• Time: Tuesday/Thursday 10:45 AM - 12:00 PM
• Location: STEPS Building 290
• Taeho
Kim / tak422@lehigh.edu
• Office: Chandler-Ullmann 229
• Office Hours:
T 12:00-1:00 pm, R 2:00-3:00 pm or by
appointment.
• Web Office Hours: W
1:30-2:30 (PW:010011)
• Syllabus: Download
• Gradescope: [LINK]
1. The final grade has been submitted.
2. Have a great summer break!
| Week | Slide | R Code | Data Sets & etc. |
|---|---|---|---|
| Week | Slide | R Code | Data Sets & etc. |
| 1 | [R Basic I], [R Basic II] | [R Basic I], [R Basic II] | [FOREARM] |
| 2 | [R Basic III], [R Graphics] | [R Basic III], [R Graphics] | - |
| 3 | [Probability Review], [Statistics Review] | [Probability Review], [Statistics Review] | [Note for Prob. Review], [Note for Stat. Review] |
| 4 | [Generating Random Variable I] | [Generating RV I] | [Diff./Integ. Formula] |
| 5 | [Generating Random Variable II] | [Generating RV II] | - |
| 7 | [Monte Carlo Integration] | [Monte Carlo Integration] | - |
| 8 | [Variance Reduction] | [Variance Reduction] | - |
| 9 | [Monte Carlo in Inference] | [Monte Carlo in Inference] | [MC in Infer. vs MC Integ.], [Hypothesis Testing] |
| 10 | [Markov Chain Monte Carlo]. | [Markov Chain Monte Carlo] | - |
| 11 | [Bootstrap & Jackknife], [Bootstrap & Jackknife CIs] | [Bootstrap & Jackknife] | - |
| 12 | [Density Estimation] | [Density Estimation] | - |
| 13 | [Multivariate Dist’n & Gibbs Sampler] | [Multivariate Dist’n & Gibbs Sampler] | - |
| Exam | Date | Time | Location | Practice Exam |
|---|---|---|---|---|
| Exam | Date | Time | Location | Practice Exam |
| Exam I | 03/02 | 10:45-12:00 | STEPS Building 290 | |
| Exam II | 04/13 | 10:45-12:00 | STEPS Building 290 | |
| Final Exam | 05/11 | 08:00-11:00 | Drown Hall 210 |
| # | Homework | Release | Due (11:50pm) | Solution |
|---|---|---|---|---|
| # | Homework | Release | Due (11:50pm) | Solution |
| 1 | [HW1] / [HW1.Rmd] | 01/27 | 02/03 | [HW1:Sol] |
| 2 | [HW2] | 02/03 | 02/10 | [HW2:Sol] |
| 3 | [HW3] | 02/10 | 02/17 | [HW3:Sol] |
| 4 | [HW4] | 02/18 | 02/24 | [HW4:Sol] |
| 5 | [HW5] | 02/24 | 02/28 | [HW5:Sol] |
| 6 | [HW6] | 03/11 | 03/24 | [HW6:Sol] |
| 7 | [HW7] | 03/25 | 03/31 | [HW7:Sol] |
| 8 | [HW8] | 04/01 | 04/07 | [HW8:Sol] |
| 9 | [HW9] | 04/8 | 04/11 | [HW9:Sol] |
| 10 | [HW10] | 04/23 | 04/29 | [HW10:Sol] |
| 10 | [HW11] | 04/30 | 05/06 | [HW11:Sol] |
| # | Lab | Due Date (11:50 pm) | Solution |
|---|---|---|---|
| # | Lab | Due Date (11:50 pm) | Solution |
| 1 | [Lab 1] | 01/24 | - |
| 2 | [Lab 2] | 01/26 | [Lab 2:Sol] |
| 3 | [Lab 3] | 01/31 | [Lab 3:Sol] |
| 4 | [Lab 4] | 02/02 | [Lab 4:Sol] |
| 5 | [Lab 5] | 02/07 | [Lab 5:Sol] |
| 6 | [Lab 6] | 02/09 | [Lab 6:Sol] |
| 7 | [Lab 7] | 02/14 | [Lab 7:Sol] |
| 8 | [Lab 8] | 02/16 | [Lab 8:Sol] |
| 9 | [Lab 9] | 02/21 | [Lab9:Sol] |
| 10 | [Lab 10] | 02/23 | [Lab10:Sol] |
| 11 | [Lab 11] | 03/07 | [Lab11:Sol] |
| 12 | [Lab 12] | 03/09 | [Lab12:Sol] |
| 13 | [Lab 13] | 03/21 | [Lab13:Sol] |
| 14 | [Lab 14] | 03/23 | [Lab14:Sol] |
| 15 | [Lab 15] | 03/28 | [Lab15:Sol] |
| 16 | Lab 16 was cancelled. | 03/30 | - |
| 17 | [Lab 17] | 04/04 | [Lab17:Sol] |
| 18 | [Lab 18] | 04/06 | [Lab18:Sol] |
| 19 | Lab 19 was cancelled. | 04/19 | - |
| 20 | [Lab20] | 04/21 | [Lab20:Sol] |
| 21 | [Lab21] | 04/25 | [Lab21:Sol] |
| 22 | [Lab22] | 04/27 | [Lab22:Sol] |
| Date | Topics Covered | Related Material |
|---|---|---|
| Date | Topics Covered | Related Material |
| 1/24 (Day1) | We went over the syllabus and started chapter I. Basic R syntax was covered. Lab 1 was done at the end of the class. | Course Material |
| 1/26 (Day2) | We continued to talk about the basic R syntax. Lab 2 was done at the end of the class. | Course Material |
| 1/31 (Day3) | We finished the basic syntax of R. Lab 3 was done at the end of the class. | Course Material |
| 2/02 (Day4) | We discussed the basic R graphics. Lab 4 was done at the end of the class. | Course Material |
