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Question > (1 pointyDiscrete random variables can only assume whole number of values....

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Question > (1 pointyDiscrete random variables can only assume whole number of values.

Question > (1 pointy Discrete random variables can only assume whole number of values.



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Fill in the blank. For a discrete random variable, the sum of the probabilities of its possible values equals _____.

We're gonna look at some examples for discreet random variables. And just to recap, I'm just great. Random variables have whole numbers or countable numbers of possible outcomes. So some examples are gonna look like the number of people who have watched this video. So basically, the reason this is an example of a discreet a random variable is because you could not have 2.5 people that are watching this. It would have to be a whole number. So one person watched it and then a second purchase and watched it. Even if that person watch it multiple times, it's only one person, not half of a viewing, even if they watched only half of it. Another example of something like rolling the dice. Um, because even if you roll the dice, you're never gonna get 2.5, um, as a value when you roll the dice to either get a value between one and six another, people would be something like the number of books on a shelf, because that's something that can be counted. Even if you add another book that adding one whole book so you'd have five books, you go the story. You get two more books you're never gonna get again. Half of a book. So again, the big thing with discreet random variables is having that whole number or countable number, as your final outcomes about you.

You know this problem? We would like to know what rule of probability permits you to obtain any probability for discrete, random variable simply by knowing its probability. Distribution in this tool is a special addition rule, and that is called the Special Edition Room. And then what this tells us is that the probability event event and I just street random variable can be found by adding the probabilities of each individual event. That is what the special edition rule tells us, and that is what allows us find the probability simply by knowing the probability distribution.

This probably want to determine if we have a discreet, random variable or continuous random variable or in the variables, discreet. When it has a finite, we're accountable number of possible outcomes that can be listed Brandenburg was continuous when it has an uncountable number of possible outcomes represented by an interval on a number line. This problem X represents the amount of snow that fell in Nome, Alaska, last winter. This would be a continuous random variable because it has an uncountable number of values and will be best represented on the number line mhm.

Here on this problem. We want to determine whether or not we have a discrete or continuous random, very random variables, discreet when it has a finite or accountable number of possible outcomes that cumulus did random variables continuous when it has an uncountable number of possible outcome represented by an interval on the number line on this particular problem that represents the number of messages posted each month on a social networking site. And so this must be discreet, since it is a countable number. Yeah, Mhm. Mhm. Okay.


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