Section: A

Meeting Time: 800-850 M W F

(Additional classes may be scheduled for computer orientation)

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: 292 Bachelor (529-3538)

Office Hours: 9-10 Monday, Wednesday, Friday

other hours by appointment (don't be shy!)

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

Introduce the student to statistical computing SAS (on the DEC/IBM mainframe).

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.

Texts:

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

Course notes. Copies to be purchased at the Oxford Copy Shop & Typing Pool, 10 South Poplar. [REQUIRED]

Grading:

Straight 90-80-70-60 split for A,B,C,D, respect.

item contribution to letter grade

Exam 1 20%

Exam 2 20%

Exam 3 20%

Homework 10%

Project 10%

Final 20%

Grading and Exam comments:

0. Homework must be in my mailbox by 4 p.m. on the assigned due date in order to be considered.

1. Midterm exams will be given on weeks 5, 9, and 12 (±1 week) of the semester.

2. Midterm exams will (most likely) be out of class exams.

3. 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. 2 LABOR DAY, no classes.

Sept. 3 Monday/Tuesday class exchange day (Monday classes meet).

Sept. 17 Last day to drop a class without a grade (W).

Sept. 20 Project proposal due.

Oct. 8 Last day to drop a course with a grade of W.

Oct. 18 Mid-term break, no classes.

Oct. 25 Project proposal revision and methods enhancement due.

Nov. 22 Project final report due.

Nov. 27-30 Thanksgiving break.

Dec. 13 Last day of classes.

Question:

Can you can meet on Tuesdays or Thursdays at 8 if we need to make up lectures?

When can you meet for computer help sessions?

Topic

Organizing & Describing Data

(MM 1.1-1.3) Distributions

measurement, variation, stemplots, histograms,

measuring center and spread (traditional & resistant),

normal distributions

(MM 2.1-2.6) Relationships

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

(MM 6.1-6.3) Introduction

confidence intervals, significance testing, power, decision making

(MM 7.1-7.2) Inference for Distributions

tests of means

(MM 8.1-8.3) Inference for Count Data

tests of proportions and two-way tables

(MM 9.1) Inference for Regression

linear regression, parameter estimation, confidence intervals & tests

(MM 10.1) Analysis of Variance

one-way analysis of variance, ANOVA table