| 2/07 (Day5) | We started to review probability. Lab 5 was done (only for 1st question) at the end of the class. | Course Material |
| 2/08 (Day6) | We finished probability review and talked about point estimation. Lab 6 was done at the end of the class. | Course Material |
| 2/14 (Day7) | We talked about interval estimation and hypothesis testing and almost finished statistics review. Lab 7 was done at the end of the class. | Course Material |
| 2/16 (Day8) | We started chapter 3 and covered the inverse transformation method (continuous) for random variable generation (The uncovered statistics review will be given in HW IV). Lab 8 was done at the end of the class. | Course Material |
| 2/21 (Day9) | We covered the inverse transformation method (discrete) for random variable generation. Lab 9 was done at the end of the class. | Course Material |
| 2/23 (Day10) | We learned the Acceptance-Rejection method for random variable generation, and finished Chapter 3. Lab 10 was done at the end of the class. | Course Material |
| 2/28 (Day11) | We reviewed the practice exam. | Course Material |
| 3/02 (Day12) | We held our first exam. | Course Material |
| 3/07 (Day13) | We started Monte Carlo Integration in chapter 6. Lab 11 was done at the end of the class. | Course Material |
| 3/09 (Day14) | We learned different approaches to choose f(x) and g(x) in Monte Carlo integrations. | Course Material |
| 3/21 (Day15) | We started the topic of “variance reduction” after reviewing properties of variance of MC estimators. Antithetic variates approach was introduced as the first variance reduction method. | Course Material |
| 3/23 (Day16) | Control variates and importance sampling approach were introduced as the second and third variance reduction methods. | Course Material |
| 3/28 (Day17) | We covered MC methods in point estimation and confidence interval. The focus was more on the evaluations of the procedure: MSE for point estimation; coverage probability and expected length for confidence interval. | Course Material |
| 3/30 (Day18) | We talked about the contruction and evaluation ot hypothesis testing by using a t-test example. Hypothesis testing uses the probability of Type-I and Type-II errors as its performance measures, and we can use the MC methods for their approximation. | Course Material |
| 4/04 (Day19) | We reviewed the evlauation of hypothesis testing. Started MCMC and covered the basic idea of Markov Chain. | Course Material |
| 4/06 (Day20) | Metropolis-Hastings sampler (MHS) was introduced to implement MCMC. Briefly covered Bayesian statistics and saw why MHS can be particularly useful to generate r.v.’s from posterior distribution. | Course Material |
| 4/11 (Day21) | We revied the material and went over the practice exam. | Course Material |
| 4/13 (Day22) | Exam II | Course Material |
| 4/19 (Day23) | We started Bootstrap and Jackknife. | Course Material |
| 4/21 (Day24) | We covered five different Bootstrap and Jackknife confidence interval procedurese. | Course Material |
| 4/25 (Day25) | We talked about histogram as a density estimator. There were different results regarding the bind width and # of bins, including Sturges’ rule, Scott’s rule, and Freedman & Diaconis rule. | Course Material |
| 4/27 (Day26) | We learned improved density estimators: the averaged shifted histogram and kernel density estimator. | Course Material |
| 5/02 (Day27) | We reviewed multivariate distributions and talked about the Gibbs sampler to generate r.v.’s from multivariate distributions or their marginal distributions. This was a special topic and will not be on the final exam. | Course Material |
| 5/04 (Day28) | We had a review session and solved the practice exam together. | Course Material |
Attendance
Attendance at lectures is required. Announcements will often be made in
class and students are responsible for the contents of every lecture.
