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# Can You Find ... Random Variable Edition

### Why do this problem?

Random variables are a critical idea in the development of probability: they take us from just describing probabilities of things happening to assigning numerical values to outcomes and asking about the probabilities of obtaining a particular outcome. This is an example of a "can you construct..." problem: rather than giving students a set of random variables and asking them to calculate their means and variances, we ask students to construct random variables having various properties. This is likely to lead to a deeper appreciation for aspects of random variables and the building of connections between different examples of random variables they have already seen.

This problem has been framed in the context of discrete random variables. It could equally well be posed for continuous random variables, making the obvious changes.

### Possible approach

This could be used as a consolidation activity once students have already seen a variety of random variables. For example, they may wish to make use of properties of standard distributions such as the binomial, Poisson or geometric distributions when constructing examples. Alternatively, it could be used earlier on once students have a basic competence at calculating means and variances of random variables, in order to widen their conception of what a random variable could look like.

The questions ask for several examples of random variables which satisfy the conditions, so that students are encouraged to think beyond their first answer. There are several ways of approaching the problems. For example, students might start with the values that $X$ can take, and then try to assign probabilities to them so that the conditions are fulfilled. Alternatively, they may start with a random variable they already know and consider how they might modify it.

Sharing ideas for part (a) with the whole class after students have had some time to think about it on their own is likely to lead to more creative solutions for the subsequent parts.

For part (c), students may well find it hard to find such an example; the question does suggest that such an example exists, so students will have to think about what they know about infinite series. Whether or not they succeed, the thought processes will expand their thinking: they will have to consider random variables which can take infinitely many values and consider how probabilities and means will work in this context. (Note that this part excludes the possibility of random variables taking both positive and negative values. Otherwise one could have a random variable $X$ which is symmetrical about $X=0$, but for which $\mathrm{E}(|X|)=\infty$. In such a case, we say that $X$ does not have a mean: the sum $\sum_{n=-\infty}^\infty n.\mathrm{P}(X=n)$ is not well defined, but that is another story.) Part (c) may well give ideas for how to tackle part (d).

### Key questions

### Possible extension

### Possible support

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Challenge Level

- Problem
- Getting Started
- Student Solutions
- Teachers' Resources

Random variables are a critical idea in the development of probability: they take us from just describing probabilities of things happening to assigning numerical values to outcomes and asking about the probabilities of obtaining a particular outcome. This is an example of a "can you construct..." problem: rather than giving students a set of random variables and asking them to calculate their means and variances, we ask students to construct random variables having various properties. This is likely to lead to a deeper appreciation for aspects of random variables and the building of connections between different examples of random variables they have already seen.

This problem has been framed in the context of discrete random variables. It could equally well be posed for continuous random variables, making the obvious changes.

This could be used as a consolidation activity once students have already seen a variety of random variables. For example, they may wish to make use of properties of standard distributions such as the binomial, Poisson or geometric distributions when constructing examples. Alternatively, it could be used earlier on once students have a basic competence at calculating means and variances of random variables, in order to widen their conception of what a random variable could look like.

The questions ask for several examples of random variables which satisfy the conditions, so that students are encouraged to think beyond their first answer. There are several ways of approaching the problems. For example, students might start with the values that $X$ can take, and then try to assign probabilities to them so that the conditions are fulfilled. Alternatively, they may start with a random variable they already know and consider how they might modify it.

Sharing ideas for part (a) with the whole class after students have had some time to think about it on their own is likely to lead to more creative solutions for the subsequent parts.

For part (c), students may well find it hard to find such an example; the question does suggest that such an example exists, so students will have to think about what they know about infinite series. Whether or not they succeed, the thought processes will expand their thinking: they will have to consider random variables which can take infinitely many values and consider how probabilities and means will work in this context. (Note that this part excludes the possibility of random variables taking both positive and negative values. Otherwise one could have a random variable $X$ which is symmetrical about $X=0$, but for which $\mathrm{E}(|X|)=\infty$. In such a case, we say that $X$ does not have a mean: the sum $\sum_{n=-\infty}^\infty n.\mathrm{P}(X=n)$ is not well defined, but that is another story.) Part (c) may well give ideas for how to tackle part (d).

- What possible strategies are there for finding such a random variable?
- If you restrict $X$ to only taking certain values, does that help?
- Can you make use of familiar random variables to solve this problem?

- For the first parts, can you find random variables satisfying the conditions for which $\mathrm{P}(X=2)=0$?
- For the first parts, can you find a random variable $X$ which takes the smallest possible number of values and still satisfies the conditions?

- If you restrict the random variable to a small number of possible values, can you write down explicit equations relating the probabilities of $X$ taking these values and the mean and variance of $X$?

How is the length of time between the birth of an animal and the birth of its great great ... great grandparent distributed?

This pilot collection of resources is designed to introduce key statistical ideas and help students to deepen their understanding.