Lunarnotes - SW13_Auftrag

{"cells":[{"cell_type":"markdown","source":["# Self-Study Tasks SW13 - PYTHON BASICS\n","\n","These are the self-study tasks of the semester week, which you will solve within one week in your JupyterHub environment. After completing your work, download a copy of the Jupyter notebook file locally to your laptop (Menu: File->Download).\n","\n","On ILIAS you will find the weekly scheduled assignment where you will upload your solved Jupyter notebook file. After your submission, you will receive a corresponding sample solution to the assignment. Your submission will not be corrected. Although the assignments are marked “mandatory”, they do not count towards your semester grades. Only the grades of the tests during the semester are relevant for this.\n","\n","We wish you every success!\n","\n","***\n","***"],"metadata":{"id":"n63mClNoHBNW"},"id":"n63mClNoHBNW"},{"cell_type":"markdown","id":"b5b2fec7","metadata":{"id":"b5b2fec7"},"source":["# Normal distribution and binomial distribution\n","\n","## Problem\n","In an experiment, the number of “heads” results is determined over 10,000 tosses of a coin. Calculate the probability that the number is at least equal to 5050, on the one hand exactly using the binomial distribution, on the other hand using the normal distribution (central limit theorem)."]},{"cell_type":"code","source":["# computation using binomial distribution\n","import math\n","\n","...\n","print(p)"],"metadata":{"id":"iF2Jta18vZWS"},"id":"iF2Jta18vZWS","execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Normal distribution: ..."],"metadata":{"id":"hj8rLHdmbt9R"},"id":"hj8rLHdmbt9R"}],"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":"3.11.7"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}

SW13_Auftrag_EN