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Solved Lab Benjamin Mancera #212

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4947243
Solved Lab
Benja022 Nov 30, 2024
440a7ea
Refactoring challenge 2
Benja022 Nov 30, 2024
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220 changes: 194 additions & 26 deletions lab-intro-probability.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -38,11 +38,31 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"La probabilidad de que falten 10 pasajeros o más es: 0.1853\n"
]
}
],
"source": [
"#code here"
"import scipy.stats as stats\n",
"\n",
"# Número de boletos vendidos\n",
"n = 460\n",
"# Probabilidad de que un pasajero no se presente\n",
"p = 0.03\n",
"# Número de pasajeros que se presentan\n",
"k = 450\n",
"\n",
"# Calculamos la probabilidad de que 10 o más pasajeros no se presenten\n",
"prob = 1 - stats.binom.cdf(k - 1, n, 1 - p)\n",
"\n",
"print(f\"La probabilidad de que falten 10 pasajeros o más es: {prob:.4f}\")"
]
},
{
Expand Down Expand Up @@ -72,11 +92,32 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 17,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that the representative needs at least three attempts is: 0.4900\n",
"The probability that the representative needs exactly three attempts is: 0.1470\n"
]
}
],
"source": [
"#code here"
"# Probabilidad de éxito en cada intento\n",
"p = 0.3\n",
"# Número de intentos\n",
"k = 3\n",
"\n",
"# Calculamos la probabilidad de que se necesiten al menos 3 intentos (fallar en los primeros 2 intentos)\n",
"prob_at_least_3_attempts = (1 - p) ** (k - 1)\n",
"\n",
"# Calculamos la probabilidad de que se necesiten exactamente 3 intentos (fallar en los primeros 2 intentos y luego tener éxito)\n",
"prob_exactly_3_attempts = (1 - p) ** (k - 1) * p\n",
"\n",
"print(f\"The probability that the representative needs at least three attempts is: {prob_at_least_3_attempts:.4f}\")\n",
"print(f\"The probability that the representative needs exactly three attempts is: {prob_exactly_3_attempts:.4f}\")"
]
},
{
Expand Down Expand Up @@ -107,11 +148,29 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability of the website server being overwhelmed is: 0.0129\n"
]
}
],
"source": [
"#code here"
"import scipy.stats as stats\n",
"\n",
"# Tasa promedio de visitas por hora\n",
"lambda_ = 500\n",
"# Capacidad máxima del servidor\n",
"max_visits = 550\n",
"\n",
"# Calculamos la probabilidad de que el número de visitas sea mayor que 550\n",
"prob = 1 - stats.poisson.cdf(max_visits, lambda_)\n",
"\n",
"print(f\"The probability of the website server being overwhelmed is: {prob:.4f}\")"
]
},
{
Expand All @@ -123,11 +182,38 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability of the website server being overwhelmed at some point during a day is: 0.2677\n"
]
}
],
"source": [
"#code here"
"import scipy.stats as stats\n",
"\n",
"# Tasa promedio de visitas por hora\n",
"lambda_ = 500\n",
"# Capacidad máxima del servidor\n",
"max_visits = 550\n",
"\n",
"# Calculamos la probabilidad de que el número de visitas sea mayor que 550 en una hora\n",
"prob_overwhelmed_per_hour = 1 - stats.poisson.cdf(max_visits, lambda_)\n",
"\n",
"# Calculamos la probabilidad de que el servidor no se vea abrumado en una hora\n",
"prob_not_overwhelmed_per_hour = 1 - prob_overwhelmed_per_hour\n",
"\n",
"# Calculamos la probabilidad de que el servidor no se vea abrumado en ninguna de las 24 horas\n",
"prob_not_overwhelmed_per_day = prob_not_overwhelmed_per_hour ** 24\n",
"\n",
"# Calculamos la probabilidad de que el servidor se vea abrumado al menos una vez en 24 horas\n",
"prob_overwhelmed_per_day = 1 - prob_not_overwhelmed_per_day\n",
"\n",
"print(f\"The probability of the website server being overwhelmed at some point during a day is: {prob_overwhelmed_per_day:.4f}\")"
]
},
{
Expand Down Expand Up @@ -157,10 +243,30 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that the next customer will arrive within the next 5 minutes is: 0.3935\n"
]
}
],
"source": [
"import scipy.stats as stats\n",
"\n",
"# Tasa promedio de llegadas (por minuto)\n",
"lambda_ = 1 / 10\n",
"# Tiempo en minutos\n",
"t = 5\n",
"\n",
"# Calculamos la probabilidad de que el próximo cliente llegue dentro de los próximos 5 minutos\n",
"prob = stats.expon.cdf(t, scale=1/lambda_)\n",
"\n",
"print(f\"The probability that the next customer will arrive within the next 5 minutes is: {prob:.4f}\")"
]
},
{
"cell_type": "markdown",
Expand All @@ -173,10 +279,33 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that an employee can take a break is: 0.2231\n"
]
}
],
"source": [
"import scipy.stats as stats\n",
"\n",
"# Tasa promedio de llegadas (por minuto)\n",
"lambda_ = 1 / 10\n",
"# Tiempo en minutos\n",
"t = 15\n",
"\n",
"# Calculamos la probabilidad de que no haya clientes durante 15 minutos\n",
"prob = stats.expon.cdf(t, scale=1/lambda_)\n",
"\n",
"# La probabilidad de que no haya clientes durante 15 minutos es 1 menos la probabilidad acumulada\n",
"prob_no_customers = 1 - prob\n",
"\n",
"print(f\"The probability that an employee can take a break is: {prob_no_customers:.4f}\")"
]
},
{
"cell_type": "markdown",
Expand All @@ -196,11 +325,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that the bird's weight is between 140 and 160 grams is: 0.6827\n"
]
}
],
"source": [
"#code here"
"import scipy.stats as stats\n",
"\n",
"# Parámetros de la distribución normal\n",
"mu = 150\n",
"sigma = 10\n",
"\n",
"# Pesos entre los que queremos calcular la probabilidad\n",
"lower_bound = 140\n",
"upper_bound = 160\n",
"\n",
"# Calculamos la probabilidad de que el peso esté entre 140 y 160 gramos\n",
"prob = stats.norm.cdf(upper_bound, mu, sigma) - stats.norm.cdf(lower_bound, mu, sigma)\n",
"\n",
"print(f\"The probability that the bird's weight is between 140 and 160 grams is: {prob:.4f}\")"
]
},
{
Expand All @@ -219,17 +369,35 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability that the component fails within the first 30 hours is: 0.4512\n"
]
}
],
"source": [
"#code here"
"import scipy.stats as stats\n",
"\n",
"# Tasa promedio de fallos (por hora)\n",
"lambda_ = 1 / 50\n",
"# Tiempo en horas\n",
"t = 30\n",
"\n",
"# Calculamos la probabilidad de que el componente falle dentro de las primeras 30 horas\n",
"prob = stats.expon.cdf(t, scale=1/lambda_)\n",
"\n",
"print(f\"The probability that the component fails within the first 30 hours is: {prob:.4f}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -243,7 +411,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.12.4"
}
},
"nbformat": 4,
Expand Down