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CS2035 Assignment

Creation date：2024-06-21 15:42:33

Belonging category：计算机computer science

Tag CS2035

Assignment

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CS2035

Assignment
a) The population of the US in 1990 was estimated at 249.62 million. Verify that the
counties present in this data set cover more than 99% of the US population.
b) The state names are abbreviated with only two letters in the data set. With the
help of the file US-states-abbrev.txt, add a variable that displays the full name
of the state.
c) Generate a table that displays, for every state, the median value of the percentage
of votes at the county level, for each party. The table should look like this (use the
full name for states):
state democrat republican Perot
Alabama 42.9 46.3 10.5
Arizona … … …
… … … …
Wyoming … … …
d) Create a function plot linreg(T, party, var) that has three inputs: the table T
of the imported data set, a string variable party that represents the percentage of
votes received by the party and another string variable var that represents a
demographic variable of the data set. This function will create a scatter plot of the
two variables party and var with the linear regression line. You will indicate in the
plot title the values of the intercept, slope and unadjusted R2
. For example, the call
plot linreg(T,”democrats”,”black”) should produce a figure similar to Figure 1.
e) Using plot linreg(), create a figure with 15 subplots. The subplots will be
organized in three rows, one for each political party and five columns. The column
will represent the following variables: crime, income, college, white and
black.
f) Explore the dataset and describe (preferably with a figure) something you find
interesting, that has not been covered in this exercise (it could be trends,
unexpected distribution, etc.).
Page 2 of 5
University of Western Ontario CS2035
Exercise 2 – Basketball Player Doping?
A basketball league is worried that one of its player is taking athletic
performance-enhancing drugs (“doping”). The league suspects that the player may have
started doping around the 40th game last season. The league is providing you with the
player’s points-per-game (PPG) for every match of last season in order to establish if the
apparent increase in this player’s PPG can be caused by chance alone. See the file named
basketball-ppg.csv.
a) Plot the time series of the player’s PPG as a function of the game number (game 1
is the first of the season, game 2 the second, and so on). Mark with a dashed
vertical line the time t
∗ when the player is suspected to start doping.
b) Check visually that the distributions of PPGs before and after t
∗ are approximately
normally distributed.
c) Check normality with the more formal Kolmogorov-Smirnov test.
d) Based on your findings about normality of the PPGs, explain why a Z-test can be
used to test if the distribution of PPGs after is different from before t
∗
. State the
Null Hypothesis.
e) Determine, with a 1% confidence level, if the change before/after t
∗
in the player’s
PPGs is due to chance alone. Does your analysis support the league’s suspicion?
Page 3 of 5
University of Western Ontario CS2035
Exercise 3 – Sales Analysis
A large company would like to have a brief analysis regarding the sales of its new
division, Great Products Inc., that manufactures and sells electronic components. The
company has extracted from its main databases the sales records for Great Product Inc.
and has sent you the following files:
• db cust country.csv: The global list of their customers’ unique identification
number (not only customers of Great Products) and their country of origin.
• db cust orders.csv: Sales orders fulfilled by Great Products Inc. that shows the
order ID and the customer’s ID.
• db order ref.csv: The information that links a sales order ID with the reference
ID of the item sold as well as the quantity shipped.
• db ref price.csv: The unit price, in dollars, of an item given its reference ID.
a) Merge the information from all four files such that you end up with a table that
contains only the customers from Great Products Inc, their customer ID, the ID of
the order (the transaction), the country of origin of the customer, the reference ID
of the item they purchased, the quantity purchased, and the unit price of that item.
b) Display a breakdown by countries of the total revenues generated by Great Product
Inc.

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LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 FINC5090 MATH1052 SciComp Elec4622 FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 MAT 237 BISM2201 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum

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