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:: Course SummaryThis training course covers advanced experimental designs to account for different constraints that experimenters have to deal with, such as: time, resources, material heterogeneity, repeated measures on subjects and randomization constraints. It also deals with how to analyze data and interpret results based on the design selected.
:: Learning ObjectivesUpon completion of this training session, participants will be able to:
Acquire an understanding of advanced experimental designs, such as complete and balanced incomplete block designs, Latin square designs, designs with covariates, split-splot designs and repeated measures designs
Choose an experimental design based on study objectives and experimental constraints
Construct and implement the selected experimental design
Perform the data analysis based on the study design selected and its underlying hypotheses
Accurately interpret the experimental results:: Target AudienceThis applied training session in statistics is aimed at all the scientific staff who need to design studies or experiments based on constraints whether physical or budgetary and who make decisions based on the data collected.:: PrerequisiteThis two-day course deals with the construction of advanced designs required to account for experimental constraints.
An applied knowledge of the fundamental principles in the design of experiments including the concepts involved in Analysis of Variance (ANOVA) is required, whether by having followed the course Introduction to the Design of Experiments or by having a similar level
Moreover, participants should know the essential tools in statistics - descriptive statistics, both numerical (mean, standard deviation, standard error etc.) and graphical (histogram, box-plot, scatter plot, etc.), hypothesis testing and confidence intervals - either by having attended the training session Fundamental Tools in Statistics or by possessing a similar background | | |
:: Topics Covered
- Brief Review of Statistical Inference and the Logic behind Statistical Testing: Confidence Intervals, Standard Errors and P-Values, Accounting for Variability in Experimental Units and Review of Factorial Designs
- Complete Block Designs
- Balanced Incomplete Block Designs
- Latin Square Designs
- Designs with Covariates
- Repeated Measures Designs
- Split-Plot Designs
- ANOVA for Advanced Designs
- Using Real Data, Interpretation of the Results
:: Course ContentThis training session covers advanced experimental designs to account for different constraints that experimenters have to deal with, such as time, resources, material heterogeneity, repeated measures on experimental units and randomization constraints. It also deals with the way to analyze data and to interpret results.
The main objective of this course is to master the fundamental concepts involved in planning an experiment in the presence of constraints.
The training begins with a review of hypothesis testing, the principles of the Design of Experiments (DOE), the structure of an experimental design and factorial designs.
Next, different advanced experimental designs are introduced for a growing number of constraints imposed on the experiment. First, experimental designs that take into account heterogeneity constraints are studied: balanced complete and incomplete block designs. Second, designs which can take into account several constraints are described: Latin square designs and designs with covariates. Last, the design of experiments under physical constraints is discussed: split-plot designs and repeated measures designs.
This training session is only offered on-site. If you are interested, please contact us.
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