28 courses with the subject STAT, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
STAT 210Elementary Statistics I3
Presentation of data, frequency distributions, descriptive statistics, elementary concepts of probability, random variables, binomial and normal distributions, sampling procedures, student’s t-test, linear correlation. Interpretation of data. This course cannot be taken as a mathematics elective by mathematics majors. Prerequisites: MATH 113 or MATH 120 or MATH 131.
Sampling of attributes, comparison of several samples, one-way analysis of variance, sign test, median test, Kruskal- Wallis test and test for randomness, simple regression analysis and Statistical software.. Prerequisite: STAT 210 or equivalent.
Basic probability rules, conditional probability, independence, B ayes’ theorem, discrete and continuous probability distributions, probability density functions, binomial, Poisson, hypergeometric, negative binomial, geometric and normal distributions. Prerequisite: MATH 261.
Introduction to the concepts of probability, random variables, estimation, hypothesis testing, regression, and analysis of variance with emphasis on application. Prerequisites: MATH 261.
Mathematical derivations, computational formulas, and applications and interpretations associated with the techniques of probability theory and elementary statistical inference will be emphasized. Moment- generating functions, basic sampling distribution theory, t and chi-square distributions, one-sample estimation and tests of hypotheses. Prerequisites: MATH 360; STAT 330 or STAT 340.
Random, stratified, systematic and cluster sampling, ratio and regression estimates, estimation of sample size, sampling methods in social, economic and biological surveys, sources of error in surveys. Prerequisite: STAT 380.
Theory, methodology, and practical applications of analysis of variance (ANOVA). Topics will include: one-factor and two-factor ANOVA; multiple comparisons; two-factor and three-factor balanced factorial designs with interactions; random, fixed and mixed-effect models; contrasts and confounding; and the regression approach to ANOVA. Prerequisite: STAT 380.
Research applications for social sciences, natural sciences, agriculture and education. Basic probability concepts, point and interval estimates, significance test for mean and proportion, two sample inferences, linear regression, analysis of variance, nonparametric statistics. Uses of statistical software is emphasized. Prerequisite: STAT 380.
Statistical techniques for the treatment of multiple samples. Joint discrete and continuous probability distributions, conditional and marginal distributions, covariance, independent random variables, t-distribution, one sample estimation and hypothesis testing of population parameters in the two-sample case, chi-square tests, and simple linear regression and correlation. Prerequisite: STAT 380.
Statistical applied whether the relationship between the population and sample is unknown. Wilcoxon rank-sum test, Mann-Whitney U-test, sign test, Wilcoxon signed-rank test, tests for randomness, Spearman’s correlation, Kolmogorov-Smirnov statistics, Turkey’s quick test, Friedman and Cochran’s test, statistical software. Prerequisite: STAT 380.
Multivariate methods using matrix algebra and applied statistics to analyze several correlated measurements made on each experimental unit. Multivariate normal distribution, estimation and hypothesis testing in multiple regression, Hotelling’s T, one-way multivariate analysis of variance, introduction to discriminant and factor analysis, principal components and canonical correlations and statistical software. Prerequisite: STAT 410.
Probability theory applied to the study of phenomena in engineering, management science, operations research, and the natural sciences. Markov’s inequality, conditional expectation, Markov chains, Chapman-Kolmogorov equation, interarrival and waiting time distributions. Prerequisite: STAT 480.
A rigorous development and proofs of the theory of probability. Formal probability systems, conditional probability, sequences of events, independence of events, random variables, probability density and distribution functions, joint distributions, independence of random variables, functions and transformations of random variables, fundamental limit theorems. Prerequisites: At least two 400-level statistics courses or consent of the instructor.
This is a general terminal course designed primarily for graduate students enrolled in professional education research, psychology, guidance, or other behavior sciences. It is defined as an applications approach to methodology of modern research. This course will help prepare individuals to comprehend, interpret, and report statistical results for use in educational research, thesis presentation, and publication in research journals. Elementary and advanced statistical methods will be discussed. Statistical software will be used to analyze and interpret large databases occurring in real life situations.
The main techniques of statistical analysis as applied in the biological sciences are discussed. This course is of interest to students in social sciences as well. Probability, Binomial, Poisson and normal distributions, estimation and hypothesis testing, Analysis of variance, regression and analysis of covariance. Prerequisite: STAT 480 or equivalent.
Only for students in Mathematics Education or Science Education. Descriptive statistics, normal, binomial, t, Chi-square and F distributions. Estimation and hypothesis testing, Parametric and nonparametric tests: z-test, t-test, one-way and two-way analysis of variance, analysis of covariance, chi-square tests of goodness-of-fit and independence for categorical data, linear correlation and regression, multiple regression. Statistical results from mathematics education research journals will be studied and real data from educational sources will be analyzed using statistical software. Prerequisite: STAT 330 or equivalent.
Exploring data, planning a study, anticipating patterns and statistical inference. Course is designed to make connections between statistics topics and the teaching of statistics in elementary, middle and high school. This course does not satisfy the requirements of STAT 520.
Univariate and multivariate distribution theory; moment generating function; inequalities in statistics; order statistics; estimation theory; likelihood; sufficiency; efficiency; maximum likelihood; testing hypotheses; likelihood ratio; confidence and prediction interval; Bayesian estimation and testing; basic decision theory. Prerequisites: MATH 261, STAT 480 or equivalent.
Rank correlations, linear and monotonic regression, several related samples, balanced incomplete block design, randomization, rank transformation and goodness-of-fit tests. Prerequisite: STAT 481 or equivalent.
General linear model; fixed, random and mixed effects models; randomized block, incomplete block and Latin square designs; factorial designs; analysis of covariance. Prerequisite: STAT 480 or equivalent.
Random walks; Markov chains; Poisson processes; Wiener processes; queuing and inventory analysis; reliability theory. Prerequisites: STAT 480, STAT 490 or equivalent.
Two-way and three-way contingency tables; measures of association; log-linear, logit and hierarchical models; inferences based on multinomial, Poisson and Chi-Square distributions and residual analysis. Prerequisite: STAT 480 or equivalent.
Linear and multiple regression; analysis of residuals; variable and model selection including stepwise regression; transformations, weighting and diagnostics to correct model inadequacies. Prerequisite: STAT 480 or equivalent.
Statistical theory associated with multivariate normal distribution; Wishart and related distributions; partial and multiple correlations; Hotelling’s statistic; multivariate linear models; classification and discriminant analysis; principal components. Prerequisites: MATH 325, STAT 480 or equivalent.
Estimation, relative precision, optimum allocation and stratum sizes in stratified random sampling; quota sampling; ratio and regression estimates; systematic and cluster sampling. Prerequisite: STAT 382 or equivalent.
General linear model; fixed, random and mixed effects models; randomized block, incomplete block and Latin square designs; factorial designs; analysis of covariance. Prerequisites: Instructor’s or graduate program coordinator permission.
This course focuses on building computational abilities, inferential thinking, and practical skills for tackling core data scientific challenges, and covers predictive modeling methods, approaches and tools.
