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.

2/3x + 7 = 29

step-by-step explanation:

two thirds = 2/3

plus a number = x

at least 29 = 29

there for you should get 2/3x + 7 = 29

-2x + 7 = 7

subtract 7 from both sides.

-2x = 0

divide both sides by -2.

x = 0

55.44 inches

step-by-step explanation:

part a. assuming a perfectly linear relationship, we can find the slope from the first two data points.

slope = m = (change in rainfall)/(change in years)

= (61.80 -62.33)/(2005 -2004) = -0.53/1 = -0.53

then the point-slope form of the equation of the line can be written as

y = m(x -h) +k . . . . . m = -0.53, (h, k) = (0, 62.33)

y = -0.53x +62.33 . . . x = years after 2004

part b. in 2017, x = 2017 -2004 = 13. then the predicted rainfall is

y = -0.53·13 +62.33 = 55.44 . . inches

the predicted rainfall in 2017 is 55.44 in.

c = 0.429t

step-by-step explanation:

the constant rate at which carlos harvests cassava is 35 minutes per 15 cassavas 15/35 = 0.429 cassava per minute

therefore, an equation that models the number of cassava (c) that harvest carlos in a time (t) is the following:

where t is the time in minutes.

this means that when 35 minutes have elapsed 15 cassava have been harvested, at 70 minutes 30 cassava have been harvested and so on.