Please explain how I set up and solve this problem:
For each of the populations would a score of x= 50 be considered a central score (near the middle of the distribution) or an extreme score (far out of the tail of the distribution ) is this set up x-u/o?
u=45 o= 10
u=45 o= 2
u= 90 o= 20
u= 60 o=20
If i have a problem that is a distribution with a mean of u=38 and a standard deviation of o=5 is transformed into a standardized distribution with u= 50 and o= 10. Now i need to find the new standardized score for each value from the original population. I have no clue how to set this up and solve it. Please explain in steps if possible.
original x= 39 transformed x=____
original x= 43 transformed x=____
original x= 35 transformed x=____
original x= 28 transformed x=____.
1. An extreme score happens when z value is above 2 or below -2.
So if x=50, z=(50-45)/10=0.5 (central score)
if x=50, z=(45-45)/2=0 ...
The solution gives detailed steps on determining central score and extreme score and transforming between two normal random variables. All steps are shown with brief explanations.
Normal probability z-scores
If a student had a z-score of 1, what would be the raw score (rating)?
If a student had a z-score of 1, what percentage of the participants would be
expected to rate the product higher than he/she did?
If a student gave a rating of 75, what would be the z-score?
What is the probability that a rating is below 51.03?
What is the probability that a rating is higher than 80?
What percentage of participants would be expected to rate the product lower than 75?
What is the probability that a rating falls between 25 and 65?
What is the probability that a rating falls between 75 and 80?
If the z-score is 2, what is the rating?
If the rating is 21, what is the z-score?
What is the probability of observing a rating lower than 20 or higher than 80?
4. What if one participant gave a particularly odd (extreme) rating? How extreme would it have to be for us to suspect that it should be discounted (i.e., the participant was rating a different product or was trying to ruin our data or wasn't really paying attention to the task)?View Full Posting Details