COURSE NUMBER AND TITLE: MATH 2310 Statistical Methods
CREDIT HOURS: 3
CATALOG DESCRIPTION: Further study of simple and multiple linear regression and correlation, study of experimental design, analysis of variance, analysis of covariance, and non-parametric statistics, categorical analysis and time series.
PREREQUISITE(S): MATH 2210
SUGGESTED TEXT(S): A First Course in Statistical Methods by R. Lyman Ott, Michael T. Longnecker, Cengage Learning, 2003, ISBN-13: 9780534408060.
Inferences About Population Central Values - estimation and testing the mean of a population, choosing the sample size for testing, inferences about median.
Inferences Comparing Two Population Central Values –estimation and testing the difference in means of two independent as well as dependent populations, sample size determination for testing.
Inferences About Population Variances - estimation and testing for a population variance, estimation and testing for comparing two population variances, testing for comparing more than 2 population variances.
Nonparametric Methods - Wilcoxon rank sum test, Wilcoxon signed-rank test, Kruskal-Wallis test, Friedman’s test.
Analysis of Variances - completely randomized design, model for observations in a completely randomized design, analyzing data from a completely randomized design, checking on the Analysis of Variance conditions, multiple comparisons.
Experimental Designs - completely randomized design, randomized complete block design, Latin square design, factorial treatment structure in a completely randomized design and randomized complete block design, estimation of treatment differences and comparisons of treatment means.
Categorical Data - inferences about a population proportion, the difference between two population proportions, inferences about several proportions, chi-square goodness-of-fit test, contingency tables, tests for independence and homogeneity, odds and odds ratios.
Linear Regression and Correlation - inferences about regression parameters, prediction, examining lack of fit in linear regression, model diagnostics, inverse regression problem (calibration), correlation.
Multiple Regression - inferences in multiple regression, model diagnostics, testing a subset of regression coefficients, logistic regression.