Economic-Statistics-Experiment-Design&Analysis-: 统计: 试验设计与分析: GMP(过程或系统的通用模型) + The Scientific Method(科学方法) + 5 Free Resources for Learning Experimental Design in Statistics

Economic-Statistics-Experiment-Design&Analysis-:

Textbook:

https://designexptr.org/

• Design and Analysis of Experiments, 10th Edition by Douglas C. Montgomery (Author)
  https://www.researchgate.net/publication/361342853_Design_and_Analysis_of_Experiments
• [Experimental Design and Analysis by CMU(Carnegie Mellon University)](This textbook from Carnegie Mellon University)
  This textbook from Carnegie Mellon University
  
  

The GMP/GMS(General Model of a Process or System)

Start with an example

• Every experiment design has inputs.
  Back to the cake baking example: we have our ingredients such as flour, sugar, milk, eggs, etc. Regardless of the quality of these ingredients we still want our cake to come out successfully.
• In every experiment there are inputs and in addition, there are factors (such as time of baking, temperature, geometry of the cake pan, etc.), some of which you can control and others that you can't control.
• The experimenter must think about factors that affect the outcome.
• We also talk about the output and the yield or the response to your experiment.
  For the cake, the output might be measured as texture, flavor, height, size, or flavor.
  
  

What is the Scientific Method?

Do you remember learning about this back in high school or junior high even?
What were those steps again?

• Decide what phenomenon you wish to investigate.
• Specify how you can manipulate the factor and hold all other conditions fixed, to insure that these extraneous conditions aren't influencing the response you plan to measure.
• Measure your chosen response variable at several (at least two) settings of the factor under study. If changing the factor causes the phenomenon to change, then you conclude that there is indeed a cause-and-effect relationship at work.
• How many factors are involved when you do an experiment?
  • Some say two - perhaps this is a comparative experiment?
  • Perhaps there is a treatment group and a control group? If you have a treatment group and a control group then, in this case, you probably only have one factor with two levels.
• How many of you have baked a cake?
  What are the factors involved to ensure a successful cake?
  Factors might include preheating the oven, baking time, ingredients, amount of moisture, baking temperature, etc.-- what else?
  You probably follow a recipe so there are many additional factors that control the ingredients - i.e., a mixture.
  In other words, someone did the experiment in advance! What parts of the recipe did they vary to make the recipe a success?
  Probably many factors, temperature and moisture, various ratios of ingredients,
  and presence or absence of many additives.
  Now, should one keep all the factors involved in the experiment at a constant level and just vary one to see what would happen?
  This is a strategy that works but is not very efficient.
  This is one of the concepts that we will address in this course.
  
  

Objectives

Upon completion of this lesson, you should be able to:

• understand the issues and principles of DOE(Design of Experiments),
• understand experimentation is a process,
• list the guidelines for designing experiments, and recognize the key historical figures in DOE

5 Free Resources for Learning Experimental Design in Statistics

by Vinod ChuganiPosted on August 26, 2024

Experimental design is a fundamental component of statistical analysis,
enabling researchers to plan experiments systematically to gather valid, reliable, and interpretable data.
This discipline is essential across various fields, from clinical trials to agricultural studies.
Below, you'll find a curated list of free resources, ranked from beginner to advanced, that will guide you in mastering the principles and applications of experimental design in statistics.

1. Introduction to Experimental Design Basics (Coursera)

This Coursera course provides a beginner-friendly introduction to the basics of experimental design, covering fundamental concepts like randomization, control groups, and types of experiments. Learners have the option to audit most of the content for free, which includes access to video lectures and some quizzes. For those who want additional material like certificates, graded assignments, and quizzes, an upgrade option is available. This makes it an accessible and flexible resource for those at the start of their journey in experimental design.

2. Understanding Statistics and Experimental Design

This free online book available through the OAPEN Library provides a comprehensive guide to designing experiments and analyzing data, with a focus on social sciences and healthcare research. It covers various statistical methods and experimental designs, offering both theoretical and practical perspectives. The book is accessible to readers with a basic understanding of statistics and is ideal for those looking to apply these concepts in research settings.

3. Experimental Design and Analysis (Carnegie Mellon University)

This textbook from CMU(Carnegie Mellon University) offers a comprehensive guide to experimental design and analysis, covering topics such as ANOVA, regression, and factorial experiments. It includes practical examples and exercises using SPSS. This resource is ideal for learners with some statistical background who want to apply experimental design methods using SPSS in their research or projects.

4. Experimental Design Lecture Notes (PSU(Penn State University) )

Penn State's online course provides detailed lecture notes on experimental design, covering block designs, factorial designs, and mixed models. These notes are designed for graduate students but are also accessible to those with a basic understanding of statistics. The resource is structured to provide both theoretical background and practical applications, making it a comprehensive guide for those looking to deepen their understanding of experimental design. The notes are well-illustrated with examples, helping to clarify complex concepts and techniques.

5. Design and Analysis of Experiments and Observational Studies using R

This online resource offers a practical overview of experimental design and analysis, aimed at applied researchers and industry professionals. The community-driven platform provides access to guides, best practices, and real-world applications of experimental design across various fields. Additionally, it includes useful techniques and examples in R, making it particularly beneficial for those looking to apply these methods using R programming. This resource is ideal for staying updated with the latest trends and discussions in the field.

This is the free website for Design and Analysis of Experiments and Observational Studies using R.
A hardcopy of the book can be purchased from Routledge. This book grew out of course notes for a twelve-week course (one term) on the Design of Experiments and Observational Studies in the Department of Statistical Sciences at the University of Toronto. Students are senior undergraduates and applied Masters students who have completed courses in probability, mathematical statistics, and regression analysis. The purposes of the book are to expose students to the foundations of classical experimental design and design of observational studies through the framework of causality, use real data and computational tools, such as simulation, to explore these topics.

Mastering experimental design is critical for conducting rigorous and reliable research.
The resources above cater to different levels of expertise, from beginners to advanced learners, offering a range of video tutorials, textbooks, lecture notes, and courses. These resources will help you design experiments that yield meaningful insights and drive data-driven decisions across various fields.

Posted in Resources

Vinod Chugani

An adept professional in the realms of Data Science, Machine Learning, and Artificial Intelligence, operating across various educational and content creation platforms.