This Learning Support course provides corequisite support for students enrolled in STAT 1401 – Elementary Statistics. Topics will parallel topics being studied in STAT 1401 and the course will provide support for the essential skills needed to be successful in STAT 1401. Taken with STAT 1401, topics to be covered will include descriptive statistics, probability theory, confidence intervals, hypothesis testing, and other selected statistics topics.

Prerequisite(s): STAT 1401 (may be taken concurrently)

This Learning Support course provides corequisite support for students enrolled in STAT 1401 – Elementary Statistics. Topics will parallel topics being studied in STAT 1401 and the course will provide support for the essential skills needed to be successful in STAT 1401. Taken with STAT 1401, topics to be covered will include descriptive statistics, probability theory, confidence intervals, hypothesis testing, and other selected statistics topics.

Prerequisite(s): STAT 1401 (may be taken concurrently)

This Learning Support course provides corequisite support for students enrolled in STAT 1401 – Elementary Statistics. Topics will parallel topics being studied in STAT 1401 and the course will provide support for the essential skills needed to be successful in STAT 1401. Taken with STAT 1401, topics to be covered will include descriptive statistics, probability theory, confidence intervals, hypothesis testing, and other selected statistics topics.

Prerequisite(s): STAT 1401 (may be taken concurrently)

This is a non-calculus based introduction to statistics. Course content includes descriptive statistics, probability theory, confidence intervals, hypothesis testing, and other selected statistical topics.

The goal of this course is to provide students with an introduction to statistical programming for data management, analysis, and reporting, and familiarize students with practical issues related to the exploration of actual data sets. This course introduces the most commonly used features of one of several popular statistical packages, especially in examining, transforming, and analyzing data (linear regression, ANOVA, and dummy variable regression).

Prerequisite(s): BUSA 3115 with a minimum grade of C or CRJU 3107 with a minimum grade of C or DATA 1501 with a minimum grade of C or MATH 1127 with a minimum grade of C or MATH 1401 with a minimum grade of C or PSYC 2127 with a minimum grade of C or STAT 1127 with a minimum grade of C or STAT 1127H with a minimum grade of C or STAT 1401 with a minimum grade of C or STAT 1401H with a minimum grade of C

Prerequisites: STAT 3127 with a grade of C or better. Applied multivariate methods, sample correlations, multivariate date plots, eigenvalues and eigenvectors, principle components analysis, factor analysis, discriminant analysis, logistic regression methods, cluster analysis, mean vectors and variance-covariance matrices, multivariate analysis of variance, prediction models.

Prerequisite(s): STAT 3127 with a minimum grade of C

Rank tests of comparing two treatments, comparing two treatments or attributes in a population model, blocked comparisons for two treatments, paired comparisons in a population model and the one-sample problem, the comparison of more than two treatments, randomized complete blocks, tests of randomness and independence.

Prerequisite(s): STAT 3127 with a minimum grade of C

Prerequisites: STAT 3127 with a grade of C or better. Sampling distributions, two by two contingence tables, Simpson's paradox and 2?3 Tables, Goodman's full rank interaction analyzed for two way tables, further examples and extensions, conditional independence models for two-way tables, further topics.

Prerequisite(s): STAT 3127 with a minimum grade of C

Completely randomized designs, treatment comparisons, diagnosing agreement between the data and the model, experiments to study variances, factorial treatment design and applications. Appropriate statistical software will be used.

Prerequisite(s): STAT 3127 with a minimum grade of C

Prerequisite: STAT 3127 with a grade of C or better in both courses. Completely randomized designs, treatment comparisons, diagnosing agreement between the data and the model, experiments to study variances, factorial treatment design and applications. Appropriate statistical software will be used.

Prerequisite(s): STAT 3127 with a minimum grade of C

Simple and multiple regression, transformation of variables, diagnostic procedures, analysis of variance and residuals, comparison of two multiple regression models, calibration and regulation for linear regression, linear splines, subset analysis and variable selection, nonlinear regression. Appropriate statistical software will be used.

Prerequisite(s): STAT 3127 with a minimum grade of C

Prerequisite: STAT 3127 with a grade of C or better. Simple and multiple regression, transformation of variables, diagnostic procedures, analysis of variance and residuals, comparison of two multiple regression models, calibration and regulation for linear regression, linear splines, subset analysis and variable selection, nonlinear regression. Appropriate statistical software will be used.

Prerequisite(s): STAT 3127 with a minimum grade of C

Logistic and Probit analyses in problems of assay. Count data analysis. Methods of survival analysis. Analysis of contingency tables, Analysis of variance for balanced data, unbalanced data, repeated measures data, binomial data. Some additional homeworks and projects will be given.

Prerequisite(s): STAT 3127 with a minimum grade of C

Prerequisite: STAT 3127 with a grade of C or better. Logistic and Probit analyses in problems of assay. Count data analysis. Methods of survival analysis. Analysis of contingency tables, Analysis of variance for balanced data, unbalanced data, repeated measures data, binomial data.

Prerequisite(s): STAT 3127 with a minimum grade of C