Duplicate Question
The question on this page has been marked as a duplicate question.
Original Question
Consider the following partially completed ANOVA, and answer the following: give the value of (A). / Beskou die onvoltooide ANO...Question
Consider the following partially completed ANOVA, and answer the following: give the value of (A). / Beskou die onvoltooide ANOVA en beantwoord die volgende: gee die waarde van (A).
ANOVA
Source SS df MS Fstat Ftab
Treatment 12 689 (C) (F) 10.6642 (G)
Error (A) (E) 595.3611
Total (B) 11
ANOVA
Source SS df MS Fstat Ftab
Treatment 12 689 (C) (F) 10.6642 (G)
Error (A) (E) 595.3611
Total (B) 11
Answers
There are no human answers yet.
Answered by
GPT-4o mini
To fill in the missing values (A), (B), (C), (E), (F), and (G) in the ANOVA table, we first need to understand some key relationships in ANOVA calculations.
1. **Total Sum of Squares (SS Total)**:
\[
SS_{\text{Total}} = SS_{\text{Treatment}} + SS_{\text{Error}}
\]
Here, \( SS_{\text{Treatment}} = 12,689 \) and we need to find \( SS_{\text{Error}} (A) \) and \( SS_{\text{Total}} (B) \).
2. **Degrees of Freedom (df)**:
The degrees of freedom can be calculated as follows:
\[
df_{\text{Treatment}} = k - 1 \quad \text{(where \( k \) is the number of treatments)}
\]
\[
df_{\text{Error}} = N - k \quad \text{(where \( N \) is the total number of observations)}
\]
The total degrees of freedom is:
\[
df_{\text{Total}} = N - 1
\]
Given that \( df_{\text{Total}} = 11 \), we also learn that:
\[
N - 1 = 11 \implies N = 12
\]
Since we know that \( df_{\text{Treatment}} + df_{\text{Error}} = df_{\text{Total}} \):
\[
df_{\text{Treatment}} + df_{\text{Error}} = 11
\]
Let \( df_{\text{Treatment}} = (C) \) and \( df_{\text{Error}} = (E) \). Then:
\[
(C) + (E) = 11
\]
3. **Mean Squares (MS)**:
Mean Squares are calculated as follows:
\[
MS_{\text{Treatment}} = \frac{SS_{\text{Treatment}}}{df_{\text{Treatment}}} = \frac{12,689}{C}
\]
\[
MS_{\text{Error}} = \frac{SS_{\text{Error}}}{df_{\text{Error}}} = \frac{A}{E}
\]
4. **F-statistic**:
The F-statistic is calculated as follows:
\[
F = \frac{MS_{\text{Treatment}}}{MS_{\text{Error}}}
\]
Given \( F = 10.6642 \) and \( MS_{\text{Error}} = 595.3611 \):
\[
MS_{\text{Treatment}} = F \times MS_{\text{Error}} = 10.6642 \times 595.3611
\]
\[
MS_{\text{Treatment}} \approx 6348.688
\]
5. **Finding Treatment Degrees of Freedom (C)**:
Now we can find \( C \):
\[
C = \frac{SS_{\text{Treatment}}}{MS_{\text{Treatment}}} = \frac{12,689}{6348.688} \approx 2
\]
Thus:
\[
E = 11 - C = 11 - 2 = 9
\]
### Summary Values:
- \( C = 2 \)
- \( E = 9 \)
6. **Finding SS Error (A)**:
\[
MS_{\text{Error}} = \frac{A}{E} \implies 595.3611 = \frac{A}{9} \implies A = 595.3611 \times 9 \approx 5,358.250
\]
7. **Finding Total SS (B)**:
\[
SS_{\text{Total}} = SS_{\text{Treatment}} + SS_{\text{Error}} = 12,689 + 5,358.250 \approx 18,047.250
\]
### Results:
- (A) = 5,358.250 (approximately)
- (B) = 18,047.250 (approximately)
- (C) = 2
- (E) = 9
- (F) = 10.6642 (from the question)
- (G) = 10.6642 (same as F)
Now, you can fill in:
- **(A)**: 5,358.250
- **(B)**: 18,047.250
- **(C)**: 2
- **(E)**: 9
- **(F)**: 10.6642
- **(G)**: 10.6642
1. **Total Sum of Squares (SS Total)**:
\[
SS_{\text{Total}} = SS_{\text{Treatment}} + SS_{\text{Error}}
\]
Here, \( SS_{\text{Treatment}} = 12,689 \) and we need to find \( SS_{\text{Error}} (A) \) and \( SS_{\text{Total}} (B) \).
2. **Degrees of Freedom (df)**:
The degrees of freedom can be calculated as follows:
\[
df_{\text{Treatment}} = k - 1 \quad \text{(where \( k \) is the number of treatments)}
\]
\[
df_{\text{Error}} = N - k \quad \text{(where \( N \) is the total number of observations)}
\]
The total degrees of freedom is:
\[
df_{\text{Total}} = N - 1
\]
Given that \( df_{\text{Total}} = 11 \), we also learn that:
\[
N - 1 = 11 \implies N = 12
\]
Since we know that \( df_{\text{Treatment}} + df_{\text{Error}} = df_{\text{Total}} \):
\[
df_{\text{Treatment}} + df_{\text{Error}} = 11
\]
Let \( df_{\text{Treatment}} = (C) \) and \( df_{\text{Error}} = (E) \). Then:
\[
(C) + (E) = 11
\]
3. **Mean Squares (MS)**:
Mean Squares are calculated as follows:
\[
MS_{\text{Treatment}} = \frac{SS_{\text{Treatment}}}{df_{\text{Treatment}}} = \frac{12,689}{C}
\]
\[
MS_{\text{Error}} = \frac{SS_{\text{Error}}}{df_{\text{Error}}} = \frac{A}{E}
\]
4. **F-statistic**:
The F-statistic is calculated as follows:
\[
F = \frac{MS_{\text{Treatment}}}{MS_{\text{Error}}}
\]
Given \( F = 10.6642 \) and \( MS_{\text{Error}} = 595.3611 \):
\[
MS_{\text{Treatment}} = F \times MS_{\text{Error}} = 10.6642 \times 595.3611
\]
\[
MS_{\text{Treatment}} \approx 6348.688
\]
5. **Finding Treatment Degrees of Freedom (C)**:
Now we can find \( C \):
\[
C = \frac{SS_{\text{Treatment}}}{MS_{\text{Treatment}}} = \frac{12,689}{6348.688} \approx 2
\]
Thus:
\[
E = 11 - C = 11 - 2 = 9
\]
### Summary Values:
- \( C = 2 \)
- \( E = 9 \)
6. **Finding SS Error (A)**:
\[
MS_{\text{Error}} = \frac{A}{E} \implies 595.3611 = \frac{A}{9} \implies A = 595.3611 \times 9 \approx 5,358.250
\]
7. **Finding Total SS (B)**:
\[
SS_{\text{Total}} = SS_{\text{Treatment}} + SS_{\text{Error}} = 12,689 + 5,358.250 \approx 18,047.250
\]
### Results:
- (A) = 5,358.250 (approximately)
- (B) = 18,047.250 (approximately)
- (C) = 2
- (E) = 9
- (F) = 10.6642 (from the question)
- (G) = 10.6642 (same as F)
Now, you can fill in:
- **(A)**: 5,358.250
- **(B)**: 18,047.250
- **(C)**: 2
- **(E)**: 9
- **(F)**: 10.6642
- **(G)**: 10.6642
Submit Your Answer
We prioritize human answers over AI answers.
If you are human, and you can answer this question, please submit your answer.