Applied Statistics for Scientists and Engineers

This course provides instruction statistical methods for data analysis of applications related to the pharmaceutical, biopharmaceutical, and medical device industries.
Different statistical methods are required for each of these particular applications. Data and tolerance intervals are common tools used for setting acceptance criteria and specifications. Simple linear regression and analysis-of-covariance (ANCOVA) are used for setting expiries and conducting stability analysis studies. Two-sample hypothesis tests, analysis-of-variance (ANOVA), regression, and ANCOVA are methods used for analyzing designed experiment for process development and validation studies. Descriptive statistics (distribution, summary statistics), run charts, and probability (distributions) are used for developing process control charts and developing process capability indices.
This course provides instruction on how to apply the appropriate statistical approaches: descriptive statistics, data intervals, hypothesis testing, ANOVA, regression, ANCOVA, and model building. Once competence in each of these areas is established, industry-specific applications are presented for the participants.

Day 1 Schedule

Lecture 1:

Basic Statistics
sample versus population
descriptive statistics
describing a distribution of values
Lecture 2:

Intervals
confidence intervals
prediction intervals
tolerance intervals
Lecture 3:

Hypothesis Testing
introducing hypothesis testing
performing means tests
performing normality tests and making non-normal data normal
Lecture 4:

ANOVA
defining analysis of variance and other terminology
discussing assumptions and interpretation
interpreting hypothesis statements for ANOVA
performing one-way ANOVA
performing two-way ANOVA
Day 2 Schedule

Lecture 1:

Regression and ANCOVA
producing scatterplots and performing correlation
performing simple linear regression
performing multiple linear regression
performing ANCOVA
using model diagnostics
Lecture 2:

Applied Statistics
setting specifications
Measurement Systems Analysis (MSA) for assays
stability analysis
introduction to design of experiments (DOE)
process control and capability
presenting results