IES 612 - Environmental Analysis and Modeling
STA 473/573 - Applied Multiple Regression
STA 476/576 - Experimental Design

Spring 2006

Meeting Times: 800- 850 MT RF
Meeting Location: 201 Bachelor Hall  

Prerequisites: basic course in statistics (e.g. STA 671); willingness to work hard and ask questions when confused; desire to understand more about how statistical models are used to analyze experimental and observational studies.
Instructor: Dr. John Bailer
E-mail address: baileraj@muohio.edu
URL: http://www.users.muohio.edu/baileraj
Office (phone):

292 Bachelor (9-3538) *

369C Upham (9-2648)

Office Hours:

* 900-1000 Monday, Tuesday, Thursday, Friday

(other times by appointment - don't be shy!)

Teaching Assistant/Grader TBA

Purpose of Course:
  1. Introduce regression models and accompanying assumptions. (STA 473/573) - through 2/10
  2. Introduce experimental designs and their associated analyses. (STA 476/576) - through 3/24
  3. Introduce methods of probability sampling.
  4. Introduce mathematical models
Course Objectives:
  1. Identify and correctly analyze various regression models and experimental designs using SAS.
  2. Develop a sense for the most appropriate sampling method for addressing a particular problem.
  3. Develop a sense for what issues are important in the construction and validation of mathematical models.

Texts:
regression and experimental design (OL) Ott, R.L. and Longnecker, M. (2001) Statistical Methods and Data Analysis, 5th Ed., Pacific Grove, CA,  Duxbury.
sampling Course notes - based in part on sampling chapter from Piegorsch, W.W. and Bailer, A.J. (2005) Analyzing Environmental Data. John Wiley & Sons: West Sussex , England (also used for reg./exp. design)
modeling

(Optional) Neuwirth E and Arganbright D (2004) Mathematical Modeling with Microsoft Excel. Thomson - Brooks/Cole: Belmont, CA

Course notes - based in part on Giordano, F.R., Weir, M.D. and Fox, W.P. (2003) A First Course in Mathematical Modeling, 3rd Ed., Pacific Grove, CA,  Duxbury

Grading:           Straight 90-80-70-60 split for A,B,C,D, respect. (+/- used on boundaries)

 (for IES 612)

Item

contribution to grade

Homework

1/3

Exam 1 (following reg.)

1/3

Exam 2 (following exp. design)

1/3

 (for STA 473/576 or STA 476/576)

Item

Contribution to grade

Homeworka

1/2

Exama,b

1/2

aAdditionally, students in STA 573/576 may be required to work additional "grad-only" problems on homework or exams. 

bExam will be handed out during the last regularly scheduled class period and will be due Monday of the next week.

 Comments:            Exams will be take-home exams.  The first exam will focus on regression analysis, and the second exam will focus on experimental design.

Tentative course outline

IES 612 Part I and STA 473/573

 

Week

Day

Comments

Reading

Expanded topic list

1

1/09

First day of classes - Intro.

OL 11.1

Linear regression; model; error terms

 

1/10

 

OL 11.2

Least squares; Residuals, leverage, outliers, estimating s

 

1/12

 

 

 

 

1/13

Inference

OL 11.3

b1 and b0 inference; General linear test

2

1/16

HOLIDAY, no class

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1/17

Diagnostics

 

Remedial measures; transformations

 

1/19

 

OL 11.4

Mean response and new response inference

 

1/20

 

OL 11.7

Correlation and r2

3

1/23

Multiple Reg.

OL 12.1

Intro, Model, interpretation, dummy/indicators

 

1/24

 

OL 12.2

GLM

 

1/26

 

OL 12.3,12.4

estimation, inference

 

1/27

 

 

Extra SS; collinearity; VIF

4

1/30

 

OL 12.5

Testing subsets

 

1/31

 

OL 12.6

Forecasting/prediction

 

2/02

 

OL 12.7

OL 16

Slopes of several reg. Lines (connection to ANCOVA)

 

2/03

 

OL 12.8

Logistic reg.

5

2/06

Selecting variables

OL 13.1

All possible reg.; PRESS; Cp; backward elimination; forward selection; stepwise

 

2/07

Model formulation/ assumptions

OL 13.2, 13.3, 13.4

Linearity; transformation; Plots

 

2/09

TOPICS

 

Nonlinear regression/ Logistic Regression/  time series

 

2/10

 

 

 

End of REGRESSION

IES 612 Part II and STA 476/576

 

6

2/13

Experimental Design

OL 8.1-8.2

One-way AOV vocab, t-test analog, F-test, SS/AOV table

 

2/14

Single factor anova models

OL 8.3

CRD

 

2/16

 

OL 8.4

AOV conditions; residual analysis

 

2/17

 

 

 

7

2/20

HOLIDAY, no class

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2/21

 

OL 8.5, OL 8.6

Transformation, Kruskal-Wallis

 

2/23

Multiple comparisons

OL 9.1, OL 9.2

Contrasts, SSC

 

2/24

 

OL 9.3

Error rates - individual, per comparison, Bonferroni

8 

2/27

MCA

OL 9.4, OL 9.5, OL 9.6

Fisher, Tukey, SNK

2/28

MCC+

OL 9.7, OL 9.8

 

 

3/02

Design concepts

OL 14.1-14.3

Intro, study types, vocab

 

3/03

 

 

Oneway, factorial, control, experimental unit, replication

 9

3/06

 

OL 14.5-14.6

Randomization, Replication (study size)

3/07

AOV for standard designs

OL 15.1-15.7

Oneway CRD

 

3/09

 

 

RCBD, LS?, Factorial treatment in CRD and RCBD

 

3/10

 Topics

 OL 17/18

 Fixed/random effects, repeated measures

10

3/13

SPRING BREAK

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3/14

SPRING BREAK

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3/16

SPRING BREAK

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3/17

SPRING BREAK

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11

3/20

 

 

 

 

3/21

 

 

 

 

3/23

 

 

 

 

3/24

 

 

 

End of EXPERIMENTAL DESIGN

IES 612 Part III

 

12

3/27

Sampling - ENAR? Pinch hitter?

Course notes

SRS

 

3/28

ENAR? Pinch hitter?

 

 

 

3/30

 

 

Stratified RS, Cluster Sampling

 

3/31

 

 

 

13

4/03

 

 

Systematic samples

 

4/04

 

 

 

 

4/06

 

 

Estimating Wildlife Population Size

 

4/07

 

 

 

14

4/10

 Modeling

Course notes

Vocabulary; model/variable types

 

4/11

 

 

 

 

4/13

 

 

 

 

4/14

 

 

 

15

4/17

Monte Carlo models

Course notes

 

 

4/18

 

 

 

 

4/20

 

 

 

 

4/21

Deterministic models

Course notes

 

16

4/24

 

 

 

 

4/25