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week5 lab3 done #228

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week5 lab3 done #228

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186 changes: 159 additions & 27 deletions lab-intro-probability.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -38,11 +38,27 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"0.8844772466215431"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#code here"
"from scipy.stats import binom, norm\n",
"n = 460\n",
"p = 0.97\n",
"binom_ = binom(n, p)\n",
"proba = binom_.cdf(450)\n",
"\n"
]
},
{
Expand Down Expand Up @@ -72,11 +88,29 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 24,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"0.49"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#code here"
"from scipy.stats import geom\n",
"\n",
"p = 0.3\n",
"geom_p = geom(p)\n",
"proba = 1 - (geom_p.cdf(2))\n",
"proba\n",
"\n",
"\n"
]
},
{
Expand Down Expand Up @@ -107,11 +141,28 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 30,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"0.01289822084039205"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#code here"
"from scipy.stats import poisson\n",
"#p(x > 550)\n",
"\n",
"m = 500\n",
"poiss = poisson(m)\n",
"proba = 1 - poiss.cdf(550)\n",
"proba"
]
},
{
Expand All @@ -123,11 +174,27 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 38,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#code here"
"from scipy.stats import expon\n",
"\n",
"lambda_ = 500 * 24\n",
"poiss = poisson(lambda_)\n",
"proba_result = 1 - poiss.cdf(550)\n",
"proba_result"
]
},
{
Expand Down Expand Up @@ -157,10 +224,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"0.3934693402873666"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lambdda = 0.1\n",
"la = expon(scale= 1/lambdda)\n",
"result = la.cdf(5)\n",
"result"
]
},
{
"cell_type": "markdown",
Expand All @@ -173,10 +256,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"0.7768698398515702"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lambdda = 0.1\n",
"la = expon(scale= 1/lambdda)\n",
"result = la.cdf(15)\n",
"result"
]
},
{
"cell_type": "markdown",
Expand All @@ -196,11 +295,27 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 46,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"0.6826894921370859"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#code here"
"mean = 150\n",
"std = 10\n",
"\n",
"norm_dist = norm(loc=mean,scale=std)\n",
"proba = norm_dist.cdf(160) - norm_dist.cdf(140)\n",
"proba"
]
},
{
Expand All @@ -219,17 +334,34 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 50,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"0.4511883639059735"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#code here"
"mean_during = 50\n",
"panne = 30\n",
"\n",
"lambda2 = 1 / mean_during\n",
"lambda2_inv = expon(scale= 1/lambda2)\n",
"lambda2_inv.cdf(30)\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -243,9 +375,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}