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Yes it can assume countable number of outcomes.

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Q: A discrete random variable may assume a countable number of outcome values?
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Continue Learning about Statistics

A discrete variable may assume fractional or decimal values but they must have distance between them?

True


What is difference between a discrete variable and a continuous variable?

a continous variable is one that can assume different values at each point, so if you were measuring height it could be 187.1, 187.2.. 187.8, but this can not be used for something such as measuring the amount of people in a family, because there can't be 3.4 people in a family. This is when discrete variable is used, this measures full numbers.


Is family size a continuous variable?

No, this is a discrete variable since it can assume only whole number values: 0, 1, 2, 3, ... . A continuous variable would be one such as volume of water in a swimming pool which could be measured in real number units of volume.


How would you interpret the findings of a correlation study that reported a linear correlation coefficient of -0.13?

There is not enough information to say much. To start with, the correlation may not be significant. Furthermore, a linear relationship may not be an appropriate model. If you assume that a linear model is appropriate and if you assume that there is evidence to indicate that the correlation is significant (by this time you might as well assume anything you want!) then you could say that the dependent variable decreases by 0.13 units for every unit change in the independent variable - within the range of the independent variable.


What is a continuous random variable?

In the simplest setting, a continuous random variable is one that can assume any value on some interval of the real numbers. For example, a uniform random variable is often defined on the unit interval [0,1], which means that this random variable could assume any value between 0 and 1, including 0 and 1. Some possibilities would be 1/3, 0.3214, pi/4, e/5, and so on ... in other words, any of the numbers in that interval. As another example, a normal random variable can assume any value between -infinity and +infinity (another interval). Most of these values would be extremely unlikely to occur but they would be possible. The random variable could assume values of 3, -10000, pi, 1000*pi, e*e, ... any possible value in the real numbers. It is also possible to define continue random variables that assume values on the entire (x,y) plane, or just on the circumference of a circle, or anywhere that you can imagine that is essentially equivalent (in some sense) to pieces of a real line.