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Milwaukee School of Engineering

Oct 24, 2017

MG 610 - The Application of Statistics

3 lecture hours 0 lab hours 3 credits
Course Description
Decision-making, planning and the presentation of information can be significantly enhanced by the intelligent use of mathematical methods or statistics. This course expands on a basic understanding of statistics used in business today with the focus being on application rather than the mathematics and theory of the methods. Statistical tools used to describe collections of data, estimate parameters, make comparisons, develop mathematical relationships or models, control processes, predict outcomes, and plan experiments are covered. Specific tools include frequency distributions, sampling, estimation, Chi-Square analysis, regression and correlation analysis, simple and multiple regression, forecasting, control charts, and process capability analysis. (prereq: none)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
• Understand what kinds of statistical tools are available, and where and how they can be applied in a business and industrial environment
• Know what statistical tools require further independent study to satisfy the requirements of other courses as well as personal and career needs

Prerequisites by Topic
• Experience in using or course(s) in basic statistical methods is strongly recommended
• Experience using Excel and software for statistical analysis would be helpful

Course Topics
• Introduction and graphical descriptive statistics management, work environment, culture and its impact on the application of statistics Statistical thinking - problems must be addressed in the context of a larger system and not as an exercise in mathematics
• Basic concepts descriptive statistics - graphical methods (1 class)
• Descriptive statistics - numerical methods central value, spread, and correlation (1 class)
• Interval estimates and sampling confidence intervals, approaches to sampling, and probability (1 class)
• Hypothesis testing, and making comparisons and inferences comparing a condition to a standard, comparing two conditions, risks and sample sizes (1 class)
• Statistical quality control variables control charts, attributes control charts, capability studies and indexes (1 class)
• Systematic collection and analysis of information for making decisions making multiple comparisons using the analysis of variance - ANOVA, Design of experiments - DOE (2 classes)
• The analysis of enumerated or counted data evaluating goodness of fit, and checking for independence (1 class)
• Studying and defining relationships between variables regression analysis and correlation analysis (1 class)
• Nonparametric methods and review sign tests, ranking tests, rank correlation, and review (1 class)
• Final Exam

Coordinator
David Schmitz