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# Applied Statistics

25 University Ave
West Chester, PA 19383

Phone: 610-436-2440
Email: Randy Reiger

## Principles of Experimental Analysis - 512

Course Objectives

The purpose of this course is to provide students with knowledge and experience in using regression and Analysis of Variance (ANOVA) techniques and other common statistical modeling methods for continuous outcome data. By the end of the course, students should feel comfortable understanding and applying commonly-used linear models to data arising from a wide variety of disciplines. This class is introductory in that all techniques are taught using scalars, instead of vectors and matrices and algebra, rather than calculus. The emphasis is more on the concepts and ideas involved in model building, data analysis, and diagnostic techniques, rather than the underlying statistical theory. Students will learn how to cohesively report results of statistical analyses, both orally and written. The class is taught using SAS. Students will learn how to program every method discussed using SAS and be able to interpret all output for SAS procedures for basic linear models.

Topics: Topics covered include straight-line regression and correlation; multiple regression models, estimation, and testing; dummy variables; analysis of covariance; regression diagnostics; model building and selection strategies; one-way ANOVA; multiple comparison techniques; two-way ANOVA; randomized blocks.  We will learn the appropriate SAS computer syntax for all methods mentioned above.

### Example Syllabus

#### STA512: Principles of Experimental Analysis

Instructor: Dr. Randall H. Rieger

Office:  328 Anderson

Office Telephone: (610) 436-2893

Office Hours: Monday 3:30-5:30, Tuesday 2:00-3:30, Wednesday 4:00-5:30

Campus Emergencies
For campus emergencies call WCU’s Department of Public Safety at (610)436-3311

##### Statistics Texts

In order to review very basic statistical topics used in this class, you may want to have an introductory (non-mathematical) statistics textbook to use for reference, such as:
Brase and Brase, Understanding Basic Statistics, 3rd ed., Houghton Mifflin, New York, 2007.
Alternatively, any introductory mathematical statistics text book will serve this purpose on a more theoretical level. The following are recommended: Hogg and Tanis, Probability and Statistical Inference, Prentice Hall or Wackerly, Mendenhall and Sheaffer, Mathematical Statistics with Applications, Duxbury

##### Supplemental Textsbooks for Introductory Linear Models

Johnson, Applied Multivariate Methods for Data Analysis

Keppel and Zedeck, Data Analysis for Research Designs

Kirk, Experimental Design

Ramsey and Shafer, Statistical Sleuth: A Course in Methods of Data Analysis

##### SAS Software

The SAS Statistical software package is available free to any West Chester University student. The software can be loaded from a DVD onto a student's PC. The method of distributing the DVD’s to interested students will be discussed on the first day of class. As part of the licensing agreement, upon graduating from or leaving WCU, the software will expire and no longer be available for the student's use. In addition, the SAS software (and other statistical packages) will be available on all computers in the Applied Statistics Laboratory.

Graduate Assitants A second year applied statistics graduate assistant will be available in the Stat Lab on Sunday evenings from 7-10 PM to provide help with STA512. Additionally, applied statistics graduate assistants will have office hours in the office of the West Chester Statistics Institute – Mitchell 408.

##### Course Objectives

The purpose of this course is to provide students with knowledge and experience in using regression and Analysis of Variance (ANOVA) techniques and other common statistical modeling methods for continuous outcome data. By the end of the course, students should feel comfortable understanding and applying commonly-used linear models to data arising from a wide variety of disciplines. This class is introductory in that all techniques are taught using scalars, instead of vectors and matrices and algebra, rather than calculus. The emphasis is more on the concepts and ideas involved in model building, data analysis, and diagnostic techniques, rather than the underlying statistical theory. Students will learn how to cohesively report results of statistical analyses, both orally and written. The class is taught using SAS. Students will learn how to program every method discussed using SAS and be able to interpret all output for SAS procedures for basic linear models.

Topics: Topics covered include straight-line regression and correlation; multiple regression models, estimation, and testing; dummy variables; analysis of covariance; regression diagnostics; model building and selection strategies; one-way ANOVA; multiple comparison techniques; two-way ANOVA.

