Basic Statistics, Hypothesis Testing, & Regression

The objective of the curriculum is to provide participants with the analytical tools and methods necessary to:

  • Describe and summarize data effectively with descriptive statistics and graphical methods
  • Correctly compare groups with respect to means, variability, and proportions by testing hypotheses
  • Estimate key statistics and quantify uncertainty (confidence intervals)
  • Characterize expected variation from sample data (tolerance intervals)
  • Determine appropriate sample sizes to achieve adequate power for hypothesis tests
  • Develop, validate, and utilize predictive models

Participants gain a solid understanding of important concepts and methods to analyze data and support effective decision making.  Many practical examples are presented to illustrate the application of technical concepts. 

Seminar Content (3 or 4 Days)

  1. Basic Statistics & Distributions
    • Data Types
    • Populations & Samples
    • Central Tendency and Variation
    • Probability Distributions
    • The Normal Distribution
    • The Central Limit Theorem
  2. Graphical Analysis
    • Pareto Charts
    • Run Charts
    • Boxplots and Individual Value Plots
    • Histograms
    • Scatter Plots
  3. Hypothesis Testing Concepts
    • Test Statistics, Crit. Values, p-values
    • One and Two Sided Tests
    • Type I and Type II Errors
    • Confidence Intervals for the Estimates
  4. Hypothesis Tests for One and Two Groups
    • Testing Means (1 sample t ,2 sample t and paired t tests)
    • Testing Variances (Chi-Square, F test)
    • Testing Proportions (overview)
    • Tolerance Intervals (Range over which x% are expected to fall with specified confidence)
  5. Hypothesis Tests for Multiple (>2) Groups
    • Testing Means (ANOVA)
    • Multiple Comparisons
    • Testing Variances (Bartlett’s and Levene’s Test)
    • Testing for Normality
    • Data Transformations
  6. Power & Sample Size
    • Type II Errors and Power
    • Factors affecting Power
    • Computing Sample Sizes
    • Power Curves
    • Sample Sizes for Estimation
  7. Relating Two Variables
    • Scatter Plots
    • Correlation Coefficient
  8. Linear Models with a Single Factor
    • Model Building Concepts
    • Simple Linear Regression
    • R Squared
    • Residual Analysis
    • Hypothesis Testing for the Model
    • Making Predictions
    • Confidence and Prediction Intervals
  9. Linear Models with Multiple Factors
    • Multiple Regression
    • Testing for Significant Explanatory Variables
    • Model Performance
    • Model Building Strategies and Tools
    • Confidence and Prediction Intervals
    • Multi-collinearity
    • Influential Data Points