Statistics - ANOVA table calculation

In summary, the conversation discusses the process of calculating MSTr and MSE for ANOVA analysis, with a focus on how to obtain the values for SSTr and SSE. The formula for SST is given as Ʃ(Xibar-Xbar)^2, with clarification that Yi and Ybar are dummy variables. The purpose of this calculation is to obtain the F statistic for hypothesis testing. The conversation concludes by mentioning that using a statistics package like Minitab can simplify the process and provide additional information such as the p-value.
  • #1
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Homework Statement


http://studyterps.com/files/STAT401-FE-SP09-CREMINS.pdf

#5

Homework Equations


The Attempt at a Solution



How did they get the MSTr and MSE form that table? I'm aware of the formulas such as MSE= SSE/(N-k) and so on, but usually you have some values given. Do I first claculate SST using the table, then use SST to get MSE?

SST would be: Sum of (Yibar - Ybar)2

What's Yi and Y here? I suppose Yi is each separate value for a specific row andYbar is the average for the entire table.
 
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  • #2
First, the total=the sum of all the data=216.
The correction factor for the mean (CM)= 216^2/12 (note the total of all values=n=12)

To get MSTr, you first need the SSTr. Get the three totals from each distinct hormone. So, here are the totals:
T1=59, T2=79, T3=78
Then SSTr= (T1^2/n1+T2^2/n2+T3^3/n3)-CM=(59^2/4+79^2/4+78^2/4)-(216^2/12)=63.5
Next, you need the degrees of freedom (d.f.) for the "treatment", i.e. type of hormone=3-1=2
Finally,
MSTr=SStr/d.f.=63.5/2=31.75

For MSE, it's a similar process, but you need to find SSE, and then divide it by the degrees of freedom for error.

Note: The total degrees of freedom=n-1=12-1=11, and the d.f. for treatment=2, so the d.f. for error=11-2=7
 
  • #3
Also,the formula for SSTr you mentioned you need to multiply by ni which equals the number of values per row (in your specific example=4) and if you compute SSTr using the formula you mentioned, Yi is the average of a specific row, and Ybar is the total average (216/12). So you compute (Yi-Ybar)^2 for each row and sum them up then multiply by 4.
 
  • #4
SMA_01 said:
First, the total=the sum of all the data=216.
The correction factor for the mean (CM)= 216^2/12 (note the total of all values=n=12)

To get MSTr, you first need the SSTr. Get the three totals from each distinct hormone. So, here are the totals:
T1=59, T2=79, T3=78
Then SSTr= (T1^2/n1+T2^2/n2+T3^3/n3)-CM=(59^2/4+79^2/4+78^2/4)-(216^2/12)=63.5
Next, you need the degrees of freedom (d.f.) for the "treatment", i.e. type of hormone=3-1=2
Finally,
MSTr=SStr/d.f.=63.5/2=31.75

For MSE, it's a similar process, but you need to find SSE, and then divide it by the degrees of freedom for error.

Note: The total degrees of freedom=n-1=12-1=11, and the d.f. for treatment=2, so the d.f. for error=11-2=7

Thank you. Is this not overly complicated for SSTr? How about SST= ni(Xibar-Xbar)^2 ? I don't think I've seen your way of doing it before.

BTW. I'm doing this to get F, which I need for other things. Don't know if there's a faster way to get F.
 
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  • #5
Basically, you're filling out the ANOVA table, you can do this easily buy running a test through a statistics package like Minitab. The formula you mentioned for SST is the one that you use for SSTr, I just figured Yi and Yi bar were dummy variables and the meaning was the same. But that does not look like the formula used for SST, are you sure about it?
For SST, I have Ʃ(Xibar-Xbar)^2

You can just compute using the formulas, the way I did it was the way we were taught, but it's the same answer.

And yes, you do it to get F, that way you can use the F distribution to conclude whether to accept or reject your hypothesis.

The easiest way is to run it through a stats package, that way you get the p-value as well.
 

Related to Statistics - ANOVA table calculation

1. What is an ANOVA table and why is it used in statistics?

An ANOVA table, or analysis of variance table, is a statistical tool used to determine whether there are significant differences between the means of three or more groups. It breaks down the total variability in a dataset into different sources, such as between groups and within groups, to determine the overall significance of the differences.

2. How is an ANOVA table calculated?

An ANOVA table is calculated by first determining the sum of squares for each source of variability, which is then used to calculate the mean squares and F ratio. The F ratio is then compared to a critical F value to determine the significance of the differences between groups.

3. What is the difference between a one-way and two-way ANOVA table?

A one-way ANOVA table is used when there is only one independent variable, while a two-way ANOVA table is used when there are two independent variables. In a two-way ANOVA, the sources of variability include the main effects of each independent variable and the interaction between them.

4. How do I interpret an ANOVA table?

To interpret an ANOVA table, you first need to look at the F ratio and compare it to the critical F value. If the F ratio is larger than the critical F value, it means there are significant differences between the means of the groups. You can also look at the p-value, which indicates the probability of obtaining the observed results by chance.

5. What are the assumptions of an ANOVA table?

The assumptions of an ANOVA table include the normality of the data, homogeneity of variances, independence of observations, and equal group sizes. Violation of these assumptions can affect the accuracy and validity of the results, so it is important to check for these assumptions before conducting an ANOVA analysis.

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