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Answer :
To solve this problem, we need to understand the components involved in an analysis of variance (ANOVA). The terms mentioned here are:
- SST (Total Sum of Squares): This is the total variation in the data.
- SSTR (Sum of Squares for Treatment): This represents the variation due to the differences between the treatment means.
- SSE (Sum of Squares for Error): This accounts for the variation within the treatments.
The relationship between these components is given by the formula:
[tex]\[ \text{SST} = \text{SSTR} + \text{SSE} \][/tex]
Given:
- [tex]\( \text{SST} = 170 \)[/tex]
- [tex]\( \text{SSTR} = 90 \)[/tex]
We aim to find [tex]\( \text{SSE} \)[/tex].
Using the relationship:
[tex]\[ \text{SSE} = \text{SST} - \text{SSTR} \][/tex]
Substituting the values:
[tex]\[ \text{SSE} = 170 - 90 \][/tex]
[tex]\[ \text{SSE} = 80 \][/tex]
Therefore, the value of SSE is 80. The correct answer is c. 80.
- SST (Total Sum of Squares): This is the total variation in the data.
- SSTR (Sum of Squares for Treatment): This represents the variation due to the differences between the treatment means.
- SSE (Sum of Squares for Error): This accounts for the variation within the treatments.
The relationship between these components is given by the formula:
[tex]\[ \text{SST} = \text{SSTR} + \text{SSE} \][/tex]
Given:
- [tex]\( \text{SST} = 170 \)[/tex]
- [tex]\( \text{SSTR} = 90 \)[/tex]
We aim to find [tex]\( \text{SSE} \)[/tex].
Using the relationship:
[tex]\[ \text{SSE} = \text{SST} - \text{SSTR} \][/tex]
Substituting the values:
[tex]\[ \text{SSE} = 170 - 90 \][/tex]
[tex]\[ \text{SSE} = 80 \][/tex]
Therefore, the value of SSE is 80. The correct answer is c. 80.
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