Apparently f(x)=sqrt(x)
But I'm not sure why. That's what I'm looking for now :)
See: http://en.wikipedia.org/wiki/Uniform_distribution_(continuous)
The probability distribution function (pdf) is defined over a domain which contains at least one interval in which the pdf is positive for all values. Usually the domain is either the whole of the real numbers or the positive real numbers, but it can be a finite interval: for example, the uniform continuous distribution. Also, trivially, the pdf is always non-negative, the integral of the pdf, over the whole real line, equals 1.
There are infinitely many continuous probability functions and there is no information whatsoever in the question to determine the nature of the distribution: uniform, Normal, Student's t, Chi-square, Fisher's F, Gamma, Beta, Lognormal, etc, etc. Second, every continuous function must have at least two points for which the probability is the same. There is no information as to which of these two (or more) points is the relevant one. There can therefore be no answer.
Assuming the uniform continuous distribution, the answer is 29/49. With the uniform discrete distribution, the answer is 29/50.
No. First of all, it depends on what is being graphed. Also, a distance-time graph of non-uniform motion could be any continuous line other than a straight line - for example a saw-tooth shape.
fist disply your anser
See: http://en.wikipedia.org/wiki/Uniform_distribution_(continuous)
There exists an N such that for all n>N, for any x. Now let n>N, and consider the continuous function . Since it is continuous, there exists a such that if , then . Then so the function f(x) is continuous.
The way I understand it, a continuos function is said not to be "uniformly continuous" if for a given small difference in "x", the corresponding difference in the function value can be arbitrarily large. For more information, check the article "Uniform continuity" in the Wikipedia, especially the examples.
They are continuous, symmetric.
Don't know what "this" is, but all symmetric distributions are not normal. There are many distributions, discrete and continuous that are not normal. The uniform or binomial distributions are examples of discrete symmetric distibutions that are not normal. The uniform and the beta distribution with equal parameters are examples of a continuous distribution that is not normal. The uniform distribution can be discrete or continuous.
The probability distribution function (pdf) is defined over a domain which contains at least one interval in which the pdf is positive for all values. Usually the domain is either the whole of the real numbers or the positive real numbers, but it can be a finite interval: for example, the uniform continuous distribution. Also, trivially, the pdf is always non-negative, the integral of the pdf, over the whole real line, equals 1.
the variance of the uniform distribution function is 1/12(square of(b-a)) and the mean is 1/2(a+b).
We need the five example of uniform and non-uniform motion.
There are infinitely many continuous probability functions and there is no information whatsoever in the question to determine the nature of the distribution: uniform, Normal, Student's t, Chi-square, Fisher's F, Gamma, Beta, Lognormal, etc, etc. Second, every continuous function must have at least two points for which the probability is the same. There is no information as to which of these two (or more) points is the relevant one. There can therefore be no answer.
It is the continuous production of food for emample that is uniform in shape size, and very consistant in products. It operates 24/7, 7 days a week.
They are both continuous, symmetric distribution functions.