Description of Course Goals and Curriculum
ORF245: Fundamentals of Statistics, is an introductory statistics and probability class designed for students in the Operations Research and Financial Engineering department, as well as other science and engineering disciplines. The first half of the course focuses on probability and important distribution functions. Topics covered before the midterm include conditional probability, random variables and methods of estimation. During the second half of the course, the material is generally centralized around the ideas of intervals, different methods of hypothesis testing, and linear regression. The course assigns weekly homework problem sets, which are composed of traditional word problems, proofs, and a coding assignment in R. R is used in the class to model different experiments, but prior knowledge of R is not needed before taking this class. Proofs, which oftentimes deal with proving and defining statistical formulas and assumptions, require a knowledge of basic calculus.
Learning From Classroom Instruction
There are two 1 hour and 20-minute lectures per week, in addition to a 50-minute precept. Lecturer Dytso moves quickly through the material but will periodically stop the class to gauge the students’ understanding. Lecture is composed of definitions and introductions of statistical assumptions and rules, as well as additional proofs and simulations using R. Lecturer Dytso tends to take selected portions from the textbook, and the textbook is a good supplementary tool for further understanding but is not required or needed to understand and grasp the material. Lecturer Dytso’s lectures are engaging and he likes to interact with the class, while precept is more chalkboard based and covers the lecture material in a more general form. Precepts are all taught from the same weekly guide, which is posted on Blackboard. In addition, Lecturer Dytso posts the lecture slides before each lecture, along with “chapter” or topic study guides that include notes on which proofs students need to know how to write out.
Learning For and From Assignments
Problem sets are weekly and are a combination of proving assumptions or rules covered in class, more traditional “find the value of” math problems, and a R code portion. The problem sets are all created by Lecturer Dytso and although the problems are derived from material covered lecture, they require students to use critical thinking skills to solve the problems. Lecturer Dytso is an amazing resource and hosts weekly office hours. During his office hours, he will not directly answer problem set questions, but instead will go through similar examples to ensure that the student understands the material. Working in groups is encouraged, as the proofs in the problem sets can be quite long.
Tests are split up into in-class lecture quizzes, which are composed of one basic question that functions as a way to check attendance, and a midterm and a final. Both the midterm and final are open note, but the tests go beyond just repeating problem set questions. Test questions include a mixture of proofs, true/false hypothetical elements, and plug in a value and report an answer questions. R is not tested. Lecturer Dytso provides a practice midterm and final before each exam, and it would be in the student’s best interest to complete these practice exams and understand the proofs that are indicated in the chapter summary guides. Another helpful study tool is to re-do the problem sets in order to practice approaching longer problems and to refresh the memory on older topics, as on the final there is one question about pre-midterm topics. In addition, the exams are relatively long, and students are actually expected to run out of time, so Lecturer Dytso tends to require students to fill out say five out of the six exam questions. A good approach is to skim the test beforehand and answer the questions in order of confidence.
The textbook covers what is introduced in lecture but is not necessary to grasp the material. However, if a student misses lecture, the book can be a good way to fill in the gaps and can be used with the lecture slides to supplement greater understanding. Lecturer Dytso’s slides are very comprehensive and he does create the course based on his own organizational system, so the textbook can be seen as more of a supplementary tool.
Dytso tries to provide students with a lot of resources, including his office hours and preceptor office hours, chapter study guides, comprehensive lecture slides, sample R code, and McGraw study hall sessions. For extra supplement, however, students are encouraged to look online for further knowledge into R, as the course does not test R coding proficiency besides in problem sets.
What Students Should Know About This Course For Purposes Of Course Selection
ORF245 is a basic introduction to probability and statistics and is often a pre-requisite for more advanced Operation Research and Financial Engineering, SML and econ classes. The introduction to R gives students a tangible skill that they can take away from the class, and students coming out of this class can easily translate this programming skill into real-life applications, such as internships or data analysis projects. There is a substantial weekly workload that consists of a problem set that includes proofs and R-coding. In addition, there is a midterm and final exam, and attendance is checked during lecture through random in-class one question quizzes. Lecturer Dytso is devoted to making sure all his students finish his class with a comprehensive fundamental knowledge of statistics and probability and is always open to meeting with students individually to talk about the course material or specific interests that might even go beyond the topics covered in class.