It would be <span>2.416667 yep </span>
Answer:
20 loads
Step-by-step explanation:
2000 divided by 100 is 20
In a positive correlation, both variables increase together. An example of this is a company's profit's relationship with its sales. The greater the sales, the greater the profit.
A negative correlation is where one variable decreases with increase in the other. An example is the time taken to reach somewhere and the speed traveled at. The faster you move, the less the time.
No correlation means the two variables are not related. An example of this is as you get older, it does not mean you will get better grades.
Inverse Variation is shown through the formula y=k/x
plug in the given values and solve for k: 3=k/5 so k=15
Answer: y=15/x
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.
