Test your knowledge of Factorial Experiment Design MCQs with this 20-question MCQ quiz! Perfect for students, statisticians, data analysts, and data scientists, this quiz covers key concepts like full factorial designs, interactions, orthogonality, contrasts, and fractional factorial experiments. Whether you’re preparing for exams, job interviews, or research, this quiz helps you master essential DOE (Design of Experiments) principles. Check your understanding of factors, levels, efficiency, and experimental regions with detailed answers provided. Sharpen your skills and boost your confidence in statistical experimental design today! Let us start with the Online Factorial Experiment Design MCQs now.
Online Factorial Experiment Quiz, Design of Experiment MCQs with Answers
Online Factorial Experiment Design MCQs with Answers
- A factorial experiment is an experiment whose design consists of two or more factors, each with
- Ronald Fisher and —————– are the pioneers of factorial design
- A full factorial design is also called a fully
- When Interaction is present, we should prefer
- Factorial designs provide a chance to estimate the effect of a factor at ———— levels of the other factor
- In the case of two factors, the relative efficiency of factorial design to one-factor-at-a-time experimental design is:
- The factorial analysis requires that dependent variables be measured as
- A factorial experiment requires that factors
- In a $2^2$ design, the number of trials is equal to
- Factorial experiments can involve factors with ————— levels
- Orthogonality of a design can be checked by putting the levels of factors in
- Factorial experiments can involve factors with —————– numbers of levels
- The range of factor levels in which an experiment can be performed is commonly known as
- In the first phase of the experiment, the stage that is completed is called
- Typically region of experimentation is a cuboidal or a
- A contrast may be used to know the magnitude or direction of —————.
- Contrast can be used to compute
- Average effect of $B$ for 3 replicates of experiment with factors $A$ and $B$ is computed by diving contrast to
- The runs of two or more fractional factorial designs may be —————– to estimate the effects of vital interest
- ————— factorial designs fill the gaps of the run size of the common factorial design.
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