625.603.83 - Statistical Methods and Data Analysis

Applied and Computational Mathematics
Summer 2024

Description

This course introduces statistical methods that are widely used in modern applications. A balance is struck between the presentation of the mathematical foundations of concepts in probability and statistics and their appropriate use in a variety of practical contexts. Foundational topics of probability, such as probability rules, related inequalities, random variables, probability distributions, moments, and jointly distributed random variables, are followed by foundations of statistical inference, including estimation approaches and properties, hypothesis testing, and model building. Data analysis ranging from descriptive statistics to the implementation of common procedures for estimation, hypothesis testing, and model building is the focus after the foundational methodology has been covered. Software, for example R-Studio, will be leveraged to illustrate concepts through simulation and to serve as a platform for data analysis. Prerequisite(s): Multivariate calculus.

Instructor

Default placeholder image. No profile image found for Zonghui Hu.

Zonghui Hu

zhu18@jhu.edu

Course Structure

The course materials are divided into modules which can be accessed by clicking Modules on the menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.

Course Topics

Lecture titles for lectures with technical content provide a detailed guide for topics addressed in the course.

Course Goals

The intent of this course is to provide an understanding of statistical techniques and guidance on the appropriate use of methodologies. The use of probability and statistics for understanding random phenomena and analyzing data is common practice in scientific disciplines, government, and commercial enterprise. This course develops many of these methods, shows their justification, and gives practice in their application. The mathematical foundations will promote a broad understanding, but student problems focus on application. A wide variety of problem contexts will keep it interesting. Attention will also be given to statistical software to free us from computational drudgery while extending analytical capability. This class is R-centric. It is strongly recommended that you work with R or RStudio.

Course Learning Outcomes (CLOs)

Textbooks

Required

Larsen, R. J., & Marx, M. L. (2018). An introduction to mathematical statistics and its applications (6th ed.). Boston, MA: Pearson.

ISBN-13: 9780134114217

Required Software

Statistical software will be required in the course. A specific software is not absolutely required; however, instruction will be heavily R-centric. It is recommended that you secure a free copy of R or RStudio. For R travel to https://www.r-project.org/ and follow links to a CRAN for R download. Alternatively, or in addition to, you can go to https://rstudio.com/ for R with a more attractive interface and extended capabilities.

Student Coursework Requirements

This course will consist of three basic student requirements:

HW Assignments (30% of Final Grade Calculation)

HW assignments account for 30% of the grade. Assignments will generally involve solving problems assigned from the text with some additional problems from the instructor that may require statistical software. Whether the work is performed by hand or using software, the grading occurs online in Canvas. Consequently, all work by hand and all software will be arranged in order of problems assigned and scanned as a SINGLE PDF file to upload into Canvas. That pdf file should not exceed 1 MB, usually. I do not need photo quality images. I will not accept separate files, PNG files, TIFF files, R workspaces, Excel spread sheets, etc. If you want your work graded, it will be scanned at modest resolution to PDF (to keep the file size down) and uploaded to Canvas. Do not email assignments to me. Also, be aware that if you would rather prepare even the hand-worked problems electronically, for example in Word, you need to check the PDF conversion. If you do not have a scanner, there are some cell-phone apps that reliably produce PDF files. As an example, the App Store at Apple has Genius Scan. It is capable of scanning multi-page documents to a pdf and allows you a choice of resolution to help you get near the 1 MB target. Use as naming convention, Module1_LastName_FirstName.pdf.

Exam and project submissions will be prepared in the same manner as HW assignments.

Preparation and Participation (Class Discussions) (10% of Final Grade Calculation)

Each student is responsible for carefully reading all assigned material and being prepared for discussion. The majority of readings are from the course text. Additional reading may be assigned to supplement text readings.

Student participation in class discussions is imperative for a successful online class experience Lessons learned and past experiences provide the entire class with a broader perspective of the topics being discussed. All students are required to participate throughout the semester. Class participation will count as 10% toward the student's final grade. Throughout the week, each student is required to submit postings to the assigned discussion thread(s). Each student is expected to respond to the initial discussion question and to at least one of your classmates' responses (additional postings are preferred). A student's initial response and one follow-on posting must be "significant" in nature (see definition below). The goals of these discussions are to (a) thoroughly examine the topic area, (b) apply critical thinking skills, and (c) gain an appreciation for the application of the subject area. For many modules, discussion space will be provided collaborative HW and/or software efforts. This is a wonderful opportunity to learn from each other.

Definition of "significant" posting: (a) 100 words or greater, (b) properly referenced if necessary, and (c) demonstrating critical thinking skills (opinion should be separated from fact). These postings will count toward class participation.

Timeline:

Day 1 (Tuesday) - discussion topic is posted Day 1–4 (Tuesday–Friday) - initial student response is posted Day 5–7 (Saturday–Monday) - follow-on student responses are posted

Preparation and participation are evaluated by the following grading elements:

Generally, this is an opportunity to have a class discussion on topics and collaboration on some HW problems. It is an opportunity to help one another and enrich the class for one another. Everyone comes to this course with a unique work experience. The varying work contexts as examples are often the best part of the discussion.  Meeting requirements and a subjective determination of “good” earns a 90. You lose points for not meeting deadlines or for minimalistic responses. You gain points for my subjective determination of extra effort. Score is determined completely within a module. Don’t assume that a week off can be made up with a prolific contribution the following week. HW collaboration is of course not as lengthy as collaboration, but substance is required.

Examinations (30% Midterm, 30% Final; Totaling 60% of Final Grade Calculation.)

A midterm and a final exam will be given. Each will be graded on a scale of 0 to 100 and will be worth 30% of your grade. Exact dates will appear on the course outline and will be announced during the semester. Exams will be take-home. You will be allowed to have access to resources (book, notes, etc.) but work independently. 

 

Grading Policy

Assessment will be typical of a traditional in-class setting. Students will be graded on assignments performed (30%), discussion participation (10%),  a midterm exam (30%), and a final (30%). Details on how we will communicate for each of these will be posted elsewhere under course content.

Discussion assessment is based on the substance of individual posts, and whether posting requirements are met. Substantive posts meeting posting frequency requirements would earn a 90%. Excellent posts involving critical thinking or analysis will garner more credit, as will more frequent engagement in the discussion. However, merely being “chatty” in the discussion will not help your score. Please be advised that while the discussions are left open during the semester, they are time stamped. A last minute rush to contribute to weeks-old discussions will not be credited.

HW assignments will see 5 problems graded in detail. Each problem is worth 20% of the 100% score. Omitted problems reduce your score by 20% for each problem. Late HW is highly discouraged. At the instructor’s discretion, a day or two might be allowed with an email prior from the student explaining the delay. A week delay is unacceptable and will result in a 20% reduction in your score. Two weeks delay will result in a 40% deduction. HW turned in two weeks or more past the Monday due date and time will receive a zero. HW not turned in according to the specifications described above will not be accepted. Students will have one opportunity to conform to the specifications for the assignment. 

A special circumstance for discussions and HW is late entry to the class. Generally, I expect you to be working double time to catch up. Thus, a week late entry is caught up with the class by the end of week two. A two-week late entry is caught up with the class by the end of week 4. EP should not be admitting students once the course is more than two-weeks into the semester.

A grade of A indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week.

A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.