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    Quantitative Methods for Process Improvement (online, self-paced)

    Please allow one business day from the day of enrollment to receive access to this online, self-paced course.

    Analysis of quantitative information is important to any business problem, and especially so for Six Sigma projects. This online course gives participants the knowledge and the tools to dissect complicated business problems and provide quantitative analysis to problems instead of relying on intuition and instincts. Part of the course involves role play in which you are assigned to be a consultant to a multifaceted resort hotel business. Step by step, this multimedia program gives the learner the knowledge and tools needed to satisfy the hotel manager's demands for information and analysis. Most learners complete the program in approximately 20 to 30 hours depending on the number problems performed and previous experience with the topics discussed.

    You'll learn how to:

    • Develop a solid understanding of the basic concepts underlying quantitative analysis and business statistics
    • Strengthen your ability to frame and formulate management decision problems
    • Interpret and evaluate data that relates to business and process improvement problems
    • Use a variety of Microsoft Excel functions, charts, and tests to analyze business data

    Unit 1: Introduction to Course Learning System

    • Course philosophy
    • Introduction to story line and characters
    • Your role as advisor/consultant to the business owner
    • Navigating through the course management system
    • Accessing course resources (data files, glossary, pre-test, post-test)

    Unit 2: Descriptive Statistics

    • Working with data (graphs, interpretation)
    • Measures of “central tendency” (mean, median, mode)
    • Variability (variance, standard deviation, coeff. of variation)
    • Relationships between variables (correlation, scatter diagrams)

    Unit 3: Sampling and Estimation

    • Generating random samples
    • The normal distribution
    • The central limit theorem
    • Confidence intervals for sample means, sample proportions

    Unit 4: Hypothesis Testing

    • Hypothesis tests for single population means, proportions
    • Using P-values
    • Hypothesis tests comparing two population means, proportions

    Unit 5: Regression

    • The uses of regression
    • Calculating the regression line
    • Goodness of fit measures
    • Residual analysis
    • Coefficient significance

    Unit 6: Multiple Regression

    • Introduction to multiple regression
    • Interpretation of coefficients, R2, residuals
    • Multicollinearity
    • Lagged variables
    • Dummy variables
    Note that Units 7 and 8 are not required for course completion but provided for the student:

    Unit 7: Decision Analysis 1 (not required for course completion)

    • Introduction to probability
    • Decision trees
    • Expected monetary value (EMV)
    • Sensitivity analysis

    Unit 8: Decision Analysis 2 (not required for course completion)

    • Joint, conditional, and marginal probabilities
    • The expected value of information
    • Risk analysis

    Scott Converse

    Scott Converse is the director of project management and process improvement programs for the Wisconsin School of Business. He has developed courses for and has expertise in the areas of project management, portfolio management, technology project implementation, process improvement, Six Sigma, business statistics, data analysis, and data mining.