FE541 Applied Statistics with Applications in Finance
Course Catalog Description
Introduction
While financial applications are emphasized, the course may also serve areas of science and engineering where statistical concepts are needed. The course is designed to familiarize students with the use of R for statistical data analysis (familiarity with programming in R is assumed. See below).
Campus  Fall  Spring  Summer 

On Campus  X  X  X 
Web Campus  X  X  X 
Instructors
Professor  Office  

David Starer 
dstarer@stevens.edu 
More Information
Course Description
Students require sound understanding of probability gathered through an undergraduate class such as MA222 or equivalent. Also students must have the ability to program in R. Please consider taking FE515 if you are not familiar with R.
Attendance
Attendance is mandatory, and there may be short pop quizzes every week, starting from the second week.
Course Outcomes
 Understand and summarize complex data sets through graphs and numerical measures.
 Calculate estimates of parameters using fundamental statistical methods.
 Measure the “goodness” of an estimator by computing confidence intervals.
 Apply statistical tests to experimental observations.
 Estimate and calibrate parameters of mathematical models using real data.
 Study relationships between two or more random variables.
 Be prepared for more advanced applied statistical courses.
Course Resources
Textbook
 Moore et al. (2017) will be our main textbook. Earlier editions (say back to the sixth edition) should be OK.
 Greene (2012) (or other editions) will be useful for classes on Inference.
 Dalgaard (2004) is a useful basic reference concentrating on the use of R in statistics.
 Florescu and Tudor (2013) is useful for Probability and Estimation Methods.
 James et al. (2013) will be useful for its chapter on Variable Selection. It is available free online from its authors: http://wwwbcf.usc.edu/~gareth/ISL/.
Additional References
Ionut Florescu and Ciprian Tudor. Handbook of Probability. Wiley, 2013.
William H. Greene. Econometric Analysis. Prentice Hall, Seventh edition, 2012.
Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer Verlag, 2013.
David Moore, George P. McCabe, and Bruce A. Craig. Introduction to the Practice of Statistics. W. H. Freeman and Co., Ninth edition, 2017.
Grading
Grading Policies
You will be required to submit four homework assignments.
All homework assignments must be submitted in R markdown (.Rmd) format, with all answers written as functions. For your information, the main markdown page is here: https://rmarkdown.rstudio.com/. A nice summary of the use of R markdown appears here: http://www.stat.cmu.edu/~cshalizi/rmarkdown/. You may wish to include mathematical expressions in your markdown code. If so, it is useful to use L A TEX, which is taught in FE505. If you wish, you may optionally submit a .pdf version of your assignment, but no other formats will be accepted.
To emphasize: submission in R markdown format is mandatory. When I grade your homework, I will automatically parse your markdown code to extract your functions. I will run your functions with test data to confirm that they work and provide the correct results.
Late assignments will not be accepted unless you inform me of your circumstances before the assignment is due, and I grant you an extension. I will only grant extensions for serious medical or compassionate reasons. You will not receive an extension just because your computer fails or the network goes down at an inconvenient time.
Examination and Project
There will be an inclass, closedbook, handwritten, midterm examination. This will test your understanding of the basic concepts. There will also be a takehome final project that will test your ability to put theory into practice.
For the project, you will work in groups of three to propose, design, and analyze a research topic that contains a significant data component and is applicable to your primary field of study. The project must use statistical methods that are taught in this course. Before you spend more than a few hours of work on your project, you must get my formal approval of your topic.
Grade distribution
Your final grade will be determined by your performance in the homework, midterm examination, project, and spot quizzes, as weighted below. However, I reserve the right to “curve” the grades; i.e., to adjust the grades such that they follow the usual distribution at Stevens.
Assignments: 40% Midterm: 10% Final Presentation: 10% Final Project: 30% Quizzes, Class Participation: 10%
Lecture Outline
Topic  Reading  

Week 1  Descriptive Graphical Measures.
Numerical Measures. 
Moore et al. (2017): Ch. 1
Moore et al. (2017): Ch. 2 
Week 2  Sampling Distributions.  Moore et al. (2017): Ch. 5 
Week 3  Introduction to Inference.  Moore et al. (2017): Ch. 6 
Week 4  Inference for Distributions.  Moore et al. (2017): Ch. 7 
Week 5  Inference for Proportions.  Moore et al. (2017): Ch. 8 
Week 6  Estimation. Methods in General.

Greene (2012): Ch. 12
Greene (2012): Ch. 13 Greene (2012): Ch. 14 Greene (2012): Ch. 16 
Week 7  Midterm Examination  
Week 8  Analysis of TwoWay (and OneWay) Tables.
Goodness of Fit Test. Independence Test. 
Moore et al. (2017): Ch. 9 
Week 9  Simple Linear Regression.
Least Squares Method. Analysis and Testing. Prediction. 
Moore et al. (2017): Ch. 10 
Week 10  Multiple Regression.
Confidence intervals. ANOVA Table, Multiple R 2 , Residuals. 
Moore et al. (2017): Ch. 11 
Week 11  Selection of Variables.
Correlation Analysis Variance Inflation Factors. Nonlinear Regression. Generalized Additive Models. 
James et al. (2013): Ch. 6 
Week 12  Analysis of Variance (ANOVA) Models.
TwoWay Analysis of Variance. Expansion to Mixture Models. Analysis of Covariance. 
Moore et al. (2017): Ch. 12
Moore et al. (2017): Ch. 13 
Week 13  Logistic regression.  Moore et al. (2017): Ch. 14 
Week 14  Bootstrap Method and Permutation Tests.
CrossValidation Methods. 
Moore et al. (2017): Ch. 16 