Course (Section)	STA 671 (A)
Meeting Time:	800-850 M W F
Meeting Location	102 Bachelor Hall
Prerequisites:	graduate standing or consent of instructor
Professor	Dr. John Bailer
E-mail:	ajbailer@muohio.edu
URL:	http://www.muohio.edu/~ajbailer
Office (phone)	292 Bachelor Hall (529-3538)
Office Hours	10 - 11 M, W, F (other hours by appointment)

Course Purpose:

A. Gain Skills in Statistical Reasoning

Introduce the student to the basic principles of probability and statistical theory in order to:

1. Translate real world experimental concerns into statistical concerns.
2. Select the appropriate statistical procedures to address these statistical concerns.
3. Apply the statistical procedures to available data.
4. Determine correct statistical conclusions.
5. Translate these statistical conclusions into experimental conclusions.
   

B. Gain skills in the use of the computer as a data analytic tool

Course Objectives:

1. Provide the statistical foundation to be an informed consumer of research in the environmental sciences which includes the ability to read basic statistics in articles and popular literature.
2. Provide the background to know which statistical method is appropriate for a given data analysis problem.
3. Develop the link between research methods in the environmental sciences and the practice of statistics.
4. Enhance skills in written presentation of quantitative information.

Text:

D. S. Moore and G. P. McCabe (MM) (1993).Introduction to the Practice of Statistics, 2nd edition. Freeman. [REQUIRED]

Grading:

Straight 90-80-70-60 split for A,B,C,D, respect. (+/- for borderline cases)

item contribution to letter grade

Exam 1	25%
Exam 2	25%
Homework	15%
Project	10%
Final	25%

Grading and Exam comments:

1. Homework must be in my mailbox by 3 p.m. on the assigned due date in order to be considered.
2. Midterm exams will be given on weeks 6, and 11 (±1 week) of the semester.
3. Midterm exams will (most likely) be out of class exams.
4. Final exam will be CUMULATIVE. Additionally, the final exam will be an out of class (i.e. take home) exam that will be handed out during the last regularly scheduled class period and will be due Monday of Finals week.
   

Project comments:

Details attached.

Attendance Policy:

Do not miss class. Lecture and classroom activities are intended to complement the text and out-of-class activities.

Exam Make-up Policy:

There will be NO make-up exams. If you miss an exam during the semester due to illness or for some other excused reason, then the contribution of this grade to your final grade will be transferred to the Final exam. For example, if you miss Exam 1, then your final exam will now contribute 40% to your course grade.

Dates of interest:

Sept. 1	LABOR DAY, no classes
Sept. 2	Monday/Tuesday class exchange day (Monday classes meet).
Sept. 16	Last day to drop a class without a grade (W).
Sept. 22	Project proposal due
Oct. 7	Last day to drop a course with a grade of W
Oct. 10	Mid-term break, no classes
Oct. 20	Project proposal revision and methods enhancement due.
Nov. 24.	Project final report due
Nov. 26-28	Thanksgiving break
Dec. 12	Last day of classes

Topic

Organizing & Describing Data

Distributions (MM 1.1-1.3)

• measurement, variation, stemplots, histograms,
• measuring center and spread (traditional & resistant),
• normal distributions
  

Relationships (MM 2.1-2.6)

• scatterplots, regression, correlation, categorical assoc., causation
  

Producing Data (MM 3.1-3.4)

• sampling, experiments, sampling design, randomization
  

Probability (MM 4.1-4.3)

• assigning probabilities, random variables, means & variances
  

Sampling Distribution (MM 5.1-5.2)

Statistical Inference

Introduction (MM 6.1-6.3)

• confidence intervals, significance testing, power, decision making
  

Inference for Distributions (MM 7.1-7.2)

• tests of means
  

Inference for Count Data (MM 8.1-8.3)

• tests of proportions and two-way tables
  

Inference for Regression (MM 9.1)

• linear regression, parameter estimation, confidence intervals & tests
  

Analysis of Variance (MM 10.1)

• one-way analysis of variance, ANOVA table