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日期:2020-10-22 08:36

MTH117 Test 2 Take-Home Portion
Please remember that the Honor Code is in effect for all graded work. This is the
second portion of Test 2. The grade from this portion will be combined with the grade from the
first portion (completed through Canvas). This portion of Test 2 is due by 1pm Eastern time
Tuesday, October 20, 2020. Please turn in your work through Canvas.
Task 1: Histogram in R
1. Go to http://archive.ics.uci.edu/ml/machine-learning-databases/00300/
2. Download the Tennis-Major-Tournaments-Match-Statistics.zip file.
3. Locate the Wimbledon-women-2013.csv file.
4. In tennis, an unforced error is abbreviated UFE.
Use R to read in the file mentioned in #3 and to create a histogram of the data listed in
UFE.1 in the file.
5. Turn in your code that completes these tasks. Please do not send in a screenshot.
Please either add me to your project in CoCalc or copy/paste your code into a document.
Task 2: Code in Python
Create your own code in Python that computes the mean of the following three numbers from a
dataset: 1) smallest piece of data, 2) the largest piece of data, and 3) the median of a given dataset.
This should work for any given vector of data. Turn in your code that completes this task.
Please do not send in a screenshot. Please either add me to your project in CoCalc or copy/paste
your code into a document.
Note: I am not giving you the vector that I will use to test your code. It is a good idea to
create at least one toy dataset for you to use to test your code.
Note: Do not import functions to find the mean or median of the dataset. You can complete
this task by using what we have learned so far in class.
Note: If you are confused on what this task is asking you to do, please email me. If you are
confused on where to start, begin by outlining the tasks that the computer needs to do. Then look
through our class materials to get ideas.
Note: The University of California, Irvine has 497 Data Sets posted to the Machine Learning
Repository. These are helpful for trying out various data science tasks.

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