Question 1 Plot a histogram of the scores in the cell below

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10/31/2018 hw09 1/25 Homework 9: Central Limit Theorem Reading : Why the mean matters () Please complete this notebook by filling in the cells provided. Before you begin, execute the following cell to load the provided tests. Each time you start your server, you will need to execute this cell again to load the tests. Homework 9 is due Thursday, 11/1 at 11:59pm . You will receive an early submission bonus point if you turn in your final submission by Wednesday, 10/31 at 11:59pm. Start early so that you can come to office hours if you're stuck. Check the website for the office hours schedule. Late work will not be accepted as per the policies () of this course. Directly sharing answers is not okay, but discussing problems with the course staff or with other students is encouraged. Refer to the policies page to learn more about how to learn cooperatively. For all problems that you must write our explanations and sentences for, you must provide your answer in the designated space. Moreover, throughout this homework and all future ones, please be sure to not re-assign variables throughout the notebook! For example, if you use max_temperature in your answer to one question, do not reassign it later on. In [6]: # Don't change this cell; just run it. import numpy as np from datascience import * # These lines do some fancy plotting magic. import matplotlib % matplotlib inline import matplotlib.pyplot as plt plt . style . use( 'fivethirtyeight' ) import warnings warnings . simplefilter( 'ignore' , FutureWarning ) from client.api.notebook import Notebook ok = Notebook( 'hw09.ok' ) _ = ok . auth(inline = True ) ===================================================================== Assignment: Homework 9: Central Limit Theorem OK, version v1.12.5 ===================================================================== Successfully logged in as [email protected]
10/31/2018 hw09 2/25 1. The Bootstrap and The Normal Curve In this exercise, we will explore a dataset that includes the safety inspection scores for restaurants in the city of Austin, Texas. We will be interested in determining the average restaurant score for the city from a random sample of the scores; the average restaurant score is out of 100. We'll compare two methods for computing a confidence interval for that quantity: the bootstrap resampling method, and an approximation based on the Central Limit Theorem. In [7]: # Just run this cell. pop_restaurants = Table . read_table( 'restaurant_inspection_scores.csv' ) . drop( 5 , 6 ) pop_restaurants Question 1 Plot a histogram of the scores in the cell below. Out[7]: Restaurant Name Zip Code Inspection Date Score Address 6M Grocery 78652 01/17/2014 90 805 W FM 1626 RD AUSTIN, TX 78652 6M Grocery 78652 04/27/2015 93 805 W FM 1626 RD AUSTIN, TX 78652 6M Grocery 78652 05/02/2016 88 805 W FM 1626 RD AUSTIN, TX 78652 6M Grocery 78652 07/25/2014 100 805 W FM 1626 RD AUSTIN, TX 78652 6M Grocery 78652 10/21/2015 87 805 W FM 1626 RD AUSTIN, TX 78652 6M Grocery 78652 12/15/2014 93 805 W FM 1626 RD AUSTIN, TX 78652 7 Eleven #36575 78660

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