Applied Statistics in Research and Development | AIChE

Applied Statistics in Research and Development


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Applied statistics expert David Clough begins by bringing you up-to-speed on classical statistical methods using Excel and then introducing the R package with just-in-time applications. You’ll uncover smoothing methods unknown to many engineers and scientists and how to apply them to examine trends in data.  You’ll learn how to develop factorial and fractional factorial designs for your experiments and apply the analysis of variance (ANOVA) to the results. In addition, you’ll see how to estimate parameters in algebraic and differential equation models and carry out both linear and nonlinear regression and generate mixture designs.

This course is highly interactive with frequent opportunities to participate in hands-on exercises. You’ll gain experience in live regression calculations, creating custom optimal designs, stepwise regression, and more. By the end of the course, you’ll have a firm grasp of the new concepts presented and the confidence to effectively implement them on your own.


  • Carry out conventional single- and two-sample statistical analysis with Excel and R.
  • Apply the loess and cubic-spline smoothing methods to noisy data series using Excel and R.
  • Apply the analysis of variance (ANOVA) to the results of factorial experiments in R & D activities.
  • Develop factorial and fractional designs for R & D experimental campaigns and carry out analysis of results using Excel and R.
  • Expand factorial designs to central composite design in sequential experimentation and analyze experimental results via response surface models.
  • Implement linear regression analysis using Excel’s array formulas, the Analysis, Toolpak add-in, and R
  • Carry out linear regression to estimate parameters in algebraic and differential equation models based on data from experimental campaigns.
  • Chemical Engineers
  • Research Engineers
  • Research Scientists
  • Lecture 1 Statistical Review and Excel
  • Lecture 2 Statistical Review, Excel and R
  • Lecture 3 Statistical Review, Comparisons, ANOVA, Regression Analysis
  • Lecture 4 Smoothing of Data ‐ Loess and Splines
  • Lecture 5 General Factorial Experiments, Analysis of Variance
  • Lecture 6 Blocking Effects and Designs
  • Lecture 7 2‐level Factorial Designs ‐ Screening of Factors
  • Lecture 8 Fractional Factorial Designs
  • Lecture 9 Central Composite Designs and Response Surface Modeling
  • Lecture 10 Mixture Experiment Designs, Regression Diagnostics, Polynomial Regression
  • Lecture 11 Nonlinear Regression ‐ Fundamental, Algebraic Models
  • Lecture 12 Nonlinear Regression ‐ Differential Equations Models

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