Statistical Methods and Data Analysis

Course Description

This course is specifically designed to meet the analytical needs of those individuals working within FDA regulated industries. Areas of focus include: JMP basics, analysis of data for basic engineering and scientific applications including statistics, distribution analysis, capability assessment, variation analysis, comparison tests, sample size selection, hypothesis testing, confidence intervals and multiple factor modeling. The course requires 24 hours of instruction.


This course is required for all scientists, engineers and quality professionals who actively work on all aspects of discovery, product and process development where the goal is to characterize, optimize and improve product and process performance.


There are no prerequisites for this course.

Course Objectives

  1. Use data to solve engineering and scientific problems.
  2. Understand the ideas associated with sampling and data collection.
  3. Demonstrate the ability to evaluate distributions.
  4. Select appropriate sample sizes for performance evaluation.
  5. Conduct comparative tests using data.
  6. Select appropriate analysis technique based on type of data.
  7. Apply JMP to data analysis problems.

Detailed Course Outline

Section I: Introduction to JMP

Table commands
Column commands
Row commands
Subset commands
Saving Scripts, Journals and Projects

Section II: Statistics Foundations and Distribution Analysis

Measures of center and spread
Standard error and central limit theorem
Normal distribution
t distribution and confidence intervals
Test for normality
Individuals and tolerance intervals (normal)
Process capability (normal)
Nonnormal distribution fitting and process capability

Section III: Nominal X, Continuous Y

Contour plots, Components of Variance, REML and POV
Sample size for the mean and standard deviation
t test – one sample
t test – two sample
Test for differences in variances
t test – paired
One-way ANOVA and F test
Nonparametric data analysis (optional)

Section IV: Continuous X, Continuous Y

Simple linear regression, correlation
Multiple regression

Section V: Nominal X, Nominal Y

Mean and sigma for proportion defective
Sample size and statistical tests for proportion defective
Mean and sigma for defect per unit
Chi-square test for defects and proportion defective
Pareto graphs and cross tabs analysis

Section VI: Continuous X, Nominal Y and Partition

Logistic regression
Nominal logistic regression (optional)
Recursive partitioning

Section VII: Nonlinear Modeling

Nonlinear modeling, growth and EC50 determination