Experimental design Course development NSS Monday 7 Nov

Experimental design Course development Intuitive approaches Fitting equations to data (D&W) Response surface approaches Quantitative methods in … From model to design ANOVA  GLM approaches Randomization, blocking, replication Balanced to unbalanced designs CRD, CRBD, LS, GLS Coding From design to model Mixed model approach Optimal Design Criteria approach

Experimental design Course development Practitioners Statistical staff ANOVA  GLM approaches Different mathematical level Different level of practice

Experimental design Practical exercises Set of educationally perfect examples from different scientific disciplines Starting with very simple example OPDOE DROPBOX, WWW Set up standard strategy for example description in R and MS-word Data Meta-data Tabular and graphical representations of design and data Research problem and hypothesis to test Analysis Presentation of the results ….

Experimental design Practical exercises Form an R-team Data base of examples on WWW Start with what we have on OPDOE Extend

Time table, team work, responsibilities This week Workshop Cuenca Finalize before workshop of Jimma We have 2 years!

This week’s planning Discuss course outlines and strategies Example problems Standard description of examples in R Structure of the educational platform

Monday 7 Nov Week planning Mo: strategy first brainstorming Thu: examples Wed: standardized examples Turs: integrate Fri: wrap up Discuss course outlines and strategies Examples

Monday 7 Nov Discuss course outlines and strategies Science, engineering & technology Biomedical sciences Humanities & social sciences Prerequisites, precalculus, calculus, baby stats, … Computational platform. Engineering toolbox. Statisticians  practitioners Approaches targeted to focus group …. Examples of good exp

Monday 7 Nov: report group discussion Sadi Garcia Dep stats offers 2 courses for all fac Baby stats. 5 th sem. UNALM. Stats methods for research, inc DOE. 8 th or 9 th sem. Agronomy, An Sc Ex des Principles. How to introduce replication, randomisation and blocking? Simple designs. CRD CRBD LS linked to ANOVA. MulComp. Factorial arrangements Regression analysis ANCOVA. Possible step from ANOVA to GLM Weakness Planning. Factors? Levels? How select optimal DOE? Practical, pragmatic, no computational exercise R training prerequisite (in baby stats) Short course

Monday 7 Nov: report discussion Ximena Reynafarje –First baby stats. Stats concepts and principles. –Stats methods for research Focused on maths No critical thinking Simple ex => understand implementation of theory Starting from research question => problem solving => design Basic principles should be understood, without prerequisites –More emphasis on practical ex Recognition of similarity between ex –Optimal compromise between too practical and too theoretical Range of ex of any application field –What to do with repeated measures? Autocorrelation? Add extra modules?

Monday 7 Nov: report discussion Daniel Martinez –3 courses Bac in Bucaramanga, Colombia Maths 2D Babybaby stats Exp Des –Graphics => sampling strategy for two pop Histogram => prob distr Hypothesis 2-means t-test Hypothesis 2-var t-test CRD CRBD Chisq indep test for contingency tables Excercises in XLS. Stats add-on. Intuitive approach. One-factor-at-a-time. Objective: do analysis independly. –Epidemiology, only course that builds further on stats concept