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School of Continuing Studies

XBUS-504 Data Analysis I: Statistics

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Course Details

The fields of statistics and probability were founded on empirical analysis of data (e.g. human height). Data scientists must possess a strong foundation in statistics and probability to uncover patterns and build models, algorithms, and simulations. This course reviews the basics of descriptive and inferential statistics, distributions, probability, and regression with a specific focus on application to real data sets.

Course Objectives

Upon successful completion of the course, students will:

  • Explain descriptive and inferential statistics
  • Compute measures of central tendency, variance, and probabilities
  • Produce and interpret meaningful and accurate summary statistics for a given data set
  • Conduct hypothesis tests and understand the difference between Type I and Type II errors
  • Develop single and multivariate regression models
  • Differentiate between correlation and causation
Wisconsin School of Business

Lean six Sigma Data Analysis

Next dates

Sep 23—25
3 days
Madison, Wisconsin, USA
USD 2495
USD 831 per day
Apr 22—24, 2020
3 days
Madison, Wisconsin, USA
USD 2495
USD 831 per day

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Prerequisite Course Required:

Before taking this course, registrants must complete Business Process Improvement Using Lean Six Sigma and Performance Metrics.

This course provides both the theoretical background and practical skills necessary to effectively apply the Six Sigma methodology in your business. It uses the powerful and proven concepts developed in statistical process control and provides you with the knowledge, tools and guidelines to apply them quickly and effectively within the DMAIC model. The focus is less on theory and more on how to apply these influential tools to conduct projects in your business. Understanding proper data collection methods and using the proper analysis tool from the myriad of possibilities will be emphasized. Data analysis techniques to uncover the root causes for process failure will be investigated, as well as how to implement lasting controls for sustained process improvement success.

How You Will Benefit

  • Differentiate between discrete and continuous data and how it affects your analysis and sample size calculations
  • Use and apply Gage R&R to validate your measurement system
  • Select the proper analysis tool for a specific situation: Pareto charts, histograms, scatter plots, normality tests, ANOVA, correlation, and regression analysis
  • Determine and understand the effects of Cp , Cpk, and other process capability metrics
  • Calculate sample size and use it in determining the scope of data collection
  • Validate and measure the effect of process improvements
  • Choose and apply the proper control tool: I-MR, Xbar, u-chart, p-chart
  • Calculate confidence intervals based on process samples


Day 1 - Essential Statistics for Process Improvement

Overview of variation using visual tools: descriptive statistics for central tendency and variation, types of data and sample sizes, normal distribution (empirical rule), standard scores (Z scores) visual tools (histograms, dot plot, box-whisker), testing for normality, normal probability plots

Day 2 - Software-Aided Application

  • Measurement system analysis and Gage R&R
  • Confidence intervals and hypothesis testing: standard error and confidence levels, confidence intervals
  • Process capability metrics: steps for determining process capability; Cp, Cpk, Pp, Ppk metrics

Day 3 - Hypothesis Testing and Control Chart Creation

  • Statistical process control (SPC) and control charts: common cause vs. special cause variation, run charts vs. control charts, selecting the appropriate control chart, characteristics of different control charts
  • Hypothesis testing: software-aided hypothesis testing, hypothesis testing tips for interpretation, decision map for hypothesis tests
  • Survey design, implementation, and analysis


Day 1 – Includes breakfast, lunch, breaks, and dinner

  • Check-In and Breakfast 7:30–8:15 a.m.
  • Course 8:15 a.m.–5 p.m.
  • Dinner 4:30–10 p.m.

Day 2 – Includes breakfast, lunch, breaks, and dinner

  • Breakfast 7:30–8:15 a.m.
  • Course 8:15 a.m.–5 p.m.
  • Dinner 4:30–10 p.m.

Day 3 – Includes breakfast, lunch, and breaks

  • Breakfast 7:30–8:15 a.m.
  • Course 8:15 a.m.–5 p.m.
  • Dinner 4:30–10 p.m.

Who should attend

Operations managers and supervisors, business analysts, process improvement teams Note: Teams will benefit from practicing the tools and techniques together.

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