Can time be an dependent variable?

Time is a common independent variable, as it will not be affeced by any dependent environemental inputs. Time can be treated as a controllable constant against which changes in a system can be measured.

How will you know that a random variable is discrete or continuous?

A discrete random variable has a countable number of possible values. The probability of each value of a discrete random variable is between 0 and 1, and the sum of all the probabilities is equal to 1. A continuous random variable takes on all the values in some interval of numbers.

How do you know if a joint random variable is independent?

Two discrete random variables are independent if their joint pmf satisfies p(x,y) = pX (x)pY (y),x ∈ RX ,y ∈ RY . f (x,y) = fX (x)fY (y),−∞ < x < ∞,−∞ < y < ∞. Random variables that are not independent are said to be dependent.

Is time independent of matter?

The answer is no – time is not necessarily an independent variable, especially in general relativity it is a function of the relative velocity of an object. A possible view on the concept of time is that it is not an element of nature but an element of our thinking about nature.

Is time the response variable?

Explanatory Variables vs. The response variable is the focus of a question in a study or experiment. An explanatory variable is one that explains changes in that variable. It can be anything that might affect the response variable. And so survival time is the response variable.

Is time a continuous variable?

Time is a continuous variable. You could turn age into a discrete variable and then you could count it.

What does it mean for random variables to be independent?

Independence of Random Variables If X and Y are two random variables and the distribution of X is not influenced by the values taken by Y, and vice versa, the two random variables are said to be independent.

How do you prove independence?

Events A and B are independent if the equation P(A∩B) = P(A) · P(B) holds true. You can use the equation to check if events are independent; multiply the probabilities of the two events together to see if they equal the probability of them both happening together.