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.
No I don't know... Plz give me the right answer
distinguish between linear and non linear demands funcions
Latent functions are unintended, while manifest functions are intended.
Contiguous is a word that is used normally when objects are spatially adjacent and Continuous is a word that is normally used when events are adjacent in time. This usage is probably the reason why arrays are "contiguously" allocated and not "continuously" allocated. On the other hand, a function f(t) is "continuous" and not "contiguous".
Exponential and logarithmic functions are inverses of each other.
A convolution is a function defined on two functions f(.) and g(.). If the domains of these functions are continuous so that the convolution can be defined using an integral then the convolution is said to be continuous. If, on the other hand, the domaisn of the functions are discrete then the convolution would be defined as a sum and would be said to be discrete. For more information please see the wikipedia article about convolutions.
The difference between continuous and discrete system lies in the variables. Whereas the continuous systems have dynamic variables, the discrete system have static variables.
contiguous is "separated in space" and continuous is "separated in time"
What is the difference between the population and sample regression functions? Is this a distinction without difference?
nothing
There is no difference
contact tob continuous with others
Linear equations are a tiny subset of functions. Linear equations are simple, continuous functions.
They are both continuous, symmetric distribution functions.
A simple continuous distribution can take any value between two other values whereas a discrete distribution cannot.
The value of a random variable that is uniformly distributed between 20 and 100 can be calculated by calculating the sum of numbers from 20 to 100 and dividing it by the difference between 100 and 20. The resulting mean is 58.5.
There is no difference they perform the same functions.