625.725.81 - Theory of Statistics I

The course materials are divided into modules which can be accessed by clicking Modules on the course menu. A module will have several sections including the overview, readings, lectures, group activity, 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.

• Bayes Theorem and Limits
• Regression Analysis
• Hypothesis Testing
• Probability Families
• Data Summaries
• Multivariate Systems
• Order Statistics and Sample Generation
• Estimators
  
• Logistic Regression
• Bayesian Regression

To deepen your understanding of the mathematical foundations of statistics

• Explain the fundamentals of probability and their connections to statistical analysis.
• Describe the dominant discrete and continuous probability distributions and the parameters that define their statistical use.
• Work with limit theorems, moment expansions and the convergence properties of large samples.
• Define the basics of estimators, being able to analyze their errors and to choose the best estimator for a problem.
• Analyze the steps involved with hypothesis generation, testing and the statistical description of the results.
• Know when to choose a Bayesian approach for statistical estimation.
• Have confidence in the connections between probability and statistical inference and how it applies to real problems.
• Know how to use logistic regression, data summaries, and sufficient statistics.

• Cassella, G. and Berger, R. (2002). Statistical Inference. (2nd edition) Wadsworth, Inc., Belmont, CA.  

Library will post PDFs within copyright guidelines; otherwise, students will need to purchase. 

• Wasserman, L. (2010). All of Statistics. Springer Nature, Switzerland.  

Library will post PDFs within copyright guidelines; otherwise, students will need to purchase 

• Larsen, R.J. and Marx M.L. (2018) An Introduction to Mathematical Statistics and Its Applications (6th edition), Prentice Hall  

Library will post PDFs within copyright guidelines; otherwise, students will need to purchase. 

• Optional Reference: Rice, J.A. (2007) Mathematical Statistics and Data Analysis (3rd edition) Duxbury, Thomson Brooks/Cole, Belmont CA) 

Final grades will be determined by the following weighting:

Item	% of Grade
HW Assignments	25%
Discussion problems	25%
Midterm	25%
Final	25%

Assignments are due according to the dates posted in the Canvas course site. You may check these due dates in the Course Calendar or the Assignments in the corresponding modules.

We generally do not directly grade spelling and grammar. However, egregious violations of the rules of the English language will be noted without comment. Consistently poor performance in either spelling or grammar is taken as an indication of poor written communication ability that may detract from your grade.

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.

100–98 = A+

97–94 = A

93–90 = A−

89–87 = B+

86–83 = B

82–80 = B−

79–70 = C

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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 Student Conduct Code website.

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).

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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.