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ECOM20001 Econometrics
Creation date：2024-05-10 16:44:35
Belonging category：经济Economics
Econometrics

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ECOM20001  Final Exam

 This is a preview of the draft version of the quiz.
Started: Oct 11 at 16:20
Quiz Instructions
Academic Integrity Declaration
By commencing and/or submitting this assessment I agree that I have read and understood the
University’s policy on academic integrity.
I also agree that:
1. The work I submit will be original and solely my own work (cheating);
2. I will not seek or receive any assistance from any other person (collusion); and,
3. I will not use any sources without proper acknowledgment or referencing (plagiarism).
Semester Two Assessment, 2021
Faculty/Dept: Faculty of Business and Economics, Department of Economics
Subject Number: ECOM20001
Subject Name: Econometrics 1
Writing Time: 3 hours
Reading: 30 minutes
Start and End Times (for students without an Alternative Exam Arrangement
(AEA))
Exam Start: 3:00 PM Australian Eastern Standard Time (AEST) on
Tuesday 09/11/2021
Exam End: 6:30 PM Australian Eastern Standard Time (AEST) on Tuesday
09/11/2021
You must start and submit your exam within this 3:00 PM - 6:30 PM
AEST time window.
The exam will end automatically at the 6:30 PM cut-off time
Students in different time zones are reminded to be mindful of relevant
time zone differences
Start and End Times (for students with an Alternative Exam Arrangement
(AEA))
Refer to your AEA for the the Exam Start and End times.
Open Book Status: Open Book
Additional Information
11/10/2021, 16:20 Quiz: ECOM20001 2021 S2 Final Exam
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Exam timetable information can be found on my.unimelb.edu.au
Instruction to Students
This examination contributes 70% to the final subject mark for ECOM20001
The exam includes 4 sections, 25 questions, and 100 marks in total.
Answer all questions using the Canvas LMS quiz tool.
Section A: multiple choice
Sections B, C, D: type in answers directly into the text boxes provided for
each question
Questions overview
Section A (20 marks)
10 questions (Q1 to Q10). Each question carries 2 marks.
Section B (36 marks)
3 questions (Q11, Q12, Q13). Each question carries 12 marks.
Section C (22 marks)
6 questions (Q14, Q15, Q16, Q17, Q18, Q19).
Section D (22 marks)
6 questions (Q20, Q21, Q22, Q23, Q24, Q25).
Exam Conditions
All exams will be moderated.
Please contact 13MELB (+61 3 9035 5511) for assistance during the
exam if you are experiencing technical difficulties. They will be able to
contact your examiner during the duration of the examination.
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'Exam Support' link is set up specifically for your exam.
Do not contact your lecturer during the examination period.
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DO NOT press the SUBMIT QUIZ button unless you are
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and submit at least several minutes before the exam ends.)
After you have answered and checked your answers to all of the questions,
click on the SUBMIT QUIZ button at the bottom of the LMS quiz window to
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submit your exam for grading.
Attachments for the Exam
Please download the following PDF attachment before starting the exam by
clicking on the link.
formulas.pdf (formula sheet)
Good luck!
SECTION A: MULTIPLE CHOICE (20 points)
This section contains 10 questions (Q1-Q10).
2 ptsQuestion 1
H0: B1=B2 vs. H1: B1!=B2 (where != means “not equals”)
H0: B3=0 vs. H1: B3!=0
H0: B1=B2=0 vs. H1: at least one of B1!=0 or B2!=0
None of the above
Consider the following regression: Y_i = B0 + B1 X1_i + B2 X2_i + B3 (X1_i *
X2_i) + u_i. Suppose we wanted to test whether the partial effect of X1_i is the
same as the partial effect of X2_i. What are the correct null and alternative
hypotheses?
2 ptsQuestion 2
In a sample with n=61 observations you run a regression with k=5 regressors and
obtain a standard error of the regression (SER) of SER=12. The sample variance
of Y is 132. What of the following statement about the R-squared (R2) is correct?