##### Evaluation
Exam 1 - (written, in class) 20%
Exam 2 (take home) 30%
Final Exam (TBA) 25%
Homework 20%
Participation  5%
##### Class Format:
• The class format will vary depending upon the particular material being covered.  Classes will usually consist of:
1.  a lecture/presentation introducing new material
2. a "LAB" assignment to be completed in class
3. a distributed homework assignment to be worked on independently outside of class.
• EXAM II will require additional time on campus for the presentation portion of the exam.
• If any classes are canceled due to inclement weather, they will be made up, most likely at the end of the semester.
• All homework assignments, some lecture notes, and all LAB assignments will be made available prior to class on the class Blackboard page. It is the student's responsibility to print the appropriate materials and bring them to class. Please do not wait until class to print the materials
• Homework assignments will usually be collected and graded. Failure to follow instructions on assignments will be counted as incorrect answers.
• On homework and take-home examination assignments, students must work independently on these assignments, under the guidelines of the Honor Code. If you have questions about the homework, Dr. Rieger will be glad to help you.
• EXAM I will be a written, in-class examination.
• EXAM II will be a one-week computer-based, take-home examination.
• The format of the Final Exam will be announced at a later date.
• Missed exams can only be made up with a valid, verifiable, written university-approved excuse and must be made up within a week of the originally-scheduled exam.
• Late or missing homework assignments will only be excused by a valid, verifiable, written university-approved excuse.
##### Class Rules
• Students engaging in disruptive behavior will be dealt with according to university policy. Students are encouraged to consult the undergraduate catalog for details of this policy
• Academic dishonesty will not be tolerated in this class. Any cases of academic dishonesty will be dealt with according to university policy, and I will recommend the maximum possible penalty.
• Students with disabilities are encouraged to make their needs known to the instructor and the Office of Services for Students with Disabilities early in the semester.
• Please make use of office hours and other department and university resources if extra help is needed.
• In the event that I am unable to meet a class, I will a) notify you in person at a prior time or b) an official class cancellation notification on the stationary of the Department of Mathematics, signed and date stamped by the Department Secretary (BarbaraMaleno) will be posted on the classroom door. All other postings announcing the cancellation of this class are to be considered unofficial and are to be ignored.
• Students should bring a storage device to each class to save any relevant lab work.
##### Class Philosophy
• This should be a fun class where you will learn valuable skills. However, in order to truly master the art of effectively building appropriate statistical models you must give yourselves time to "play around" with assignments. If you wait until the night before class to do your work, it will be hard to be successful in this class.
• Many problems in this class will have many solutions. The best solutions are the ones that you can confidently justify based on statistical arguments. If you do not give yourselves appropriate time to think through and double-check your initial answers, you will often later find that there was in fact a better solution.
• Students are encouraged to give me feedback about the class. If you have any suggestions about any aspect of the class, please let me know in person or via e-mail or a note in my box. In order to make changes, I need to know what you are thinking!
• “The fastest way to achieve success is to increase your rate of failure.” Sometimes more can be learned by struggling through a problem and allowing yourselves several iterations at solving it. Please have the confidence and resolve to not give up on a problem or idea until giving it maximum effort. I guarantee that all of you will have moments of great frustration while trying to solve a problem. But, learning to persevere and think critically under stress is one of the skills I hope to develop in this course.
• Do not hesitate to ask appropriate questions in class or in office hours. Unlike STA511, the topics in this class lend themselves to in-class discussion and questions.
•  Like STA511, this class will give you important skills. You should see the immediate relevance of every topic we cover. If you do not, please ask why not!
##### Assignments

Notes: Assignments and (especially exams) are subject to revision and re-scheduling.

Class Topics Chapters
1

Introduction / Class Overview

Review of Basic Statistical Concepts

2 & 3

2

Introduction to Regression Analysis I

4 & 5

3 (Jan 30)

Introduction to Regression Analysis II

The Correlation Coefficient

5 & 6

4

ANOVA Tables

Multiple Regression I

7 & 8
5

Multiple Regression II

Hypothesis Testing in Multiple Regression

8 & 9
6 Partial and Multiple Regression 10
7 EXAM #1
8

Interaction

Regression Diagnostics I

11 & 12
9

Regression Diagnostics II

Polynomial Regression

12 & 13

10

Dummy Variables

Selecting the Best Regression Equation

14 & 16
11

Review

EXAM #2 Distributed

Analysis of Covariance

15

12

One-Way ANOVA

17

13 Two-Way ANOVA I 18 & 19
14 Two-Way ANOVA II 19 & 20

There will be no class on March 12.

Final Exam: week of May 5