Revised January 4, 2006
Prerequisite: STA 2122
Terms Offered: Fall, Spring and Summer
Text: Statistics, 10th Edition, by James T. McClave and Terry Sincich.
1. STATISTICAL INFERENCE: Single Sample (Sections 7.1 - 7.4, 8.1 - 8.5)
Review hypothesis tests and confidence intervals for m and p. Define and interpret p-values.
Introduce MINITAB, SPSS, SAS or a similar package as a tool
for statistical analysis.
Use it throughout this course
for computation.
Introduce large sample inference procedures for m1 - m2 and p1 - p2; statistical inference for m1 - m2 and mD = m1- m2, for both independent samples and paired samples, using the
t-distribution; and the F-test for s21 = s 22.
The topics are completely randomized design, randomized
block design, two-way analysis of variance model with and without interaction
components, and pairwise comparison of means for each
design.
The topics include probabilistic models and the method of
least squares. Point estimates of the variance of the random error component,
the slope of the regression line, the correlation coefficient and the
coefficient of determination are given. Statistical inference procedures are
presented for the slope of the regression line, the correlation coefficient,
the mean value of Y at a given level of X and a new value of Y at a given level
of X.
The topics are the multinomial distribution and the
Chi-square goodness of fit test for one-dimensional count data. Contingency
tables for two categorical variables are used to test for the independence of
the two variables.
The topics are Wilcoxon Rank
Sum Test, Wilcoxon Signed Rank Test, Kruskal-Wallis H-Test, Friedman's Fr-Test and Spearman's
Rank Correlation Coefficient.