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R2 = 0
0 < R2 < 0.5
R2 = 0.5
R2 > 0.5
2 ptsQuestion 3
0.2
0.8
0.08
none of the above
Suppose we are interested in the relationship between weather (X=0 means no
rain; X=1 means rain) and stock market performance (Y=0 means stock market
falls; Y=1 means stock market rises). Suppose the joint distribution of X and Y is
given as P(X=0, Y=0)=0.32, P(X=0, Y=1)=0.48, P(X=1, Y=0)=0.08, P(X=1,
Y=1)=0.12. What is the probability of raining conditional on a stock market fall?
2 ptsQuestion 4
Reject null at the 1% significance level
Reject null at the 5% significance level, but not at the 1% significance level
Reject null at the 10% significance level, but not at the 5% significance level
Not reject null at the 10% significance level
Suppose you estimate a single linear regression of Y on X and obtain a slope
estimate of B1=-12 with a standard error of SE(B1)=2. Suppose that you wanted
to test the null hypothesis that the true value of B1 is -16 against the alternative
that it is not equal to -16. Which of the following is correct?
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2 ptsQuestion 5
The OLS estimator is the best linear unbiased estimator
The variance of the error term is the same for any X
A linear regression model fits better than a nonlinear regression model
The variance of the OLS estimator can be estimated
Which of the following statements are wrong under homoskedasticity?
2 ptsQuestion 6
An additional bar per 10,000 population is associated with an increase of 10 infected
individuals per 1,000 population
An additional bar per 10,000 population is associated with an increase of 1 infected
individual per 1,000 population
An additional bar per 10,000 population is associated with an increase of 0.1 infected
individuals per 1,000 population
An additional bar per 10,000 population is associated with an increase of 0.01 infected
individuals per 1,000 population
Suppose you run a single linear regression of Infect_i = B0 + B1 Barden_i + u_i
where Infect_i is the number of infected individuals per 10,000 population in area i
and Barden_i is the number of bars per 1,000 population in area i. If you obtained
a B1 estimate of 10, which of the following would be the correct interpretation?
2 ptsQuestion 7
The slope estimate must be positive
Suppose X is the regressor and Y is the dependent variable. Suppose there is
classical measurement error in X. When we regress Y on X, which of the following
statement is correct?
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The slope estimate must be close to zero
The slope estimate may be negative or positive
None of the above
2 ptsQuestion 8
Yes
No
We need to know the sample mean to answer the question
There is not enough information
Suppose we carry out hypothesis testing on the population mean of a random
variable. Suppose from a random sample with a very large sample size you
compute a 95% confidence interval for the population mean of [-1, 3.9]. Further,
suppose that you wanted to test the null hypothesis that the population mean
equals 4 against the alternative that it is not equal 0. Would you reject the null at
the 1% level of significance?
2 ptsQuestion 9
It is the elasticity of Y_i with respect to X_i
A 1-unit increase in X_i yields a 100B1% increase in Y_i
A 1-unit increase in X_i yields a B1% increase in Y_i
A B1 unit increase in X_i yields a 1% increase in Y_i
Consider the following regression: ln(Y_i)=B0 + B1 X_i + u_i. Which of the
following is the correct interpretation of B1?
2 ptsQuestion 10
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X and Y are statistically independent because raining cannot affect the stock market
X and Y are statistically independent
X and Y are not statistically independent
X has a causal effect on Y if X and Y are not statistically independent
Suppose we are interested in the relationship between weather (X=0 means no
rain; X=1 means rain) and stock market performance (Y=0 means stock market
falls; Y=1 means stock market rises). Suppose the joint distribution of X and Y is
given as P(X=0, Y=0)=0.32, P(X=0, Y=1)=0.48, P(X=1, Y=0)=0.08, P(X=1,
Y=1)=0.12. Which of the following is correct?
SECTION B (36 points)
This section contains 3 questions (Q11, Q12, Q13).
12 ptsQuestion 11
We are interested in estimating casual effect of yoga exercise on health.
Answer the following questions. Show your working. Wherever relevant, you
should write down the estimation equation, define all the variables that you
would use, and briefly describe the hypothesis test involved. (Hint: when
conducting the test, do not impose restrictions on the sign of the effect.)
