Term
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Definition
| Manipulating two factors and fixing them at two or more levels and then randomly assigning experimental units to a treatment |
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Term
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Definition
| Each of the n observations of the response variable for the different levels of the factors |
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Term
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Definition
| In a factorial design, when all levels of factor A are combined with all levels of factor B |
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Term
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Definition
| The two effects of A and B together |
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Term
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Definition
| If changes in the level of factor A result in different changes in the value of the response variable for the different levels of factor B |
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Term
| 3 requirements for the two-way analysis of variance |
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Definition
1. The populations from which the samples are drawn must be normal
2. The samples are independent
3. The populations all have the same variance |
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Term
| In a two-way ANOVA, there are 3 hypotheses |
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Definition
1 regarding the interaction effect
2. 2 regarding main effects |
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Term
| 1. In a two-way ANOVA, there are 3 hypotheses |
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Definition
H0: There is no interaction between the factors
H1: There is interaction between the factors |
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Term
| 2. In a two-way ANOVA, there are 3 hypotheses |
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Definition
H0: There is no effect of factor A on the response variable
H1: There is an effect of factor A on the response variable |
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Term
| 3. In a two-way ANOVA, there are 3 hypotheses |
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Definition
H0: There is no effect of factor B on the response variable
H1: There is an effect of factor B on the response variable |
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Term
| If the null of no interaction is rejected, then |
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Definition
| We do not interpret the results of the hypotheses involving the main effects |
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Term
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Definition
| Graphically representing the role interaction plans in any factorial design |
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