Online Seminars

A series of online seminars has been developed to help users make the most effective use of Statgraphics. Each seminar lasts 3 hours, divided into two 90 minute sessions with an additional 30 minute break between the sessions. The first 2 seminars in the list below cover basic operation of the program and are suggested for all users. You may pick and choose amongst the other seminars according to your specific needs. It is also possible to design custom seminars by selecting individual topics from different seminars.


INTRO: Introduction to Statgraphics Centurion

Getting Started
     The Main Statgraphics Window
     Observations and Variables
     Opening a Data Source
     Analyzing the Data
     Saving the Session in a StatFolio
     Running Statistical Procedures
     Data Input Dialog Boxes
     Printing Analysis Windows
     Copying Tables and Graphs to Other Applications
     Using the StatReporter
     Using the StatGallery
     Using the StatAdvisor
     Setting System Preferences
Manipulating Data
     Creating New Variables
     Variable Transformations
     Data Generators
     Sorting Data
     Recoding Data
Exploring Data with Dynamic Graphs
     Graphics Profile Designer
     Point Labeling
     Zoom and Pan
     3D Rotation
     Using the Response Surface Explorer
     Creating Videos

BASIC: Basic Descriptive Statistics

One Sample Analysis
     Summary Statistics
     Frequency Tabulations
     Frequency Histograms
     Quantile Plot
     Normal Probability Plot
     Stem-and-Leaf Display
     Box-and-Whisker Plot
     Confidence Intervals
     Hypothesis Tests
     Outlier Identification
Distribution Fitting
     Tests for Normality
     Selecting Alternative Distributions
     Goodness-of-Fit Tests
     Probability Distribution Plots
     Tail Areas
     Critical Values
     Transforming Data to Achieve Normality
Sampling Distributions
     Generating Random Data
     Monte Carlo Simulation
Comparing Two Samples
     Comparison of Standard Deviations
     Comparison of Means
     Comparison of Medians
     Multiple Box-and-Whisker Plots
     Quantile-Quantile Plots
     Paired Sample Comparisons
     Nonparametric Tests (Sign text, Wilcoxon test)
Sample Size Determination
     One Sample Analysis
     Comparing Two Samples
Analysis of Attribute Data
     Confidence Intervals and Tests for Binomial Distributions
     Contingency Tables
     Chi-square Tests

ANOVA: Analysis of Variance and Multivariate Methods

Oneway ANOVA
     Summary Statistics
     ANOVA Table
     Means Table
     Means Plot
     Multiple Range Tests
     Variance Check
     Residual Plots
     Nonparametric Tests (Kruskal-Wallis)
     Sample Size Determination
Multifactor ANOVA
     ANOVA Table
     Means Plot
     Interaction Plots
     Multiple Comparison Procedures
General Linear Models
Multivariate Methods
     Principal Components Analysis
     Cluster Analysis
     Discriminant Analysis
     Correspondence Analysis

REGRESSION: Regression Analysis

Curve Fitting
     Fitting a Straight Line
     Plotting the Fitted Model
     Regression Statistics
     Lack-of-Fit Tests
     Selecting a Nonlinear Model
     Analyzing Residuals
Multiple Regression
     Model Specification
     Regression Analysis Table
     Stepwise Variable Selection
Analysis of Binary Data
     Logistic Regression
     Probit Analysis
Advanced Regression
     Fitting Calibration Models
     Generating All Possible Regressions
     Comparing Regression Lines
     Orthogonal Regression
     Using the General Linear Models Procedure
     Box-Cox Transformations
     Weighted Regression
     Classification and Regression Trees (CART)
     Ordinal Regression

DOE1: Design of Experiments Part 1

Constructing an Experimental Design
     Step 1: Define the Response Variables
     Step 2: Define the Experimental Factors
     Step 3: Select a Design
     Step 4: Specify a Statistical Model
     Step 5: Select Runs
     Step 6: Evaluate Design Properties
     Step 7: Save Experimental Design
Analyzing an Experimental Design
     Step 8: Fit the Statistical Model
     Step 9: Optimize the Response Variables
     Step 10: Save the Results
Augmenting Designs
     Step 11: Add Additional Runs
     Step 12: Extrapolate Beyond the Experimental Region
Screening Experiments
     Factorial Designs
     Fractional Factorial Designs
     Plackett-Burman Designs
     Taguchi Inner and Outer Arrays
     Definitive Screening Designs