(a)(3pts) Suppose we have a data set for all residents in Melbourne. There are
two variables: individual’s health score (SCORE); individual’s number of hours of
yoga exercise per week (YOGAHRS). Suppose individuals who regularly do yoga
exercise also tend to eat more unhealthy food (e.g., they indulge themselves after
exercising). Assume that eating more healthy food results in better health, all else
being equal. Write down the equation for the single linear regression of SCORE
on YOGAHRS, and then explain what the direction of the omitted variable bias
would be for the slope coefficient on YOGAHRS.
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(b)(3pts) Suppose someone in a health organization suggests a randomized
controlled trial, which randomly selects half of the Melbourne residents to be given
a free yoga mat. Suppose you have data on all Melbourne residents' health scores
and whether each individual is given a free yoga mat. Explain how you would
evaluate the effect of this policy using regression.
(c)(6pts) Suppose the manager thought that the experiment was too expensive.
Instead, the manager held free yoga lessons in five suburbs of Melbourne
(Carlton, Parkville, Southbank, Docklands, Flemington) in February 2021.
Suppose you have data on all Melbourne residents' health scores in January 2021
and February 2021. Explain how you would use a differences-in-differences model
to evaluate the effect of this policy. You should also describe how you define the
treatment and control groups and pre- and post-policy periods in the data.

12 ptsQuestion 12
Consider the following population linear regression model of individual
consumption: Y = 30 + 0.2X + u, where Y is yearly consumption (in thousand
dollars), X is the individual’s yearly income (in thousand dollars), and 30+0.2X is
the population regression line. Suppose we generate artificial data for 3
Edit View Insert Format Tools Table
12pt Paragraph
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individuals using this model. This artificial sample, which consists of 3
observations, is shown in the following table:
Observation X Y E(Y|X) u
Predicted value of Y
(Yhat)
Residual
(uhat)
1 30 50 V1 V2 45 5
2 70 V4 44 ? V3 -10
3 110 ? ? ? ? V5
Suppose we know that in this artificial sample, the sample mean of Y is 60, the
sample variance of Y is 300, the sample variance of X is 1600, and the sample
covariance between X and Y is 600. Assume these summary statistics hold in all
parts of this question.
Answer the following questions. Show your working.
(a)(2pts) What are the values of V1 and V2?
(b)(3pts) Compute the OLS regression line of the regression of Y on X. (Hint:
Assume the OLS regression line continue to hold in parts (c)-(e).)
(c)(2pts) What are the values of V3 and V4?
(d)(2pts) Compute the value of V5 using the information given by the table alone.
Briefly explain how you obtain this result.
(e)(3pts) Compute the R-squared of the regression in part (b). Show your
working.
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12 ptsQuestion 13
Consider the regression model Y = B0 + B1 X1 + B2 X2 + u. Suppose you want to
test the null hypothesis H0: B1 + 2 B2 = 0, versus the alternative hypothesis H1:
B1+ 2 B2 != 0 (!= means "not equal to"). The data set consists of 300
observations.
Answer the following questions. Show your working. In all places that are
relevant, you should describe the test statistic and critical-value (or p-value)
used.
(a)(6pts) Describe TWO different ways of transforming the regression to conduct
the test.
(b)(2pts) Suppose we use an F-statistic to conduct the test. What are the degrees
of freedom associated with this test statistic?
(c)(2pts) Let G(.) be the CDF of the F-distribution for the F-statistic in part (b).
Denote the actual F-statistic by F_act. Suppose G(F_act)=0.07. Do you reject the
null hypothesis at the 10% significance level? Briefly explain why.
(d)(2pts) Suppose you find that the F-test in part (b)-(c) and the test in part (a)
yield identical p-values. Do you think this result is correct? Briefly explain your
reasoning.
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SECTION C (22 points)
This section contains 6 questions (Q14, Q15, Q16, Q17, Q18, Q19).
A data set of 5,000 workers contains the following variables:
LNEARN: logarithm of annual earnings
AGE: age in years
AGE_SQ: square of AGE
LNAGE: logarithm of AGE
Regression output with HC standard errors is shown below:
Dependent variable:
LNEARN
(1) (2)
LNAGE 0.491
(0.040)
AGE 0.098
(0.007)
AGE_SQ -0.001
(0.0001)
CONSTANT 8.955 8.572
(0.150) (0.152)
R2 0.037 0.069
Answer the following questions. Show your working. (Hint: You may want to
take a screenshot of the regression output so you don't have to scroll up
and down when you answer the questions.)
