EXST 4001 - Statistical Methods

Spring 2000

Course Description: EXST 4001, Survey of applied statistics; descriptive statistics, statistical inference, analysis of variance, and linear correlation and regression.

Course Prerequisites: MATH 1020/1021 or equivalent.

Course Objectives: General objectives include the following:

Help student to recognize when statistical methods are necessary or may prove beneficial in decision making and scientific inquiry.

Familiarize student with the terminology and mathematical language used to describe basic statistical techniques.

Introduce student to several of the more "well known" statistical methods.

Demonstrate software that can be used to perform the analyses discussed.

Provide hands-on practice with statistical methods that are often employed in scientific inquiry and decision making.

Stimulate the student's general interest in statistics.

Instructor: Dr. E. Barry Moser, 149A Ag. Administration, 388-8376, bmoser@lsu.edu, http://www.stat.lsu.edu/faculty/moser/

Office Hours: TBA and by appointment

Text: Devore, J.L. 2000. Probability and Statistics for Engineering and the Sciences, 5^th edition. Duxbury, Pacific Grove, CA, 775pp.

Assignments and Grading: There will generally be weekly graded assignments and short quizzes, and a final examination. The assignments and quizzes will cover material from both the lecture and lab portions of the course. The final examination will be worth 25% of the grade and the graded assignments and quizzes will be equally weighted and compose the remainder of the grade.

Grading Scale: Letter grade assignments will be made according to the scale below.

Score	Minimum Grade
90-100	A
80-89.9	B
70-79.9	C
60-69.9	D
0-59.9	F

EXST 4001 - Statistical Methods

CONTENT OUTLINE

Chapter¹	Topic
8, 15.1	Hypotheses based upon a single sample: population mean and proportion, Z, t, and signed rank test
9, 15.2, 12.5	Two independent and paired samples procedures comparing means: Z, t, Wilcoxon rank-sum, covariance, correlation, linear combinations of random variables
10, 15.4	Single factor ANOVA, multiple comparisons, Kruskal-Wallis procedure
11	Two or more factors factorial treatment arrangements in CRD, least squares means
11.1, 15.4, 11.3	Randomized block design, Random effects models, Friedman procedure, Latin square design
12	Simple linear regression, simple linear correlation, parameter estimation
13.1-13.3	Transformations and curvilinear regression
13.4	Multiple linear regression, diagnostics, partial correlation, multiple correlation

¹Devore, J.L. 2000. Probability and Statistics for Engineering and the Sciences, 5^th edition. Duxbury, Pacific Grove, CA, 775pp.

To Top of Document