The instructor will check attendance.
Labs
Students will have short labs (10 – 25 mins) after lectures during class
time. In these labs, students will be able to run the R code
examples illustrated in the lectures and write their own R code
for similar questions. The labs will only be graded for completion.
Students are required to submit their R script file (.R) to Gradescope
by 11:50 pm on their assigned dates. Clear comments to the R code are
required. No late labs will be accepted except in the case of a
documented University Excused Absence. The two lowest lab
scores will be dropped.
Project
Graduate students will have one project for this class. It will be an
individual project related to the material covered in the class. The
details will be provided after spring break. Undergraduate students
don’t have a project.
Homework
Homework assignments will be posted on the course website, usually every
Friday. Those are typically due a week later unless otherwise announced.
Homework assignments must be submitted via Gradescope by 11:50 pm on
their perspective due dates. The entire set of homework solutions must
be compiled using R Markdown. You must upload a compiled PDF of
your solutions and the .Rmd file. No late homework will
be accepted except in the case of a documented University Excused
Absence. The lowest homework grade will be dropped when
computing your course grade. You may discuss assigned problems with
fellow students, but you MUST write the solutions up yourself
independently. Copying solutions (from another student’s work
or another source) constitutes academic dishonesty. Content generated by
an Artificial Intelligence third-party service or site (AI-generated
content) without proper attribution or authorization is another form of
plagiarism. Graders have been instructed to alert instructors if they
suspect a student has copied solutions. Depending on the cases, a
complaint may be initiated against the student(s) involved through the
LU Judicial System.
It you ever have questions about drawing the line between others’ work
and your own, ask me and I will give you guidance or you may visit
Lehigh Library’s Proper Use of
Information.
Midterm
Midterms will take place during the regular class time, in the normal
classroom location. The detailed format of the midterms will be
announced later. There will be assigned seating for the Midterms. The
midterms are tentatively scheduled for March 2nd and April
13th.
Final Exam
The final exam is cumulative. The final exam is to be held during the
scheduled time by Registrar’s office.
| Component | Section 010 | Section 011 |
|---|---|---|
| Component | Section 010 | Section 011 |
| Attendance & Labs | 15% | 10% |
| Project | - | 10% |
| Homework | 20% | 20% |
| Midterm I | 20% | 20% |
| Midterm II | 20% | 20% |
| Final Exam | 25% | 20% |
| Total | 100% | 100% |
| Grading Scale | Range | Grading Scale | Range |
|---|---|---|---|
| Grading Scale | Range | Grading Scale | Range |
| A | 93-100 | A- | 90-92.9 |
| B+ | 87-89.9 | B | 83-86.9 |
| B- | 80-82.9 | C+ | 77-79.9 |
| C | 73-76.9 | C- | 70-72.9 |
| D+ | 67-69.9 | D | 60-66.9 |
| F | <60 |
If you must miss a scheduled exam, contact your instructor before the test date and time. Make-ups will only be offered to students with a valid justification. To qualify for a make-up exam due to a health issue, personal emergency event, or the death of a family member, the following three things must hold:
This schedule is subject to change depending on progress during the
semester.
