22 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.
This introductory first course in business statistics focuses on applications of data analysis and statistics in business and economics. Topics covered include descriptive statistics and graphical presentation, probability, statistical inference, and simple regression analysis.
This second course in business statistics focuses on widely used data analysis techniques in business and economics. Topics include two sample procedures, categorical data analysis, analysis of variance, regression analysis and other statistical applications as time permits. Applications are covered through practical data analysis examples.
Covers descriptive statistics, elementary probability theory, discrete and continuous random variables and probability distributions, joint distribution functions, expected values, statistical measures, sampling distributions, and point and interval estimation.
Topics include hypothesis testing, two sample procedures, analysis of variance models, regression analysis and the use of statistical software.
Focuses on current theory and practice in Six-Sigma implementation for quality monitoring and improvement. Topics include the dynamic nature of quality, Six-Sigma implementation, and the roles of management in planning and guiding quality efforts. The fundamentals of managerial and statistical methods for quality monitoring and improvements are covered.
This course introduces students to the fundamental ideas of data mining methods, including dimension reduction, cluster, classification and regression trees, and logistic regression. The emphasis is understanding the application of methods rather than on mathematical and computational foundations. All applications are business-oriented.
The purpose of this course is to introduce the student to the fundamentals of experimental design, including the planning, conducting, and analysis of statistically designed experiments. Randomized, factorial, fractional and Plackett-Burnam designs are covered with an emphasis on business applications.
This course studies the basic principles and implementation techniques of descriptive statistics, sampling, hypothesis testing, one-way ANOVA, and regression analysis. In addition, this course will emphasize how these analytical tools can used in business decision making.
This course covers the basic principles and implementation techniques of descriptive statistics, sampling, statistical inference, analysis of variance, and regression analysis. An understanding of how these tools can support managerial decision making is emphasized.
This course covers the basic principles and implementation techniques of analysis of variance, simple and multiple regression analysis. An understanding of how these tools can support business analytics is emphasized. The course covers not just methods, but theory, too.
Covers techniques used in simple and multiple regression analysis, including residual analysis, assumption violations, variable selection techniques, correlated independent variables, qualitative independent and dependent variables, polynomial and non-linear regression, regression with time-series data and forecasting. Applications related to business decision-making will be emphasized.
An introduction to multivariate statistics that focuses on the use of statistical methods for exploring and discovering information in large business datasets. Topics will be drawn from clustering and discriminate analysis for classification, principle components analysis for data exploration and variable reduction, factor analysis for indentifying latent variables, and other traditional multivariate topics.
Datamining focuses on extracting knowledge from large datasets. This course introduces the student to several key datamining concepts including classification, prediction, data reduction, model comparison and data exploration. Software and datasets are employed to illustrate the concepts.
This course covers the current theory and practice in quality, with a focus on Six-Sigma Implementation. Topics will include the dynamic nature of quality, the roles of management in planning and guiding quality efforts, as well as the fundamentals of statistical methods for quality monitoring and improvement.
Introduces design of experiments. Covers topics including scientific approach to experimentation, completely randomized designs, randomized complete block designs, Latin square designs, factorial designs, two-factorial designs, fractional factorials, nested and split plot designs, response surfaces designs, and Taguchi methods.
Covers advanced topics in statistical process control. Covers topics including cumulative sum (CUSUM) control charts, exponentially weighted moving average (EWMA) control charts, multivariate control charts, economic design and evaluation of control charts, performance specifications, process capability and improvement, and computer applications. Usually includes several guest speakers from service and manufacturing firms.
This course introduces students to cutting-edge machine learning algorithms and their significant role in contemporary business decision making. It bridges theoretical concepts with practical applications, giving students hands-on experience in applying the principles of predictive analytics. Students will extend their statistical programming expertise while learning to build and evaluate predictive models that address real business challenges.
This course provides a comprehensive introduction to the latest time series forecasting methods. Topics such as autocorrelation, forecast accuracy, seasonality, stationarity, decomposition, time series linear models, exponential smoothing, and ARIMA models are discussed. The course provides a practical skillset to students interested in more accurately forecasting future energy usage, retail sales, crime, economic indicators, user engagement, or any data which is repeatedly measured over time. Knowledge of a statistical programming language is prerequisite.
An introduction to multivariate statistics with topics that may include but are not limited to Matrix Algebra, the Multivariate Normal Distribution, Multivariate Analysis of Variance, Tests on Covariance Matrices, Discriminant Analysis, Multivariate Regression, Canonical Correlation, Principle Component Analysis, factor Analysis, and Cluster Analysis.
This course is the sequel of STAT 924. STAT 924 discussed linear regression, PCA, EFA, CFA, cluster analysis, ANOVA, discriminant analysis, logit, canonical correlation, and MDS Using SAS. This course builds on that baseline by continuing into GLM models and then exploratory regression models.
This course will cover the traditional introductory statistics topics; descriptive statistics, probability theory, random variables, discrete and continuous probability distribution, sampling distributions, estimation, and hypothesis testing. Then we’ll move on to a more advanced topic: regression analysis.
This course provides a non-theoretical coverage of common statistics topics for students in the behavioral sciences. These may include, but are not limited to descriptive statistics, probability theory, random variables, discrete and continuous probability distributions, sampling distributions, estimation, hypothesis testing, analysis of variance, & regression. Emphasis is put on and examples are of behavioral topics.
Source: Drexel University's catalog, linked per course · table learning_unit · CourseShelf publish 59
