NOTICE: Many events listed here have been canceled or postponed due to the Covid-19 emergency. It is best to call ahead or check with organizer's websites to verify the status of any local event.

Change Location × San Francisco

    Recent Locations

      Applied Statistics for Scientists and Engineers in San Francisco

      • Applied Statistics for Scientists and Engineers Photo #1
      1 of 1
      March 26, 2020

      Thursday   9:00 AM - 6:00 PM (daily for 2 times)

      601 Montgomery Street #409
      San Francisco, California

      • No Performers Listed
      Applied Statistics for Scientists and Engineers

      Applied Statistics for Scientists and Engineers
      About this Event


      Throughout 21 CFR and guidance documents for the pharmaceutical, biopharmaceutical, and medical device industries, the application of statistical methods are specified for: setting validation criteria and specifications, performing measurement systems analysis (MSA), conducting stability analysis, using design of experiment (DOE) for process development and validation, developing process control charts, and determining process capability indices.

      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.

      Why should you attend:

      21 CFR and guidance documents for the pharmaceutical, biopharmaceutical, and medical device industries specify the application of statistical methods across the product quality lifecycle.

      According to the Quality System Regulation (QSR) for medical devices, "Where appropriate, each manufacturer shall establish and maintain procedures for identifying valid statistical techniques required for establishing, controlling, verifying the acceptability of process capability and product characteristics." Although there are many statistical method that may be applied to satisfy this portion of the QSR, there are some commonly accepted methods that all companies can and should be using to develop acceptance criteria, to ensure accurate and precise measurement systems, to fully characterize manufacturing processes, to monitor and control process results and to select an appropriate number of samples.

      According to both 21 CFR and guidance documents, the need for statistical methods is well established from discovery through product discontinuation. 21 CFR specifies the "the application of suitable statistical procedures" to establish both in-process and final specifications. The guidance documents necessitate the application of statistical methods for development and validation of measurement systems, process understanding using Quality by Design (QbD) principles, process validation, as well as ensuring the manufacturing process is in control and is capable.

      This course provides instruction statistical methods for data analysis of applications related to the pharmaceutical, biopharmaceutical, and medical device industries.

      Areas Covered in the Session:


      Describe and analyze the distribution of data
      Develop summary statistics
      Generate and analyze statistical intervals and hypothesis tests to make data-driven decisions
      Describe the relationship between and among two or more factors or responses
      Understand issues related to sampling and calculate appropriate sample sizes
      Use statistical intervals to setting specifications/develop acceptance criteria
      Use Measurement Systems Analysis (MSA) to estimate variance associated with: repeatability, intermediate precision, and reproducibility
      Ensure your process is in (statistical) control and capable

      Who Will Benefit:

      This seminar is designed for pharmaceutical, biopharmaceutical, and medical device professionals who are involved with product and/or process design:

      Process Scientist/Engineer
      Design Engineer
      Product Development Engineer
      Regulatory/Compliance Professional
      Design Controls Engineer
      Six Sigma Green Belt
      Six Sigma Black Belt
      Continuous Improvement Manager


      Day 1 Schedule

      Lecture 1:

      Basic Statistics

      sample versus population

      descriptive statistics

      describing a distribution of values

      Lecture 2:


      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:


      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


      Heath Rushing

      Co-founder and Principal, Adsurgo

      Heath Rushing is the cofounder of Adsurgo and author of the book Design and Analysis of Experiments by Douglas Montgomery: A Supplement for using JMP. Previously, he was the JMP and Six Sigma training manager at SAS. He led a team of nine technical professionals designing and delivering applied statistics and quality continuing education courses. He created tailored courses, applications, and long-term training plans in quality and statistics across a variety of industries to include biotech, pharmaceutical, medical device, and chemical processing. Mr. Rushing has been an invited speaker on applicability of statistics for national and international conferences. As a Quality Engineer at Amgen, he championed statistical principles in every business unit. He designed and delivered a DOE course that immediately became the company standard required at multiple sites. Additionally, he developed and implemented numerous innovative statistical methods advancing corporate risk management, process capability, and validation acceptance criteria. He won the top teaching award out of 54 instructors in the Air Force Academy math department where he taught several semesters and sections of operations research and statistics. Additionally, he designs and delivers short courses in statistics, data mining, and simulation modeling for SAS.

      Cost: $1,189 – $7,989

      Categories: Conferences & Tradeshows

      This event repeats daily for 2 times:

      Event details may change at any time, always check with the event organizer when planning to attend this event or purchase tickets.

      Hotels and Airbnbs near Applied Statistics for Scientists and Engineers. Book your stay now!