00:01
Here we are reminded of the key concept of a z score, which is that when we take some observation from a distribution and we find its z score, or in other words we standardize it, the z score tells us how many standard deviations away from the mean the observation is.
00:24
So for example, if from some distribution we observe a value of 12 for the random variable, if this value of 12 is 3 .48 standard deviations to the right of the mean, that means that its z score is simply 3 .48.
00:46
The z score is its distance from the mean in terms of standard deviations.
00:52
So part b we have some value x which has a z score, or which we are told is 3.
00:59
3 4 standard deviations to the left of the mean, this means that its z score is minus 3 .34.
01:10
So that is we have a score of 9 which has been standardized.
01:18
So if this is the standard normal distribution it has a mean of 0 and a standard deviation of 1.
01:28
And we're told from the non -standardized distribution that x is 9 and that this value lies 3 .34 standard deviations to the left of the mean...