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Industrial Statistics

The certificate in Industrial Statistics is designed for professionals working with data from areas such as manufacturing, R&D, government, and finance and who are interested in process development and improvement.

Each certificate requires completion of four classes, as noted in the following list.

  • Regression
  • Analysis of Variance
  • Statistical Process Control
  • Industrial Design of Experiments

Each course is 15 hours, including instruction, discussions and/or chats, taken over a 5-week period. Each course may be taken individually and qualify for CEUs.

For more information, contact Greg Evershed (gmecqa@rit.edu) at (585) 475-5442.

The following additional introductory courses are available and may be necessary for individuals with limited background:

  • Basic Statistics
  • Introduction to R
  • Introduction to SAS

Regression

Brief Description: The most common method for establishing relations among different pieces of data is regression analysis. Whether you want to understand a relationship between two variables, perform a complex experiment with many factors, or make sense of data stored in a large database, regression methods are usually the natural and appropriate tool. Minitab software will be used in this course. For those interested in R language, the R code for regression will be provided.

Topics

  • General principles of statistical modeling
  • Simple linear regression
  • Multiple linear regression
  • Dangers in regression analysis and how to avoid them
  • Graphical methods in regression
  • Using Minitab software for regression
  • Case studies in the use of regression methods

Target participants: Analysts, managers, scientists, engineers, and/or professionals working with data who are interested in learning regression methods.

Prerequisites: Participants should have prior experience with analyzing data. Experience in Minitab is not required.

This course can be used for Professional Certificate in Industrial Statistics, or a customized certificate. The course is also a suggested prerequisite for individuals without prior exposure to regression, who are interested in Biostatistics or Data Mining/ Predictive Analytics options.

Analysis of Variance

Brief Description: Analysis of Variance (ANOVA) models are typically used in the presence of categorical factors as predictors or inputs into a process. You will learn how to use such models for assessing significance of factors or inputs. The ANOVA models can be used for observational data, but they also provide an important tool for dealing with data from designed experiments. Minitab software will be used in this course. For those interested in R language, the R code for ANOVA will be provided.

Topics

  • General principles of Analysis of Variance (ANOVA)
  • ANOVA models with one factor
  • Random factors
  • Graphical methods in ANOVA
  • Using Minitab software for ANOVA
  • Case studies in the use of ANOVA models

Target participants: Analysts, managers, scientists, engineers, and/or professionals working with data who are interested in learning ANOVA.

Prerequisites: Participants should have prior experience with statistical inference at the level of the 5-week course on Basic Statistics. Experience in Minitab is not required.

This course can be used for Professional Certificate in Industrial Statistics or a customized certificate.

Statistical Process Control

Brief Description: This practical course will show you how to use statistical methods for quality control. The starting point will be to distinguish between common, or chance, cause of variation and the special, or assignable, cause of variation. You will then gain in-depth understanding of the principles and practices of statistical process control and process capability. You will learn the basic skills and knowledge to analyze processes and keep them in control by using Minitab statistical software.

Topics

  • Statistical concepts related to processes
  • Variables charts for subgroups
  • Variables charts for individual observations
  • Attributes charts
  • Moving average charts
  • EWMA charts
  • CUSUM charts
  • Process capability studies
  • Process capability indexes

Target participants: Managers, engineers, and/or professionals working with processes who are interested in learning about statistical methods for quality control.

Prerequisites: Participants should have prior experience with statistical inference at the level of the 5-week course on Basic Statistics. Experience in Minitab is not required.

This course can be used for Professional Certificate in Industrial Statistics or a customized certificate.

Design of Experiments

Brief Description: While relying on observational studies is sometimes an unfortunate necessity, one should always strive for designing controlled experiments in order to understand the relationships between factors, or process inputs, and the responses, or process outputs. This course teaches how to design such controlled experiments and how to analyze the resulting data. We will start with general principles of statistical Design of Experiments (DOE) and then introduce many useful designs. We will then show examples of data from such experiments together with their analysis. Minitab software will be used in this course.

Topics

  • General principles of experimental design
  • Full factorial designs
  • Models for two factors with or without interactions
  • 2k designs
  • Fractional factorial designs
  • Using Minitab software for DOE
  • Case studies in DOE

Target participants: Analysts, managers, scientists, engineers, and/or professionals who are interested in designing and analyzing controlled experiments.

Prerequisites: Participants should have prior experience with statistical inference at the level of the 5-week course on Basic Statistics. Experience in Minitab is not required.

This course can be used for Professional Certificate in Industrial Statistics or a customized certificate.

 

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