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Added PTest and CLT

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Central_Limit_Theorem.ipynb 0 → 100644
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" Volume in drive C is Windows\n",
" Volume Serial Number is 9804-D0F8\n",
"\n",
" Directory of C:\\Users\\12058\n",
"\n",
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"06/28/2022 09:42 AM <DIR> ..\n",
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"06/08/2022 10:09 AM 248,337 01-Python_Packages.ipynb\n",
"06/08/2022 09:17 AM 98,945 04--Gauss_Jordan_pre-class-assignment.ipynb\n",
"06/08/2022 10:53 AM 39,849 04-Gauss_Jordan_in-class-assignment.ipynb\n",
"06/10/2022 09:59 AM 32,829 05--Gauss_Jordan2_pre-class-assignment.ipynb\n",
"06/12/2022 07:06 PM 9,262 05-Gauss_Jordan2_in-class-assignment.ipynb\n",
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"!dir\n",
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"('answercheck.py', <http.client.HTTPMessage at 0x20990c87880>)"
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"source": [
"##ANSWER##\n",
"#Install answercheck in current director\n",
"from urllib.request import urlretrieve\n",
"urlretrieve('https://raw.githubusercontent.com/colbrydi/jupytercheck/master/answercheck.py', filename='answercheck.py')\n",
"##ANSWER##"
]
},
{
"cell_type": "markdown",
"id": "b7598d9e",
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"source": [
"# Central Limit Theroem \n",
"Understanding and computing using the Central Limit Theorem (CLT)"
]
},
{
"cell_type": "markdown",
"id": "74fcd9bf",
"metadata": {},
"source": [
"# Description \n",
"The Central Limit Theorem states that the distribution of sample means approximates a normal distribution as the sample size gets larger, regardless of the population's distribution. "
]
},
{
"cell_type": "markdown",
"id": "2238469c",
"metadata": {},
"source": [
"# Training Materials \n",
"https://www.khanacademy.org/math/ap-statistics/sampling-distribution-ap/what-is-sampling-distribution/v/central-limit-theorem\n",
"\n",
"https://www.youtube.com/watch?v=4YLtvNeRIrg"
]
},
{
"cell_type": "markdown",
"id": "bcc501fa",
"metadata": {},
"source": [
"# Self Assessment "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aff596ae",
"metadata": {},
"outputs": [],
"source": []
}
],
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