If someone tells us that a random variable is described by a binomial or poisson distribution, it becomes a very simple matter (presuming we know the key parameters) to be able to then predict the likelihood that any particular combination of values will be seen in a dataset containing that variable. sometimes, though, we don’t know what kind of distribution our variable will fit, or we don’t know enough to be able to identify the key parameters needed to make accurate predictions. part of the analysis we routinely do with datasets is to identify whether or not any of the variables included are binomial or poisson in nature. discuss why it can be to do this? discuss how you would identify which variables in the dataset would benefit from such analysis. does it provide benefit to name the distribution that applies even if you can't precisely identify the needed parameters for that distribution? use specific examples in your post: provide some examples of variables that might be in your data that would be considered binomial or poisson. these examples should demonstrate that you understand how to recognize that a variable fits one of these distributions.

In tossing one coin 10 times, what are your chances for tossing a head? a tail? 2. in tossing one coin 100 times, what are your chances for tossing a head? a tail? 3. in tossing one coin 200 times, what are your chances for tossing a head? a tail? deviation = ((absolute value of the difference between expected heads and observed heads) + (absolute value of the difference between expected tails and observed tails)) divided by total number of tosses. this value should always be positive. 4. what is the deviation for 10 tosses? 5. what is the deviation for the 100 tosses? 6. what is the deviation for 200 tosses? 7. how does increasing the total number of coin tosses from 10 to 100 affect the deviation? 8. how does increasing the total number of tosses from 100 to 200 affect the deviation? 9. what two important probability principles were established in this exercise? 10. the percent of occurrence is the obtained results divided by the total tosses and multiplied by 100%. toss the coins 100 times and record your results. calculate the percent occurrence for each combination. percent head-head occurrence: percent tail-tail occurrence: percent head-tail occurrence: