STAT 202A
HOMEWORK 1
Output
Question 1
Output:
source('R/hw1.R')
#Solution: Generate 100 iid uniform random variables and calculate following quantile estimates by repeating
100,000 #times:
#1a) Output: Ultimate sample mean of the 90th of the 100 values s
1. Admin stuff.2. Rejection sampling again.3. R Cookbook ch. 8-9.4. R Cookbook ch 10.5. R Cookbook ch 11.1. Admin stuff.Reminder, no class or office hour Tue Oct 22.We will switch to C next lecture, and then return to R Cookbook later in the course. Tue,
1. Final projects.2. R Cookbook ch. 11.3. XCode.4. Compiling C and calling C from R.5. Hello world.6. pi.7. dnorm.8. Sum of squared differences between observations.1. Final projects. I made a couple small changes to the schedule. It only affects teams 5
1. Admin.2. R Cookbook ch. 11.3. C programming, mypi.c.4. dnorm in C.5. Sum of squared differences between observations in C.1. Admin.Submit hw before the due date. Hit the submit button.No office hours Tue Oct 28 because of the faculty retreat. Final pro
0. Final projects and other miscellaneous notes.1. Defining vectors and matrices within C.2. C functions communicating.3. Running C from terminal.4. Reading in from a file.0. Final projects and other misc notes.HW3 is due by email Thu Nov 14, 1030am.Group
1. Admin things.2. Kernel density estimation.3. 2-d kernel smoothing.4. Simulating from a density by rejection sampling.5. Maps.6. R Cookbook ch. 8-9.1. Admin things.You must work independently for all 4 homeworks!Read through ch10 for next class.Attendan
Homework 1. Stat 202a. Due Tue, Oct 8, 10:30am.
You must work on the homework INDEPENDENTLY! Collaborating on this homework
will be considered cheating. Submit your homework to me by email to
stat202a@stat.ucla.edu. Your homework solution should be a sing
0. Misc.1. Sum of squared differences between observations in C.2. Sum of squared differences from observations for each gridpoint, in C.3. Kernel regression.4. Teetor ch. 11.5. Projects.0. Misc.Hw3 is on the course website now.After today, you will have
1. Final projects and group assignment.2. Kernel density estimation in R, continued.3. 2-d kernel smoothing.4. Simulating from a density by rejection sampling.5. Maps.6. R Cookbook ch. 8.Reminder, no class or office hour Tue Oct 22.1. Final projects.I wil
1. Enrollment.2. Plotting the sample mean.3. R Cookbook.1. Enrollment.I'm not giving out any more PTE numbers. If you are an unenrolled Statistics MS or PhD student, then please see me.Note small changes to hw1 ("below" in line 5, and "100,000" in 2b.)2.
STAT 202A
HOMEWORK 2
Output
Question 1
Output:
Figure 1: A plot of your kernel density estimate of the earthquake magnitudes,
(m), from part 1b), along with your simulation-based 95% confidence bands from part
1d).
Figure 2. A plot of your 2-dimensional k
STAT 202A
HOMEWORK 1
Output
Question 1
Output:
source('R/hw1.R')
#Solution: Generate 100 iid uniform random variables and calculate following quantile estimates by repeating 100,000
#times:
#1a) Output: Ultimate sample mean of the 90th of the 100 values s
STAT 202A
HOMEWORK 2
Output
Question 1
Output:
Figure 1: A plot of your kernel density estimate of the earthquake magnitudes,
(m), from part 1b), along with your simulation-based 95% confidence bands from part
1d).
Figure 2. A plot of your 2-dimensional k
Homework 3. Stat 202a. Due Thur, Nov 13, 10:30am.
You must work on the homework INDEPENDENTLY! Collaborating on this homework
will be considered cheating. Submit your homework by CCLE. Late homeworks will
not be accepted! Your homework solution should be
Homework 3. Stat 202a. Due Thur, Nov 14, 10:30am.
You must work on the homework INDEPENDENTLY! Collaborating on this homework
will be considered cheating. Submit your homework by email to stat202a@stat.ucla.edu.
Late homeworks will not be accepted! Your h
Homework 2. Stat 202a. Due Tue Oct 28, 10:30am.
You must work on the homework INDEPENDENTLY! Collaborating with other students
on this homework will be considered cheating. Submit your homework via the CCLE
site. Late homeworks will not be accepted! Your
CSE 20 NB Exam1 V1 NEW
Kevin Zavier
43/50
Algorithms and optimization
Why not correct
Demonstrating knowledge of definition of
algorithm being optimal
Demonstrating ability to trace greedy algorithm
Valid choice of example
Correct optimal change for this