Contents
How to calculate probability under the overlapping area of?
– Juho Kokkala Jun 18 ’14 at 8:37 If you sample points from either normal distribution, you get points on the Perikymata-axis rather than on the 2-dimensional area. Furthermore, the green zone is infinitely wide, so all values sampled from either distribution are under the green zone, so in this sense the probability would be 1.
How to calculate the probability of a value between 0 and 2?
For this example, to determine the probability of a value between 0 and 2, find 2 in the first column of the table, since this table by definition provides probabilities between the mean (which is 0 in the standard normal distribution) and the number of choice, in this case 2.
How to find the point where two normal distributions cross?
Closed 2 years ago. I have two normal distributions. Say for example, one with a mean of .76 and one with a mean of .62. Both standard deviations = .05. How do I find which point on the x-axis is where the two distributions cross? In other words, x-coordinate at the deepest part of the valley in between the two distributions? P.S.
How to calculate the probability of an even number?
Here the set is represented by the 6 values of the dice, written as: S = {1,2,3,4,5,6} Probability of an even number: P (A) = {2,4,6} = 3/6 Probability of a multiple of 3: P (B) = {3,6} = 2/6 Intersection of A and B: P (A ∩ B) = {6} = 1/6 P (A U B) = 3/6 + 2/6 -1/6 = 2/3
How to calculate the probability of generating a data point?
I believe I would be interested in the probability of generating a point “at least as unlikely” as the given data point. In a 1 D normal distribution case this would be the area under the “two tails” of the PDF. E.g. 1 − ( C D F ( x) − C D F ( μ − x)).
Is there a problem with overlapping ranges of data?
The problem of overlapping rows when you are dealing with inclusive ranges is not a complex one, but it is tricky. I never can quite remember the basic algorithm right off the top of my head, and adding the output as to where the issues is messy.
Which is a problem associated with date based data?
The problem at hand is most often associated with date based data, such as effective dates for a row in a temporal/type 2 dimension table, or other cases like appointment times, etc.
How are overlapping tail areas used to find distributions?
In that case, the overlapping tail areas would add in those histogram categories, and modern methods that find distributions would have little trouble segregating that mixture into two normal distribution models to recover the input values.
What can be stated about the overlapping area depicted in the diagram below?
What can be stated about the overlapping area depicted in the diagram below: The area of overlap is 9%. The question I am trying to answer is whether the likelihood that the true value of the Green in the population is greater than the true value of the Red in the population.
How to know if an overlap has meaning?
In order for the overlap to have meaning, it would have to be relevant to some hypothesis or question about data. To do that let us assume a context to make an example.