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.
The average ocean depth is 3.7 × 103 m, and the area of the oceans is 3.6 × 1014 m2. What is the total volume of the ocean in liters? (One cubic meter contains 1000 liters. Round your answer to one decimal place.)
Answer:
a.the goodness of fit for the estimated multiple regression equation increases.
Step-by-step explanation:
As the value of the multiple coefficient of determination increases,
a. the goodness of fit for the estimated multiple regression equation increases.
As we know that the coefficient of determination measures the variability of response variable with the help of regressor. As we know that if the value of the coefficient of determination increases strength of fit also increases.
Answer:
A group of seven friends each received one-half of a pound ofcandy. How much candy did they receive total?✓
0.5 pounds x 7= 3.5 poundsStep-by-step explanation:
1/3 - 1/4 = 4/12 - 3/12 = 1/12....Fluffy's tail is 1/12 meters longer then Fireball's tail
