diff --git a/Central_Limit_Theorem.ipynb b/Central_Limit_Theorem.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..590d918df2553495ed97403602e5692ba7f708ba
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@@ -0,0 +1,175 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "6f3d3c8e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Volume in drive C is Windows\n",
+ " Volume Serial Number is 9804-D0F8\n",
+ "\n",
+ " Directory of C:\\Users\\12058\n",
+ "\n",
+ "06/28/2022 09:42 AM <DIR> .\n",
+ "06/28/2022 09:42 AM <DIR> ..\n",
+ "06/28/2022 07:50 AM <DIR> .ipynb_checkpoints\n",
+ "06/01/2022 09:53 AM <DIR> .ipython\n",
+ "06/02/2022 07:27 PM <DIR> .jupyter\n",
+ "06/02/2022 08:03 PM <DIR> .matplotlib\n",
+ "06/28/2022 09:43 AM <DIR> .ssh\n",
+ "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",
+ "06/13/2022 09:29 AM 27,253 06--Mechanics_pre-class-assignment.ipynb\n",
+ "06/13/2022 09:22 PM 19,582 06-Mechanics_in-class-assignment.ipynb\n",
+ "06/15/2022 09:34 AM 64,736 07--Transformations_pre-class-assignment.ipynb\n",
+ "06/15/2022 10:51 AM 82,657 07-Transformations_in-class-assignment.ipynb\n",
+ "06/20/2022 09:23 AM 15,503 11--Vector_Spaces_pre-class-assignment.ipynb\n",
+ "06/20/2022 10:39 AM 17,851 11-Vector_Spaces_in-class-assignment.ipynb\n",
+ "06/22/2022 10:56 AM 13,714 14-Fundamental_Spaces_in-class-assignment(1).ipynb\n",
+ "04/29/2021 12:44 AM <DIR> 3D Objects\n",
+ "06/01/2022 10:34 AM 2,675 6-1 LA Practice .ipynb\n",
+ "06/01/2022 09:50 AM <DIR> anaconda3\n",
+ "06/28/2022 07:51 AM 6,858 answercheck.py\n",
+ "10/06/2020 08:59 PM <DIR> Apple\n",
+ "06/27/2022 10:33 AM 33,584 banner.png\n",
+ "06/27/2022 10:33 AM 53,166 beaumont.png\n",
+ "06/27/2022 10:33 AM 31,359 billboard.png\n",
+ "08/22/2021 02:05 PM 151 BullseyeCoverageError.txt\n",
+ "06/28/2022 07:57 AM 2,174 Central Limit Theorem .ipynb\n",
+ "04/29/2021 12:44 AM <DIR> Contacts\n",
+ "06/28/2022 09:43 AM <DIR> data_science_bridge_curriculum\n",
+ "01/04/2021 01:01 PM <DIR> Documents\n",
+ "06/28/2022 09:13 AM <DIR> Downloads\n",
+ "06/10/2022 10:50 AM 2,193 Example 6.10 OneNote .ipynb\n",
+ "04/29/2021 12:44 AM <DIR> Favorites\n",
+ "06/17/2022 11:00 PM 69,632 HW1-Systems_of_linear_equations-STUDENT(1).ipynb\n",
+ "06/27/2022 10:38 AM 335,400 HW2-Affine_transform-STUDENT(1).ipynb\n",
+ "06/24/2022 10:36 AM 22,996 Intro_to_Statistics.ipynb\n",
+ "04/29/2021 12:44 AM <DIR> Links\n",
+ "04/29/2021 12:44 AM <DIR> Music\n",
+ "08/01/2021 10:31 AM <DIR> OneDrive\n",
+ "06/02/2022 08:50 PM 76,621 PRACTICE Chapter 2 Vectors-Copy1.ipynb\n",
+ "06/02/2022 08:51 PM 76,621 PRACTICE Chapter 2 Vectors.ipynb\n",
+ "06/13/2022 09:11 AM 9,464 Practice_6_10.ipynb\n",
+ "06/28/2022 07:56 AM 2,103 PTest.ipynb\n",
+ "12/21/2021 06:50 PM <DIR> PycharmProjects\n",
+ "06/03/2022 09:21 AM 25,465 Python_practice_6_1 (1).ipynb\n",
+ "06/07/2022 09:34 PM 4,627 Python_practice_6_6.ipynb\n",
+ "04/29/2021 12:44 AM <DIR> Saved Games\n",
+ "04/29/2021 12:44 AM <DIR> Searches\n",
+ "06/27/2022 10:33 AM 46,172 sparty.png\n",
+ "06/02/2022 10:58 PM 2,752 Untitled.ipynb\n",
+ "04/29/2021 12:44 AM <DIR> Videos\n",
+ "06/28/2022 07:48 AM 42,345 _Template.ipynb\n",
+ "06/08/2022 10:08 AM <DIR> __pycache__\n",
+ " 32 File(s) 1,516,876 bytes\n",
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+ "source": [
+ "!dir\n",
+ "\n"
+ ]
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+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "8e746444",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('answercheck.py', <http.client.HTTPMessage at 0x20990c87880>)"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "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",
+ "metadata": {},
+ "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": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.9.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/PTest.ipynb b/PTest.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cc6c5b797b51bb70fd201078150dcbcd4b7f0b44
--- /dev/null
+++ b/PTest.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "15c5a985",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('answercheck.py', <http.client.HTTPMessage at 0x22adaab7c40>)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "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": "031076bc",
+ "metadata": {},
+ "source": [
+ "# PTest \n",
+ "Understanding and computing using the Ptest "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "87deb2e1",
+ "metadata": {},
+ "source": [
+ "# Description \n",
+ "A p-test is a statistical method to test the validity of a commonly accepted claim about a population. That commonly accepted claim is called a null hypothesis. Based on the p-value, we reject or fail to reject a null hypothesis.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a182669f",
+ "metadata": {},
+ "source": [
+ "# Training Materials \n",
+ "https://www.youtube.com/watch?v=KS6KEWaoOOE\n",
+ "\n",
+ "https://www.youtube.com/watch?v=8Aw45HN5lnA"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c7fd81c",
+ "metadata": {},
+ "source": [
+ "# Self Assessment "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6b14d8f0",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.9.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}