MTH117编程代做、R编程设计图示、R语言程序代写-代写Algorithm 算法作业

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.