| Topic | Chapter |
|---|---|
| Topic | Chapter |
| Introduction to R, RStudio, R Markdown and Basic Syntax | 1 |
| Probability and Statistics Review | 2 |
| Summary Statistics | 3 |
| Methods for Generating Random Variables | 3 |
| Midterm I | - |
| Monte Carlo Method in Integration | 6 |
| Monte Carlo Method in Inference | 7 |
| Markov Chain Monte Carlo (MCMC) Method | 11 |
| Midterm II | - |
| Bootstrap and Jackknife | 8 |
| Probability Density Estimation | 12 |
| Visualization of Multivariate Data | 5 |
| Final Exam | - |
During orientation, first-year students sign a pledge to abide by the Undergraduate Student Senate’s affirmation of the Code of Conduct. At the first-year convocation, a representative of the Student Senate presents a binder containing those signatures to the President. This symbolic ritual highlights the core values of honesty and integrity in Lehigh’s culture. The Provost for Teaching and Learning developed seven short vignettes describing cases where student actions bring into question issues of academic integrity and community standards. These vignettes are available at http://www.lehigh.edu/lts/official/Academic_Integrity_Vignettes.pdf. These vignettes on academic dishonesty cases are all based on actual cases that have come before the University Committee on Discipline. Various university web resources also provide material to help understand the student Code of Conduct’s expectations, way to report violations of the Code, and the thoughtful adjudication of Code violations to which the Dean of Students Office is committed. The Undergraduate and Graduate Student Senates have affirmed students’ responsibility to uphold academic integrity by creating student statements of academic integrity (http://go.lehigh.edu/integrityresources).
Students are expected to check email on a regular basis. Even if a student fails to check email for messages, the student is still responsible for any announcements made using email.
Lehigh University is committed to maintaining an equitable and inclusive community and welcomes students with disabilities into all of the University’s educational programs. In order to receive consideration for reasonable accommodations, a student with a disability must contact Disability Support Services (DSS), provide documentation, and participate in an interactive review process. If the documentation supports a request for reasonable accommodations, DSS will provide students with a Letter of Accommodations. Students who are approved for accommodations at Lehigh should share this letter and discuss their accommodations and learning needs with instructors as early in the semester as possible. For more information or to request services, please contact Disability Support Services in person in Williams Hall, Suite 301, via phone at 610-758-4152, via email at indss@lehigh.edu, or online at https://studentaffairs.lehigh.edu/disabilities.
Lehigh University endorses The Principles of Our Equitable Community. We expect each member of this class to acknowledge and practice these Principles. Respect for each other and for differing viewpoints is a vital component of the learning environment inside and outside the classroom.
Lehigh University upholds The Principles of Our Equitable Community and is committed to providing an educational, working, co-curricular, social, and living environment for all students, staff, faculty, trustees, contract workers, and visitors that is free from harassment and discrimination on the basis of age, color, disability, gender identity or expression, genetic information, marital or familial status, national or ethnic origin, race, religion, sex, sexual orientation, or veteran status. Such harassment or discrimination is unacceptable behavior and will not be tolerated. The University strongly encourages (and, depending upon the circumstances, may require) students, faculty, staff or visitors who experience or witness harassment or discrimination, or have information about harassment or discrimination in University programs or activities, to immediately report such conduct. If you have questions about Lehigh’s Policy on Harassment and Non-Discrimination or need to report harassment or discrimination, contact the Equal Opportunity Compliance Coordinator (Alumni Memorial Building / 610.758.3535 / eocc@lehigh.edu)
To meet the challenge of teaching and learning during the COVID-19
pandemic, Lehigh instructors and students will be adopting new forms of
instruction and interaction; following new guidelines around classroom
behaviors; enhancing communications; and doing our best to be patient,
flexible, and accommodating with each other.
In remote synchronous meetings, students are expected to attend class
just as they would any other Lehigh class. Zoom classes work best when
all students come to class ready to participate and follow the
instructor’s guidelines regarding use of web-cameras. You may be asked
to turn your camera on during active learning sessions in Zoom. If you
have a strong preference not to do so, please contact me to let me know.
Students should respect the in-classroom privacy of their instructors
and fellow students by not taking screenshots or recording class
sessions.
If you are having trouble understanding lectures, textbook material, or assignments, and feel you are behind in understanding the course topics, please ask questions in class, see me after class and at my office hours, talk to your recitation section TA, and/or send email to me ASAP. Do not be embarrassed by feeling you are not grasping basic material. Everyone struggles with some aspect of every subject. Study groups with fellow students are often very helpful as well. College can be demanding whether you are a freshman or an upperclassman and time management is very important, and getting behind in a course makes time management more difficult. For these reasons, consider making use of the Academic Transitions, Center for Academic Success, Disability Support Services and other offices at Lehigh’s Division of Student Affairs. See https://studentaffairs.lehigh.edu/. In addition, your mental health should not be neglected. There are university counseling services that can help in managing stress and any life issues that you find overwhelming. In particular, Student Affairs has a Counseling & Psychological Services (UCPS) office with staff members who are trained and available to assist all students.