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STAT 467: INTERPRETATION OF DATA. Spring 2020

OBJECTIVES: This is a three-credit course designed to be an introduction to statistical computing and

data analysis. The students will learn

(i) Multivariate Data Analysis,

(ii) Basic Multivariate Data mining and big data,

(iii) Statistical computing with R. Students are also encouraged to develop good communication

skills by working in groups writing reports.

GRADING: Report 1 (15% of grade) (this can be a group report of 5 or less).

Report 2 (30% of grade) (this can be a group report of 5 or less).

Midterm (45%)

Class attendance, participation and homework (10% of grade).

REPORTS: There will be two reports involving analysis of practical questions using statistical data

analysis. Prepare a report following the format provided in the report instructions. The data sets will be

uploaded to the course web site shortly.

ATTENDANCE: Attendance is required as described under Grading, and will be taken each

class. It is the absentee's responsibility to gather any information or materials missed. If

you will miss a class, please use the University absence reporting website

(https://sims.rutgers.edu/ssra/) to indicate the date and reason for your absence. An email

is automatically sent to the instructors.

SYLLABUS

1. Introduction. Basic Linear Algebra (2-3 weeks Ch.1-2, Appendix B, Notes) Statistical software R.

Modern statistical computing with Building R packages, building Shiny apps.

2. Exploratory data analysis and visualization of multivariate data.

3. Theory:

3.1 Multivariate normal distribution.

3.2 Hoteling’s T2 and Wishart distributions.

3.3 Inference on the mean and covariance.

4. Canonical correlation. Principal components analysis (PCA), Factor analysis (FA). Multidimensional

Scaling

5. Pattern recognition, Discrimination and Classification Cluster Analysis.

6. Data Mining and Big Data. (3 weeks Ch. 8, Notes) Using multivariate analysis methods for variable

reduction and dimension reduction. Segmentation and subletting of large databases. Extracting

information from large datasets. Recursive Partitioning and trees.

7. Additional topics (if time permits):

Text Book

Applied Multiv.Stat.Analysis. Johnson & Witchern, Prentice Hall

Important References

1. Applied Multivariate Statistical Analysis, Wolfgang Karl Härdle, Léopold Simar Springer, 2015

2. Multivariate Analysis, Mardia, Kent, Bibby, A.P. 1979

3. Advanced R, H Wickham 2nd ed. Chapman Hall 2018

4. An Introduction to Statistical Learning: with Applications in R 2013, G James, D Witten, T Tibshirani

5. Modern Applied Statistics with S-PLUS. W.N. Venables, B.D. Ripley. Fourth Edition. Springer Verlag

2003

Tentative class schedule

Date Topics covered

Jan. 22 Syllabus: Review of course material. Basic Statistical concepts

Jan. 29 Basic Linear Algebra

Feb. 5 Data visualization, Introduction to R, R packages , R cmd , R shiny,

Feb. 12 Multivariate Statistics Theory

Feb. 19 Multivariate Statistics Theory

Feb. 26 Multivariate Statistics Theory

Mar. 4 Multivariate Statistics Theory

Mar 11 8. Canonical correlation. Principal components analysis (PCA),

Mar. 18 Spring Recess

Mar. 259. Factor analysis (FA). Multidimensional Scaling

Apr. 1 Midterm

Apr. 8 10. Unsupervised analysis. Cluster Analysis.

Apr. 15 Supervised methods. Discriminant Analysis. Classification.

Apr, 22 Data Mining: Final Project.

Apr. 29 DM: Classification, Pattern Recognition. Project III cont'd. Recursive

partitioning. More on Project III. Due Dec 14.

Disability Services

Rutgers University welcomes students with disabilities into all of the University's educational programs.

In order to receive consideration for reasonable accommodations, a student with a disability must contact

the appropriate disability services office at the campus where you are officially enrolled, participate in an

intake interview, and provide documentation: https://ods.rutgers.edu/students/documentationguidelines.

If the documentation supports your request for reasonable accommodations, your campus disability

services office will provide you with a Letter of Accommodations. Please share this letter with your

instructors and discuss the accommodations with them as early in your courses

as possible. To begin this process, please complete the Registration form on the ODS web

site at: https://ods.rutgers.edu/students/registration-form

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