which one of the following is a discrete variable?
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Discrete Variable. Variables that can only take on a finite number of values are called "discrete variables." All qualitative variables are discrete. Some quantitative variables are discrete, such as performance rated as 1,2,3,4, or 5, or temperature rounded to the nearest degree.
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A discrete variable has a complete range of values. ... A continuous variable is uncountable while a discrete variable has countable values. The data set for a discrete variable is finite meaning it has an end value while a continuous variable can take up an infinite value which implies that it can have endless values.
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Explanation: Discrete data is data where it has to be from a certain set of values e.g a shoe size can only be a certain value. The height is continuous as the height could take multiple values e.g from 10m all the way up 18.95m.
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A discrete probability distribution is a probability distribution that can take on a countable number of values. For the probabilities to add up to 1, they have to decline to zero fast enough. For example, if. for n = 1, 2, ..., the sum of probabilities would be 1/2 + 1/4 + 1/8 + ... = 1.
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Discrete variables are the variables, wherein the values can be obtained by counting. On the other hand, Continuous variables are the random variables that measure something. Discrete variable assumes independent values whereas continuous variable assumes any value in a given range or continuum.
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Categorical variables contain a finite number of categories or distinct groups. Categorical data might not have a logical order. ... Discrete variables are numeric variables that have a countable number of values between any two values.
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10 Answers. Discrete data can only take particular values. There may potentially be an infinite number of those values, but each is distinct and there's no grey area in between. ... Continuous data are not restricted to defined separate values, but can occupy any value over a continuous range.
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The probability distribution of a discrete random variable X is a listing of each possible value x taken by X along with the probability P(x) that X takes that value in one trial of the experiment.
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The mode of a set of data values is the value that appears most often. If X is a discrete random variable, the mode is the value x (i.e, X = x) at which the probability mass function takes its maximum value. In other words, it is the value that is most likely to be sampled.
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There are two types of quantitative variables: discrete and continuous. What does the data represent? Counts of individual items or values.
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A variable that is not continuous is called discrete. A discrete quantitative variable is one that cannot take on all values within the limits of the range. ... Other examples of discrete quantitative variables are the number of legs on an animal, number of siblings, ACT scores, IQ scores, and shoe size.
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Consider two discrete random variables X and Y. ... Intuitively, two random variables X and Y are independent if knowing the value of one of them does not change the probabilities for the other one. In other words, if X and Y are independent, we can write P(Y=y|X=x)=P(Y=y), for all x,y.
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A discrete distribution describes the probability of occurrence of each value of a discrete random variable. ... With a discrete probability distribution, each possible value of the discrete random variable can be associated with a non-zero probability.
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Strictly speaking, it is a discrete variable, given that there is a basic unit of 1p below which measurements cannot be taken. However, 1p is so small in relation to an annual salary (even the annual salary of a teacher) that in practice it is treated like a continuous variable.
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Discrete data can be numeric -- like numbers of apples -- but it can also be categorical -- like red or blue, or male or female, or good or bad. Continuous data are not restricted to defined separate values, but can occupy any value over a continuous range.
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Discrete data involves round, concrete numbers that are determined by counting. Continuous data involves complex numbers that are measured across a specific time interval. A simple way to describe the difference between the two is to visualize a scatter plot graph vs. a line graph.
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A random variable, usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. There are two types of random variables, discrete and continuous.