Statistical Analysis for Process and Product Development

Transcription

1 Statistical Analysis for Process and Product Development *** LIMITED TIME OFFER: FREE $100 AMAZON GIFT CARD! *** REGISTER TODAY! This course is designed to help scientists and engineers apply statistical methods used assist decision making in process and product development. Variability must be considered when utilizing data to arrive at conclusions. This course will cover Basic Statistics and Graphical Methods used to summarize data. You will learn how to apply Hypothesis Testing methods to determine whether groups are statistically equivalent or not with respect to key process characteristics such as process averages and variability. The use of confidence intervals when estimating key parameters will be covered. When planning studies, sample size determination is critical to ensure that study results will be meaningful. Methods to determine appropriate sample sizes for various types of problems will be covered. Finally, an introduction to Design of Experiments (DOE) is provided. DOE is an extremely efficient method to understand which variables (and interactions) affect key outcomes and allows the development of mathematical models used to optimize process and product performance. The concepts behind DOE are covered along with some effective types of screening experiments. Case studies will also be presented to illustrate the use of the methods. This highly interactive course will allow participants the opportunity to practice applying statistical methods with various data sets. The objective is to provide participants with the key tools and knowledge to be able to apply the methods effectively in their process and product development efforts. Seminar Fee Includes: Lunch AM-PM Tea/Coffee Seminar Material USB with seminar presentation Hard copy of presentation Attendance Certificate $100 Gift Cert for next seminar Course Information: Participants are requested to bring a laptop with Minitab Version 17 software installed. Learning Objectives: Effectively summarize data and communicate results with basic statistics and graphical techniques Apply Hypothesis Testing to test whether two or more groups of data are statistically equivalent or not. Estimate key process parameters with associated confidence intervals to express estimate uncertainty Determine appropriate sample sizes for estimation and hypothesis testing Understand key concepts related to Design of Experiments Apply experiments to determine cause and effect relationships and model process behaviour

2 Who will Benefit: Scientists Product and Process Engineers Quality Engineers Personnel involved in product development and validation AGENDA Day 01(8:30 AM - 4:30 PM) 8:30 9:00 AM: Registration 9:00 AM: Session Start Time Basic Statistics & Distributions Data Types Populations & Samples Central Tendency and Variation Probability Distributions The Normal Distribution Graphical Analysis Pareto Charts Run Charts Boxplots and Individual Value Plots Histograms Scatter Plots Hypothesis Testing Concepts Test Statistics, Crit. Values, p-values One and Two Sided Tests Type I and Type II Errors Estimation and Confidence Intervals 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 Equivalence Tests Hypothesis Tests for Multiple (>2) Groups Testing Means (ANOVA) Multiple Comparisons Testing Variances (Bartlett s and Levene s Test) Testing for Normality Day 02(8:30 AM - 4:30 PM) Power & Sample Size Type II Errors and Power Factors affecting Power Computing Sample Sizes Power Curves Sample Sizes for Estimation Introduction to Experimental Design What is DOE? Definitions Sequential Experimentation

3 When to use DOE Common Pitfalls in DOE A Guide to Experimentation Planning an Experiment Implementing an Experiment Analyzing an Experiment Case Studies Two Level Factorial Designs Design Matrix and Calculation Matrix Calculation of Main & Interaction Effects Interpreting Effects Using Center Points Identifying Significant Effects Determining which effects are statistically significant Analyzing Replicated and Non-replicated Designs Developing Mathematical Models Developing First Order Models Residuals /Model Validation Optimizing Responses SPEAKER Steven Wachs Principal Statistician, Integral Concepts, Inc Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control. Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Please contact the event manager Marilyn (marilyn.b.turner@nyeventslist.com ) below for: - Multiple participant discounts - Price quotations or visa invitation letters - Payment by alternate channels (PayPal, check, Western Union, wire transfers etc) - Event sponsorships NO REFUNDS ALLOWED ON REGISTRATIONS Service fees included in this listing METRICSTREAM INC - New York Events List

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