Fall 2009, Prof. D. Cline  (Sections 600, 700 & 720)

Welcome to my Statistics 601 home page!  This site has been updated for the Fall 2009 term.  Once the semester begins, you will find assignments, notes and examples for the course on the Statistics department DoStat server.  (See below. This is new for Fall 2009.)

STAT 601 is designed to introduce students to statistical methods and thinking, particularly in ways useful to research in engineering and science. No prior statistical experience is required. Emphasis is placed on modeling data, choosing the analysis appropriate for the data, understanding sampling error and how to manage it, knowing the assumptions made and checking their validity. The methods covered in depth include multiple regression and analysis of variance.

For comments or questions, e-mail me (dcline@stat.tamu.edu) or contact the TAMU Statistics Department.

Topic		Section
I. Modeling Data
	A. Data and Model, Randomness and Inference	1.1–1.4
	B. Types of Data	3.1, 4.1
	C. Displaying Data: crosstabulation, histogram, scatterplot	6.2, 6.3, 6.5, 11.1
	D. Describing Data: mean, standard deviation, quantiles, boxplot	6.1, 6.3, 6.4
	E. Basic Probability: interpretations, rules	2.1–2.3
	F. Probability Models: density, mean, variance	2.8, 3.1–3.4, 4.1–4.4
	G. Special Distributions: normal, gamma, Weibull, lognormal	4.5, 4.6, 4.8–4.11
	H. Density Estimates and Quantile Plots	6.3, 6.6
	I. Random Sampling and Simulation
II. Statistical Inference
	A. Estimation of Parameters: mean, standard deviation	7.1, 7.4
	B. Sampling Behavior: bias, standard error, normal approximation	7.2, 7.3, 8.3
	C. Confidence Intervals: interval for a mean, confidence	8.1–8.3
	D. Hypothesis Testing: t-test, hypotheses, p-value, Type I and II errors	9.1–9.3, 9.6
	E. Designing a Sample: sample size determination	8.2, 9.2, 9.3
	F. Inference for Other Parameters	8.4, 9.4, 10.1–10.5, 10.7
III. Correlation and Regression
	A. Joint Distributions: correlation and independence	5.1–5.6, 11.8
	B. Conditional Expectation and Regression	5.1, 5.2, 11.1
	C. Estimating Correlation	11.8
	D. Straight Line Regression: least squares fit, inference	11.1–11.6, 11.9
	E. Residuals Diagnostics	11.7
	F. Multiple Linear Regression: estimation, prediction	12.1–12.4
	G. Outliers and Influential Values: Studentized residuals, Cook's D	12.5, 12.6
	H. Model Fitting and Selection: hypothesis tests, model selection	12.2, 12.6
IV. Design and Analysis of Experiments
	A. Comparing Group Means: analysis of variance, Tukey's test	13.1, 13.2
	B. Handling Assumptions: Brown-Forsythe test, Kruskall-Wallis test	13.2, 15.4, 15.5
	C. Contrasts
	D. Random Effects Model	13.3
	E. Factorial Models: interaction	14.1–14.5
	F. Randomized Block Design	13.4
	G. General Linear Models: covariate analysis
V. Analysis of Categorical and Count Data
	A. Distributions for Counts: binomial, Poisson and multinomial	3.1–3.9, 4.7, 5.1
	B. Inference for Proportions	8.5, 9.5, 10.6
	C. Categorical Data Analysis: goodness of fit and contingency tests	9.7, 9.8
	D. Inference for Percentiles	15.2

Copyright Information

Each document provided on these web pages is copyrighted by me (Daren B.H. Cline) with all rights reserved, whether or not the document explicitly states so.  These documents may only be used for academic purposes and they may not be reproduced or sold without my permission.  That means you may refer to them for other classes or for research, just as you would any book, as long as neither you nor anyone else reproduces them for sale or other distribution.  If you would like to use some of the examples for instruction, you need to first get written permission from me (Daren B.H. Cline, TAMU Department of Statistics, College Station TX 77845-3143).