How do you calculate the Grand Variance?

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In summary, the conversation discusses an issue with calculating the "Grand Variance" for a data set with three groups. The correct answer is 3.124, but the individual is unsure of how the book arrived at that value. They discuss the concept of taking an average of averages and the importance of considering the sizes of each group. The conversation also touches on the formula for calculating the Grand Variance and provides additional formulas for calculating the SST and SSE. The conversation concludes with the individual successfully calculating the Grand Variance and discussing the formula for the Grand Standard Deviation.
  • #1
nukeman
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Homework Statement



Im working on this data set, and I can't get the "Grand Variance" calculated correctly, driving me nutz!

So, its 3 groups of data.

Group 1 mean = 2.20
group 2 mean = 3.20
group 3 mean = 5

Group 1 variance = 1.70
Group 2 variance = 1.70
Group 3 variance = 2.50

Now, it asks what is the "Grand Variance"... The correct answer is 3.124 - but am not sure how the book got that value. I keep getting wrong :(

Any pointers?

Homework Equations


The Attempt at a Solution

 
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  • #2
I hope you've been told at some time not to take an average of averages (unless you happen to know that the averages come from sample sets of the same size).
What else do you know about these groups? Their sizes perhaps?
 
  • #3
haruspex said:
I hope you've been told at some time not to take an average of averages (unless you happen to know that the averages come from sample sets of the same size).
What else do you know about these groups? Their sizes perhaps?

5 people in each group...3 groups
 
  • #4
Crap, I corrected my post!

It asks for the GRAND VARIANCE!, not grand mean. Grand Variance is 3.124
 
  • #5
nukeman said:
It asks for the GRAND VARIANCE!, not grand mean. Grand Variance is 3.124
Same question would have arisen, so no harm done.
Think about the last step that would have been involved in calculating the mean of each group. What can you work backwards from the info you have to determine? Then do the same for the variances.
 
  • #6
haruspex said:
Same question would have arisen, so no harm done.
Think about the last step that would have been involved in calculating the mean of each group. What can you work backwards from the info you have to determine? Then do the same for the variances.

All I did to get the Grand mean was calculate the mean, of the mean's of each group...
 
  • #7
I keep getting wrong answer. I don't know what I am doing wrong. :(

Here is the data:

2ursg8g.png
 
  • #8
nukeman said:
All I did to get the Grand mean was calculate the mean, of the mean's of each group...
Since the groups all happen to be the same size, that should be fine (but I hope you would not have done that otherwise). And you get 3.467, right?
But you can't do that with the variances because the formula used there has an n/(n-1) term, where n is the number of data values. So for each group that gives 5/4, but for the grand variance it will be 15/14.
Have you tried to calculate the group variances yourself? Do you get the values in the table?
 
  • #9
Hi nukeman! :smile:

The grand variance is
$$\text{Grand Variance} = {SST \over N-1}$$
where:
##SST## is the sum of the squared differences of each score with the grand mean,
##N## is the total number of scores (15 in your case).​
If you want, you can also calculate it without using the actual scores.
But then you'll need a couple of additional formulas, so you can calculate SST differently.
Do you have formulas for that?
 
  • #10
Hey "I Like Serena"

I don't quite understand the SST part of that equation.

Do I take the squared differences of ALL 15 data points, then add them up?

then divide by n-1 ? (14) ?
 
  • #11
Yes.
 
  • #12
I like Serena said:
Yes.

ahhg I did that! Don't tell me I just messed up on my calculator and that's why I am trying all these different things! lol :)
 
  • #13
So are you good now?
 
  • #14
Oh yes, I got it! Thanks!

While you are here, your formula make more sense than the one I was given. What is the formula for the Grand SD?

I just square root the Grand Variance correct?
 
  • #15
Correct.
 
  • #16
If you're interested, here are a couple of other identities (##n=5##).
They come from (one-way) ANOVA theory which is what you are doing.

$$SST = SSM + SSE$$
$$SSM=n((\bar X_1 - \text{Grand Mean})^2 + (\bar X_2 - \text{Grand Mean})^2 + (\bar X_3 - \text{Grand Mean})^2)$$
$$SSE=(n-1)(s_1^2 + s_2^2 + s_3^2)$$
 
  • #17
Wow, thanks! Appreciate it.
 

Related to How do you calculate the Grand Variance?

1. How is the Grand Variance calculated?

The Grand Variance is calculated by taking the sum of the squared differences between each data point and the mean of all data points, and then dividing by the total number of data points.

2. What is the formula for calculating the Grand Variance?

The formula for calculating the Grand Variance is: (Σ(x - μ)^2) / n, where Σ represents the sum of all data points, x represents each individual data point, μ represents the mean of all data points, and n represents the total number of data points.

3. Why is the Grand Variance important in statistics?

The Grand Variance is important in statistics because it measures the spread of data from the mean. A higher Grand Variance indicates a larger spread of data points, while a lower Grand Variance indicates a smaller spread. This information is useful in understanding the variability and distribution of a set of data.

4. Can the Grand Variance be negative?

No, the Grand Variance cannot be negative. The squared differences in the formula ensure that the result will always be a positive value.

5. How can I interpret the Grand Variance?

The Grand Variance is typically reported in the same units as the original data. A larger Grand Variance suggests that the data points are more spread out and have a wider range of values, while a smaller Grand Variance suggests that the data points are closer together and have a more narrow range of values. It is important to compare the Grand Variance to the mean and other measures of central tendency to fully understand the distribution of the data.

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