00:01
For a repeated measures study comparing k is equal to 3 treatment conditions with a sample of n is equal to 4 participants the participant totals the p values are 12 8 4 and 16 and the sum of squares ss values within each treatment are ss 1 is 20 ss 2 is 40 ss 3 is 65 so what is the value of value for the ss error.
00:37
So we need to find it.
00:38
It is just beginning that so in a repeated measures and over design the ss error sum of squares for some of squares for error can be calculated as ss error is equal to ss total minus ss treatments, right? so where ss total is the total number of sum of squares of for all these scores regardless of the treatment now ss treatment is the sum of squares for the treatments which is equivalent to the sum of sum of the ss values provided for each treatment now what is given to us? let us just see that ss 1 is 20 ss 2 is 40 and ss 3 is 65 so the sum of the squares for the treatment is ss treatments is equal to sum of ss 1 ss 2 and ss 3 so when you do so you will be getting 20 plus 40 plus 65 which is 125 right now we need to find the ss total.
01:41
So the formula for the formula to find ss total for the repeated measures and over is ss total is equal to ss total is equal to okay, that would be equal to ss participants plus ss treatments perfect, so where ss participants you can say that the sum of squares of the participants itself.
02:09
So ss participants could be also replaced by p square ss total here i am writing ss total would be equal to ss participants could be replaced by summation of p square divided by k minus g square by n right plus ss treatments we have right.
02:52
So we already found out the value of ss treatments.
02:54
So this could be written as 125 itself.
02:58
Okay.
02:59
So now let us just do so...