ProfCharlton explains the Design of Experiments (DOE_ANOVA_REGRESSION): An Introduction

#Lexmark
#MapuaUniversity
#DefenceUniversity
#UniversityoftheEast
#UECALOOCAN

This video tackles the very basic foundation of the Design of Experiments, a powerful tool in Statistics used in Research and Development (R& D) , Big Data , Data Gathering and Analysis, Productivity improvement and Optimization and many more including Stock Regression and in Product Design and Development. I am personally using Design of Experiments and Taguchi Methods of Robust Engineering Design in IBM-Lexmark Corporation in Kentucky,USA.

This video of classical DOE identifies the control variables that must be held constant to prevent external factors that might affect the over all results of the experimentation
Design of experiments (DOE) is defined as a branch of applied statistics that deals with planning, conducting, analyzing, and interpreting controlled tests to evaluate the factors that control the value of a parameter or group of parameters. DOE is a powerful data collection and analysis tool that can be used in a variety of experimental situations.

It allows for multiple input factors to be manipulated, determining their effect on a desired output (response). By manipulating multiple inputs at the same time, DOE can identify important interactions that may be missed when experimenting with one factor at a time. All possible combinations can be investigated (full factorial) or only a portion of the possible combinations (fractional factorial).

A strategically planned and executed experiment may provide a great deal of information about the effect on a response variable due to one or more factors. Many experiments involve holding certain factors constant and altering the levels of another variable. This "one factor at a time" (OFAT) approach to process knowledge is, however, inefficient when compared with changing factor levels simultaneously.

Many of the current statistical approaches to designed experiments originate from the work of R. A. Fisher in the early part of the 20th century. Fisher demonstrated how taking the time to seriously consider the design and execution of an experiment before trying it helped avoid frequently encountered problems in analysis. Key concepts in creating a designed experiment include blocking, randomization, and replication.

Blocking: When randomizing a factor is impossible or too costly, blocking lets you restrict randomization by carrying out all of the trials with one setting of the factor and then all the trials with the other setting.
Randomization: Refers to the order in which the trials of an experiment are performed. A randomized sequence helps eliminate effects of unknown or uncontrolled variables.
#Lexmark
#MapuaUniversity
#DefenceUniversity

[leenakokko]

Students follow your teacher he is so dedicated

[dr.adolfhimmler]

Du bist sehr gut in Unterricht Herr ProfCharlton. Ausgezeichnet..:) Prima..

[ProfCharltonAcademy]

Thanks all for the likes, and nice comments..

[SheerStitchery]

Ooh! I loved Stats in Uni! This is a great refresher.

[leenakokko]

I dont know tecnical vocabulary but I support is make likes and comments. My mother tongue is Finnish and Swedish is second oficial language. I hope you best

[ProfCharltonAcademy]

*_Summary Presentation for the DESIGN OF EXPERIMENTS.._*

[lagalagnaprobinsyanotv]

Nice content poh madami k matutulungan nian thanks poh ikaw n bahala mag balik

[jayferdbaja]

Try to use tripod po next time, sir. But overall, all goods po.

[renatocorsame]

masyado malikot ang nag video masakit sa mata