The residual plot for a data set is shown. Based on the residual plot, which statement best explains whether the regression line
is a good model for the data set and why?
A.)The regression line is not a good model because there is no pattern in the residuals.
B.)The regression line is not a good model because only one point in the residual plot is on the x-axis.
C.)The regression line is a good model because the points in the residual plot are close to the x-axis and randomly spread around the x-axis.
D.)The regression line is a good model because the residuals approach 0 as x increases.
A.)The regression line is not a good model because there is no pattern in the residuals.
B.)The regression line is not a good model because only one point in the residual plot is on the x-axis.
C.)The regression line is a good model because the points in the residual plot are close to the x-axis and randomly spread around the x-axis.
D.)The regression line is a good model because the residuals approach 0 as x increases.
1 answer:
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Answer:
We have that .
So
However, b is a probability, which means that it cannot be negative. So no, P(A ∩ B) cannot be 0.5. It can, at most, be 0.3.
Step-by-step explanation:
Event A:
Probability that a student has a Visa card.
Event B:
Probability that the student has a MasterCard.
We have that:
In which a is the probability that a student has a Visa card but not a MasterCard and is the probability that a student has both these cards.
By the same logic, we have that:
In this problem, we have that:
(a) Could it be the case that P(A ∩ B) = 0.5?
We have that .
So
However, b is a probability, which means that it cannot be negative. So no, P(A ∩ B) cannot be 0.5. It can, at most, be 0.3.