Grams/Mile')\n", + "plt.xlabel('CO2 Emission Grams/Mile')\n", + "plt.ylabel('Frequency')\n", + "plt.grid(True)\n", + "plt.show()" ] }, { @@ -190,11 +378,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.figure(figsize=(10, 6))\n", + "plt.hist(df['Combined MPG'], bins=20, color='orange', edgecolor='black')\n", + "plt.title('Histogram of Combined MPG')\n", + "plt.xlabel('Combined MPG')\n", + "plt.ylabel('Frequency')\n", + "plt.grid(True)\n", + "plt.show()" ] }, { @@ -210,7 +415,7 @@ "metadata": {}, "outputs": [], "source": [ - "# you answer here:\n" + "#Combined MPG. because the columns are with similar values" ] }, { @@ -237,11 +442,65 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "def generate_exponential(mean, size):\n", + " return np.random.exponential(scale=mean, size=size)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "mean_1 = 1\n", + "mean_100 = 100\n", + "size = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "exp_dist_1 = generate_exponential(mean_1, size)\n", + "exp_dist_100 = generate_exponential(mean_100, size)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(exp_dist_1, bins=100, color='blue', edgecolor='black')\n", + "plt.title(f'Exponential Distribution (Mean = {mean_1})')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Frequency')\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(exp_dist_100, bins=100, color='green', edgecolor='black')\n", + "plt.title(f'Exponential Distribution (Mean = {mean_100})')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Frequency')\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -257,7 +516,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "#The distribution with a larger mean (100) will have a slower decay and a more prominent central peak compared to the distribution with a smaller mean (1)\n" ] }, { @@ -273,12 +532,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability that a customer will spend less than 15 minutes in the bank: 0.7769\n" + ] + } + ], "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", + "def exponential_cdf(x, mean):\n", + " \n", + " rate = 1 / mean\n", + " \n", + " \n", + " cdf = 1 - np.exp(-rate * x)\n", + " return cdf\n", + "\n", + "mean = 10 \n", + "x = 15 \n", + "\n", + "probability = exponential_cdf(x, mean)\n", + "print(f\"The probability that a customer will spend less than {x} minutes in the bank: {probability:.4f}\")" ] }, { @@ -290,17 +570,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability that a customer will spend more than 15 minutes in the bank: 0.2231\n" + ] + } + ], "source": [ - "# your answer here\n" + "def probability_more_than(x, mean):\n", + " return 1 - exponential_cdf(x, mean)\n", + "\n", + "mean = 10 \n", + "x = 15 \n", + "\n", + "probability = probability_more_than(x, mean)\n", + "print(f\"The probability that a customer will spend more than {x} minutes in the bank: {probability:.4f}\")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -314,7 +609,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb index 310a3c6..043215b 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -33,9 +33,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that the fruit is an apple (p): 0.60\n", + "Probability that the fruit is an orange (q): 0.40\n" + ] + } + ], "source": [ "\"\"\"\n", "Calculate:\n", @@ -43,7 +52,15 @@ "q = probability that the fruit is an orange\n", "\"\"\"\n", "\n", - "# your code here\n" + "total_fruits = 100\n", + "apples = 60\n", + "oranges = 40\n", + "\n", + "p = apples / total_fruits\n", + "q = oranges / total_fruits\n", + "\n", + "print(f\"Probability that the fruit is an apple (p): {p:.2f}\")\n", + "print(f\"Probability that the fruit is an orange (q): {q:.2f}\")\n" ] }, { @@ -61,11 +78,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that the first 5 fruits are all apples: 0.0778\n", + "Probability that the first 5 fruits are all apples and the next 15 fruits are all oranges: 0.0000\n" + ] + } + ], "source": [ - "# your code here\n" + "p = 0.6 \n", + "q = 0.4 \n", + "\n", + "\n", + "prob_5_apples = p**5\n", + "\n", + "\n", + "prob_5_apples_15_oranges = prob_5_apples * q**15\n", + "\n", + "print(f\"Probability that the first 5 fruits are all apples: {prob_5_apples:.4f}\")\n", + "print(f\"Probability that the first 5 fruits are all apples and the next 15 fruits are all oranges: {prob_5_apples_15_oranges:.4f}\")" ] }, { @@ -83,11 +119,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of getting 5 apples in a sample of 20 fruits: 0.0013\n" + ] + } + ], "source": [ - "# your code here\n" + "from scipy.stats import binom\n", + "\n", + "n = 20 \n", + "k = 5 \n", + "p = 0.6 \n", + "\n", + "probability = binom.pmf(k, n, p)\n", + "\n", + "print(f\"Probability of getting {k} apples in a sample of {n} fruits: {probability:.4f}\")" ] }, { @@ -101,11 +153,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of picking less than 5 apples in a sample of 20 fruits: 0.0003\n" + ] + } + ], "source": [ - "# your code here\n" + "n = 20 \n", + "k = 5 \n", + "p = 0.6 \n", + "\n", + "probability = binom.cdf(k - 1, n, p)\n", + "\n", + "print(f\"Probability of picking less than {k} apples in a sample of {n} fruits: {probability:.4f}\")" ] }, { @@ -119,12 +185,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n", - "# Please label the axes and give a title to the plot\n" + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "n = 20 \n", + "p = 0.6 \n", + "x_values = np.arange(0, n + 1)\n", + "pdf_values = binom.pmf(x_values, n, p)\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(x_values, pdf_values, color='blue', edgecolor='black', align='center')\n", + "plt.title('Binomial Distribution PDF')\n", + "plt.xlabel('Number of Apples')\n", + "plt.ylabel('Probability')\n", + "plt.xticks(x_values)\n", + "plt.grid(True)\n", + "plt.show()" ] }, { @@ -151,11 +240,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability of scoring 5 goals in a match: 0.0538\n" + ] + } + ], "source": [ - "# your code here\n" + "import math\n", + "math.factorial(n)\n", + "lambda_value = 2.3\n", + "k = 5\n", + "probability = (math.exp(-lambda_value) * lambda_value**k) / math.factorial(k)\n", + "\n", + "print(f\"The probability of scoring {k} goals in a match: {probability:.4f}\")" ] }, { @@ -167,18 +270,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n", - "# Please label the axes and give a title to the plot \n" + "from scipy.stats import poisson\n", + "\n", + "lambda_value = 2.3\n", + "\n", + "x_values = np.arange(0, 11)\n", + "\n", + "pmf_values = poisson.pmf(x_values, lambda_value)\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(x_values, pmf_values, color='blue', edgecolor='black', align='center')\n", + "plt.title('Poisson Distribution of Goals')\n", + "plt.xlabel('Number of Goals')\n", + "plt.ylabel('Probability')\n", + "plt.xticks(x_values)\n", + "plt.grid(True)\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 +327,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4,