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F-test analysis - created from Mind Map

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One Way ANOVA
Factorial ANOVA
Manually
R- Studio
1. Set the Hypothesis
2. Analyze the degree of freedom and determine the critical value of F
3. Analyze the Sum of the Square
5. Find the F stats
4. Calculate the variance Between and Variance Within
6. Conclusion
H0 and H1
df between and df within
Critical value of F : from the F table
Calculate the mean for each sample
Grand Mean
Sum of Square
SStotal
SSwithin
SSbetween
F=Variance Between/Variance Within
Variance= Sum of Sample/Degree of freedom
Fail the hypothesis or Accept the hypothesis
1.Calculate the group means and the overall mean.
2. Calculate Regression Sum of Squares (SSR) using formula:
3. Calculate the error Sum of Squares (SSE) using :
5. Fill in the ANOVA table
4. Calculate the total Sum of Square (SST)
6. Interprets the results
nΣ(Xj – X..)2
Σ: a greek symbol that means “sum” Xj: the mean of group j X..: the overall mean
Σ(Xij – Xj)2
Σ: a greek symbol that means “sum” Xij: the ith observation in group j Xj: the mean of group j
df treatment: k-1 = 3-1 = 2 df error: n-k = 30-3 = 27 df total: n-1 = 30-1 = 29 MS treatment: SST / df treatment = 192.2 / 2 = 96.1 MS error: SSE / df error = 1100.6 / 27 = 40.8 F: MS treatment / MS error = 96.1 / 40.8 = 2.358
SST = SSR + SSE
Manually
R- Studio
4. Estimate the F ratio for
3. Estimate the degrees of freedom and mean square for
2. Estimate the Sum of Squares
1.Calculate for each cell,
5. Calculate F critical values for
6. Compare F ratio and F critical for each effect and do a conclusion
If the result of interaction factor (AB) is
Factor A
Factor B
AB interactions
Factor A
Factor B
AB interactions
SStotal
SSab
SSb
SSbetween
SSa
SSwithin
Squared score total (T2)
Sum of Squares Within (SSw)
Total (T)
Factor A, Factor B , AB interactions
Significant
Not Significant
Simple main effect
Simple Comparison (if simple main effect is significant
Main effect
Simple Comparison (if main effect is significant)
1. Set Woking directory
2. Load the libarary
3. Import the datafile into the Rstudio
4. Test the assumptions
5. Run the analysis
Specifying the location of the folder
Determine and activate the packages to be used
Bringing the datafile into Rstudio platform
a. Test for normality
b. Test of independence
c. Homogeneity of variance
Examine the distribution of the dependent variable
Assume met (believe that when collecting data , one's response is not affected by another)
By Factor A( test for equality of variance for 2 groups
By Factor B (test for equality of variance for 3 groups or more
Run factorial ANOVA and get the output

Información
- Descripción
- Apunte sobre F-test analysis - created from Mind Map, creado por potato cover el 16/06/2021.
- Creado por
- potato cover
- Visitas
- 1
- Historial
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- Creado por potato cover · 16 jun 2021
- Copiado a Apunte por potato cover · 16 jun 2021
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