Explanation:
Marginal distribution: This distribution gives the probability for each possible value of the Random variable ignoring other random variables. Basically, the values of other variables is not considered in the marginal distribution, they can be any value possible. For example, if you have two variables X and Y, the probability of X being equal to a value, lets say, 4, contemplates every possible scenario where X is equal to 4, independently of the value Y has taken. If you want the probability of a dice being a multiple of 3, you are interested that the dice is either 3 or 6, but you dont care if the dice is even or odd.
Conditional distribution: This distribution contrasts from the previous one in the sense that we are restricting the universe of events to specific condition for other variable, making a modification of our marginal results. If we know that throwing a dice will give us a result higher than 2, then to in order to calculate the probability of the dice being a multiple of 3 using that condition, we have two favourable cases (3 and 6) from 4 total possible results (3,4,5 and 6) discarding the impossible values (1 and 2) from this universe since they dont match the condition given (note that the restrictions given can also reduce the total of favourable cases).
The joint distribution calculates the probabilities for two different events (related to two different random variables) occuring simultaneously. If we want to calculate the joint probability of a dice being multiple of 3 and greater than 2 at the same time, our possible cases in this case are 3 and 6 from 6 possible results. We are not discarding 1 or 2 as possible results because we are not assuming, that the dice is greater than 2, that is another condition that we should met in the combination of events.
Answer:
A. Yes, the result is a binomial probability distribution.
Step-by-step explanation:
The experiment above depicts a binomial probability distribution because the 4 required conditions are met :
1.) The distribution is independent as the possible outcome of each trial is the same.
2.) There are two possible categories and the result of each trial is one of two outcomes : Yes or No
3.) The number of observation is fixed at sample size of 5500
4.) The probability of success and failure of each trial is the same for all trials in the sample.
Hence, we can conclude that the experiment depicts a binomial probability distribution.
Plug in numbers in for x.
If you plug in 0 then you get 1. 1/0 is undefined
If you plug in 1 then you get 6. 6/1 is 6
If you plug in 2 then you get 11. 11/2 is 5.5
If you plug in 3 then you get 16. 16/3 is 5.3 repeating
If you plug in 4 then you get 21. 21/4 is 5.25
If you plug in 5 then you get 26. 26/5 is 5.2
It would only be proportional if you got the same answer after dividing each time.
<em><u>Option E</u></em>
<em><u>The expression show how much pizza Marcus ate in total is:</u></em>
<em><u>Solution:</u></em>
Given that,
Marcus ate half of a pizza on Monday night
He then ate one third of the remaining pizza on Tuesday
Therefore,
Remaining is given as:
He then ate one third of the remaining pizza on Tuesday
Therefore,
<em><u>Thus expressions show how much pizza Marcus ate in total is:</u></em>
Total = monday night + tuesday
Thus Option E is correct
Answer:
a formula that defines each term of a sequence using preceding terms
Step-by-step explanation:
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