Introduction

The course prepares students to employ essential ideas and reasoning of applied statistics. Each part of the course will cover theoretical concepts and test the student’s understanding of developing statistical models. The course is designed to familiarize students with statistical software needed for analysis of the data. Financial applications are emphasized but the course may also serve areas of science and engineering where statistical concepts are needed. The course provides students with a solid foundation for solving empirical problems with the ability to summarize and calibrate observed multivariate data.

Course Description

Prerequisites
A sound understanding of probability gathered through an undergraduate class such as MA222 or QF 212 or equivalent. FE540 is a co-requisite if such knowledge has gaps. Students have to know to program using R. Please consider taking FE515 if R is an issue.

Course Outcome

This course will allow the students to:

1. Understand and summarize complex data sets through graphs and numerical measures.
2. Calculate estimates of parameters using fundamental statistical methods.
3. Measure the “goodness” of an estimator by computing confidence intervals.
4. Apply statistical tests to experimental observations.
5. Estimate and calibrate parameters of mathematical models using real data.
6. Study relationships between two or more random variables.
7. Be prepared for more advanced applied statistical courses.

Textbook

• D. Moore, G. McCabe and B. Craig, Introduction to the practice of Statistics, 10th edition, W.H. Freeman and Co, 2021
• Michael Kutner, Christopher Nachtsheim, John Neter, William Li: Applied Linear Statistical Models, McGraw-Hill/Irwin, 2013
• Peter Daalgard, Introductory Statistics with R,Springer; 2002. Corr. 3d printing edition January 9, 2004.
• Ionut Florescu and Ciprian Tudor, Handbook of Probability, Wiley, 2013 (for reference)
• Ionut Florescu Probability and Stochastic Processes, Wiley, 2014 (for reference)
• John C. Hull, Options, Futures and Other Derivatives, Prentice Hall, 2022, 11th edition,(for reference - you may get any of the older editions).

Additional References

1. Peter Daalgard. Introductory Statistics with R. Springer, 2004.
2. Ionut Florescu Probability and Stochastic Processes. Wiley, 2014.
3. Michael Kutner, Christopher Nachtsheim, John Neter, and William Li. Applied Linear Statistical Models. McGraw-Hill/Irwin, 2013.
4. D. Moore, G. McCabe, and B. Craig. Introduction to the practice of Statistics. W.H. Freeman and Co, 11 edition, 2021.

Grading Policies

The final grade will be determined upon the student’s performance in the Homework and Exams. We will have several assignments (each weighted equally toward your final grade) during the course of the semester. Please use the .pdf format for submitting assignment files. You should be able to transform any document into a pdf file. You can use Adobe Acrobat - should be free to Stevens students as far as I know (please call the students help desk), or a simple alternative is to save as a pdf file from the print menu. I personally write all my documents in LATEX. You can also use https://www.overleaf.com/project to produce collaborative documents.
In this course, in addition to assignments and exams the students will be required to design and produce a project which contains a data component and is applicable to their primary field of study. Any project topic needs to be approved by the instructor and requires applying statistical methods learned in this course. This project (written report and the presentations) will count as the final exam in this course. As such it is an important component of the course that should not be taken lightly.
Late assignments will not be accepted under any circumstances without prior notice and permission of the instructor. If outside circumstances are affecting your ability to perform in the course, you must contact me before you fall behind.

Generally the grade distribution follows the following percentages.

Grade distribution

• Assignments 30%
• Midterm 25%
• Final Presentation 10%
• Final Project 30%
• Attendance, class participation 5%