EXST 7013 - Statistical Inference II
Spring 1997

Course Description: EXST 7013 Statistical Inference II (4) F,S 3 hrs. lecture; 2 hrs. lab. Analysis of variance and experimental design; completely randomized and complete block designs; arrangements of treatments; covariance analysis; multiple and curvilinear regression techniques with introduction to factor, cluster, path, and canonical correlation analyses; emphasis on social and behavioral sciences research problems.

Course Prerequisites: EXST 7003 or equivalent.

Course Objectives:

General objectives include the following:

• Provide the basic tools and building blocks for performing data analysis using the analysis of variance, multiple regression, logistic regression, and log-linear methods
• Provide a foundation for further study in statistics including both theory and methods
• Stimulate each student's general interest in and an appreciation for statistics and its application

• Perform simple and multiple regression analysis, interpret basic and standard regression output, and perform basic model diagnostics
• Conduct basic analyses and diagnostics of and interpret the results from analyses of completely randomized designs, randomized complete block designs, latin-square designs, split-plot designs, and repeated measures designs, and write the expected mean squares for the balanced designs described above
• Perform and interpret the results of a basic analysis of covariance
• Analyze 2-way and n-way contingency tables using a log-linear model
• Peform and interpret the basic results from a logistic regression analysis
• Understand descriptions of statistical analyses in subject-area journals when ANOVA, multiple regression, analysis of covariance, or log-linear models are applied
• Recognize situations in which consultation with a statistician in desirable in designing an experiment and analyzing data

Instructor Office Hours: Monday and Wednesday 9:35a-10:30a, or by appointment.

Laboratory Instructor: TBA.

Lab Instructor Office Hours: TBA.

Text: Ott, R. L. 1993. An Introduction to Statistical Methods and Data Analysis, 4th ed. Duxbury Press, Belmont, CA.

Laboratory Manual: DiIorio, F.C. and K.A. Hardy. 1996. A Quick Start to Data Analysis with SAS. Duxbury Press, Belmont, CA.

Assignments and Grading: Grades will be based upon 2 regular exams, 2 laboratory practica (midterm and final), a weekly lab grade based upon homeworks, and a final exam. The final exam will be COMPREHENSIVE. Projects or outside work for additional credit are not permitted. You must take the exams and practica when scheduled unless you have received prior approval for alternative arrangements from the course instructor. A grade of "incomplete" can not be given for unsatisfactory work or failure to take exams when scheduled. Each exam, practicum, and the overall homework lab grade will be reported on a 100% basis. The Final Course Grade will then be computed by applying the percentages below to each of the items and summing them for the total score. Final letter grades will be assigned using the scale below. The instructor reserves the right to modify this scale by extending the lower limits.

	Instrument		Date         Percentage
	----------------	-------      ----------
	Lab assignments		Weekly		 20%
	Exam 1			Feb 24		 20%
	Practicum 1		Mar 13		 10%
	Exam 2			Mar 19		 20%
	Practicum 2		May 1		 10%
	Final			May 7		 20%
	----------------	-------      ----------
	Total					100%

	     Grade      Total Score
	     -----	-----------
		A	  >=90
		B	  80-89
		C	  70-79
		D	  60-69
		F	  <60

Topics: The topics and coverage will follow the text book closely beginning with chapter 8. Text book chapters and/or sections are indicated within square brackets while pages referenced from the laboratory text are in curly braces. Additional uses of the laboratory text will be made directly in the laboratory.

I. Review and Preparation

1. ON YOUR OWN: Basic probability; Z, t, chi-square, and F tests (1-sample and 2-samples as appropriate) [1-7]{87-110}
2. Linear combinations of random variables, expected values, variance, covariance, and correlation

II. Goodness-of-fit and Contingency Tables

1. Goodness-of-fit measures (Pearson, Neyman, Likelihood Ratio Chisquares) [8.1-8.3, 8.5-8.6]
2. 2-way contingency tables (tests of homogeneity and independence), measures of association [8.7-8.8]{155-163}

III. Regression

1. Simple linear regression and correlation (review) [9, 10.1-10.5]{133-140}
2. Matrix notation and some linear algebra [11.7]
3. The General Linear Model, curvilinear and polynomial regression, multiple regression analysis [11.1-11.5]{133-143}
4. Multisource regression and dummy variables, general linear hypothesis test [11.1, 11.6, 12.5]{143-151}
5. Variable selection, regression diagnostics [12.1-12.4]
6. Multiple correlation, partial correlation, canonical correlation, and extensions to factor analysis
7. Logistic regression on binary responses {167-174}

IV. Experimental Design

1. Completely randomized design, factorial treatment arrangements [13, 15.5]{111-121}
2. Contrasts and multiple comparisons procedures [14]{121-124}
3. Randomized complete block design, blocks as random effects, other blocked designs: latin square design and extensions [15.1-15.4, 15.6-15.7, 16.1-16.3]{124-130}
4. Random and fixed effects and mixed models, nested errors: experimental, sampling and sub-sampling errors and expected mean squares [17]
5. Repeated measures and split-plot designs, introduction to repeated measures designs where randomization is restricted in space or time (covariance structures) [17.6, 18.1-18.3]{130-132}
6. Analysis of covariance and adjusted means [15.8, 19]{143-151}

V. Analysis of Categorical (Count) Data

1. Logistic analysis with categorical predictors [8.7, 12.6]{174-179}
2. Conditional independence and three-way contingency tables with the log-linear model.

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