DOE2: Design of Experiments Part 2

Response Surface Experiments
     Central Composite Designs
     Three-level Factorials
     Box-Behnken Designs
     Draper-Lin Designs
Mixture Experiments
     Mixture Models
     Simplex-Lattice and Simplex-Centroid Designs
     Extreme Vertices Designs
     Analyzing Mixture Experiments
Multiple Response Optimization
     Constructing Desirability Functions
     Generating Overlay Plots
D-Optimal Designs
     Generating Candidate Runs
     Selecting the Optimal Subset
     Using D-Optimal Designs to Fix Botched Experiments
Robust Parameter Designs
     Robust Operating Conditions
     Control Variables and Noise Variables
     Using Taguchi's Orthogonal Arrays
     Using Montgomery's Combined Designs
Variance Component Designs
     Nested Factors
     Estimating Variance Components
Power and Sample Size Determination

SPC1: Statistical Process Control Part 1

Summarizing Data
     Run Charts
     Multiple Variable Analysis
Basic Control Charts
     X-Bar and R Charts
     X-Bar and S Charts
     Individuals Charts
     Control Charts for Non-Normal Data
     Attributes Charts (p, np, U, C)
     Rare Event Charts (T, g)
Process Capability Analysis
     Capability Analysis for Variables
     Selecting the Proper Distribution
     Estimating DPMO
     Estimating Capability Indices
     Calculating the Sigma Quality Level
     Non-Normal Capability Indices
     Tolerance Limits
Statistical Tolerance Limits
     Normal Tolerance Limits
     Tolerance Limits for Lognormal and Weibull Distributions
     Nonparametric Limits
     Sample Size Determination
Capability Analysis for Attributes
     Defects (binomial and hypergeometric)
     Defects per Unit (Poisson and negative binomial)
Pareto Analysis
     Pareto Charts
     Cumulative Pareto Charts

SPC2: Statistical Process Control - Part 2

Gage Repeatability and Reproducibility
     Setting up a Standard Study
     Average and Range Method
     ANOVA Method
     Short Studies
     Destructive Tests
Gage Studies for Attributes
     Setting Up a Standard Study
     Estimating R&R
     Interrater Comparisons
Advanced Control Charts
     EWMA Charts
     Acceptance Control Charts
     Multivariate Control Charts
Life Tables
     Life Tables for Interval Data
     Life Tables for Failure Times
     Survivor and Hazard Functions
     Kaplan-Meier Estimates
Distribution Fitting with Censored Data
     Maximum Likelihood Estimation
     Selecting the Proper Distribution
     Weibull Analysis
Life Data Regression Analysis
     Accelerated Life Tests
     Arrhenius Plots
     Fitting Parametric Models
     Cox Proportional Hazards Models

TIME: Time Series Analysis and Forecasting

Descriptive Methods
     Time Sequence Plots
     Partial Autocorrelations
     Tests for Randomness
     Moving Averages
     Exponential Smoothers
     Nonlinear Resistant Smoothers
Seasonal Decomposition
     Classical Decomposition
          Seasonal Indices
          Irregular Component
          Seasonally Adjusted Data
     Seasonal Subseries Plot
     X-13ARIMA-SEATS Seasonal Adjustment
     User-Specified Models
          Trend Models
          Moving Averages
          Exponential Smoothers
          ARIMA Models
     Automatic Forecasting

VISUALIZE: Data Visualization

Visualizing Numeric Data
     Matrix Plot
     Bubble Chart
     Box-and-Whisker Plot
     Violin Plot
     Diamond Plot
     Radar/Spider Plot
Visualizing Categorical Data
     Barchart, Piechart and Donut Chart
     Dashboard Gage
     Tornado/Butterfly Plot
     Mosaic Plot
     Likert Plot
Visualizing Time Series Data
     Run Chart
     Waterfall Plot
     Component Line Chart
     High-Low-Close Plot
     Time Series Baseline Plot
Visualizing Statistical Models
     Surface and Contour Plots
     Heat Maps
     Trellis Plots
     Response Surface Explorer
Visualizing Spatial Data
     Creating Maps
Dynamic Data Visualization
     Population Pyramid
     Windrose Diagram
     Deviation Dashboard
     Candlestick Plot
     Dynamic Pareto Chart
     Time Series Spiral Plot
     Sunflower Plot

Sign Up for Special Offers

Seminar Facts

Instructor Dr. Neil W. Polhemus
Material provided
  • PowerPoint slides
  • PDFs with annotated notes
  • Sample data files
Class size Negotiable
Past clients A partial list of clients for whom online and on-site courses have been given is available here.
Questions Please contact us to set up a course tailored to your specific needs.