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.

COURSE OUTLINE:

• 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.