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4 ptsQuestion 14
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Discuss the result in regression (1) by interpreting the coefficient estimate on
LNAGE and discussing its statistical significance. Then, based on regression (1),
compute the 95% confidence interval for the coefficient on LNAGE.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 BCPM0008 ECN 620 QBUS6830 ECSE-415 ECON5102 MGMT20005 MAT 653 ECO 480 COMP 5/600 CSSE 5600 ME 571 7CCMMS61 CS 550 COMP2865 MATH3075 STATS 506 QTM 220 ISYS2120 FIT5106 STAT 153 MKTG3112 ECON2102 ELE2018 COMP3031 MSIN0010 SCC461 MATH237 CS246 CSC3065 CSE 101 ICTWEB411 COMP9313 ECOS 2025 FN620 MATH11111 MATH 3018 KE1068 FINA 6112 C++ MISM 6202 MSIN0105 CSC8631 MECO6935 COMP4121 ACCT2011 DPBS1150 EEL 3701C F19PL OPM 661 CSE 310 CS918 BIOL30001 ACCT3563 BANK3600 MET CS622 MATH367 ACCT5907 Continuing COMP5048 COMP61021 CS3334 GSOE9210 CSCI-570 COMP 5322 HW 2 ALY-6020 IEOR 4706 DATA 200 EBUS504 CGE12412 COMP4108 7SSMM800 ECMM171P MATE50002 CS225 RS 6003 BH2274 ELEC3111 MAT6669 ECON-UA 227 PHIL 1012 7SSMM603 TB028A P6 ECS708 CSC 427 CS6476 CSE 250A A1A2 ITS61504 EECS 376 model HW6 UV0010 ECON 6074 MG4F7 CMP-7030Y INFO3008 BFIP013 ECON 5068 MATH6002 CS3483 PSTAT 131 C31FM CSOR W4246 AAEC 5307 DATA ELEC362 COMSM0047 ECN6006 MA20224 BST351 MATH 323 MATH323 AA2021 COMP3811 COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 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FN1024 MATH0094 MANG 6554 ECOS3003 ELEC6252 C2511C PHY328 EC1060 ECON32202 COMP4002-E1 MATH3063 EFIMM0124 QBUS2310 ECON4411 FUNDAMENTALS ACCT20001 18YX RPM COMP 2711 QBUS6600 BUSS5221 ST3189 FIT1047 MAT344 EGB375 MATH60013 MATH96011 COMP3557 MAST20029 ECM159 EAE1011 ENSCEN 105 SECTION A STA 141C SOSE 2022 PAM006 COMP0048 M40007 ISOM2500 CMPE 142 A06472 MCC150 ACST 8083 GEOG6087 ECON920 FINM7008 ENVENG 4110 COMPSCI 361 ENGRCEE 21 PHYS 1112 ACF2100 ECMM108 COMPS267F MAT00050M MAST30025 CSIT970 MSCI 224 INT202 PUBL0054 MSDM5004 BMAN 21020 BMAN21020 MGMT1002 MATH11150 LAWS7023 ACTL30002 CIVL3340 MATH1002 ECON 8069 COMPSCI 351 IBUS7302 STAT4528 CHEM1101 STAT3925 BUSN 7008 Comp 3120 ECOS2040 SWEN90010 ENGN6528 ECM158 ECON20022 COMP6452 Calculation CITS1401 S1889112 BUS 351 ACTL30008 FIT2091 ECON6002 IBUS6020 PHI 001 ECON30290 MATH7501 ECON10004 MFIN7017 ISYS90043 LAWS7012 ECON30025 COMP30023 COMM1190 ECOM20001 GSBS6009 STAT 337 EMET8002 ECOM90001 ECOM30001 FINC 2012 BUSINESS ENGCV512 ECO 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CMP7000A CMP-7038B Math 263 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 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. 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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