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A continuous variable is one that can take any value within an interval (or a set of intervals). A discrete variable is one that can only take certain values.Some further notes:* Often a discrete variable takes integer values, but that is not necessary.* Neither discrete nor continuous variables need be limited to a finite number of possible values.* Frequently, continuous variables are continuous only in principle, and the measuring instruments or recording make them discrete. Eg your height is continuous but as soon as it is recorded as 1.75 cm or 5'9", it is made discrete.
Continuous data is data that can theoretically be any amount at all. There may be a maximum and minimum value, but all numbers in between are possible. For instance, the amount of time it takes to complete a task, might be 13.4864 seconds. This makes the time continuous data. Discrete is data that can only be set amounts. For instance, how much money someone has, or how many times they've been to the cinema. You cannot have 13.4864 cents, or have visited the cinema 7.33 times. This is a discrete value.
In probability theory, the expectation of a discrete random variable X is the sum, calculated over all values that X can take, of : the product of those values and the probability that X takes that value. In the case of a continuous random variable, it is the corresponding integral.
A Bernoulli distribution is a discrete probability distribution which takes value 1 with success probability p and value 0 with failure probability q = 1 - p.
One meter is about one and a half steps. It takes a second or two.
it is a continuous random variable
A continuous variable is one that can take any value within an interval (or a set of intervals). A discrete variable is one that can only take certain values.Some further notes:* Often a discrete variable takes integer values, but that is not necessary.* Neither discrete nor continuous variables need be limited to a finite number of possible values.* Frequently, continuous variables are continuous only in principle, and the measuring instruments or recording make them discrete. Eg your height is continuous but as soon as it is recorded as 1.75 cm or 5'9", it is made discrete.
An analog signal is a continuous signal that contains time-varying quantities. Unlike a digital signal, which has a discrete value at each sampling point, an analog signal has constant fluctuations. netonplus.com
Continuous data is data that can theoretically be any amount at all. There may be a maximum and minimum value, but all numbers in between are possible. For instance, the amount of time it takes to complete a task, might be 13.4864 seconds. This makes the time continuous data. Discrete is data that can only be set amounts. For instance, how much money someone has, or how many times they've been to the cinema. You cannot have 13.4864 cents, or have visited the cinema 7.33 times. This is a discrete value.
A nominal variable is one where observations are classified to categories eg small, medium and large for t-shirts. A real variable is one that takes numerical values, such as shirt size 12, 14, 16 etc. A real value may be a continuous (as opposed to discrete) variable but the very act of recording the observation usually results in the continuous variable being replaced by a discrete one.
In probability theory, the expectation of a discrete random variable X is the sum, calculated over all values that X can take, of : the product of those values and the probability that X takes that value. In the case of a continuous random variable, it is the corresponding integral.
A Discrete Fourier Transform is simply the name given to the Fourier Transform when it is applied to digital (discrete) rather than an analog (continuous) signal. An FFT (Fast Fourier Transform) is a faster version of the DFT that can be applied when the number of samples in the signal is a power of two. An FFT computation takes approximately N * log2(N) operations, whereas a DFT takes approximately N^2 operations, so the FFT is significantly faster simple answer is FFT = Fast DFT
continuous
Zero.
Continuous conduction.
For a discrete probability distribution, you add up x*P(x) over all possible values of x, where P(x) is the probability that the random variable X takes the value x. For a continuous distribution you need to integrate x*P(x) with respect to x.
A thought is a pattern, not a discrete thing made out of matter. As such, it weighs nothing, since it takes mass to weigh anything.