Answer:
Independent events
Step-by-step explanation:
Given that:
Ramiro draws a marble from a jar without replacement and then flips a coin
Let 
be the event that Ramiro draws a marble without replacement and;
Let 
be the event of flipping a coin.
Let's have an analogy so that we can better understand the concept of independent and dependent events.
Consider a random experiment in which a marble is drawn from a jar without replacement and a fair coin is flipped together.
The event does not in any way affect the event of a head or a tail showing up in a flip of a coin.
Therefore, we say that and are independent events.
Suppose the event affects or influence the event , then we can say they are dependent events.
Answer:
rise/run = 2/-4 or it is rise/run = -2/4
Answer:
837,000
Step-by-step explanation:
If a number is above 5 round up, if it is below 4 round down
Answer:
Morning's average rate = 50 mph, and Afternoon's average rate = 25 mph.
Step-by-step explanation:
Suppose he drove 150 miles for X hours, then his average rate in the morning was (150/X) mph.
Given that he spent 5 hours in driving.
And he drove 50 miles for (5-X) hours, then his average rate in the afternoon was 50/(5-X) mph.
Given that his average rate in the morning was twice his average rate in the afternoon.
(150/x) = 2 * 50/(5-x)
150/x = 100/(5-x)
Cross multiplying terms, we get:-
150*(5-x) = 100*x
750 - 150x = 100x
750 = 100x + 150x
750 = 250x
x = 750/250 = 3.
It means he spent 3 hours in the morning and 2 hours in the afternoon.
So morning's average rate = 150/3 = 50 mph.
and afternoon's average rate = 50/(5-3) = 25 mph.
Answer:
D. No, because the sample size is large enough.
Step-by-step explanation:
The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".
Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".
If the sample size is higher than 30, on this case the answer would be:
D. No, because the sample size is large enough.
And the reason is given by The Central Limit Theorem since states if the individual distribution is normal then the sampling distribution for the sample mean is also normal.
From the central limit theorem we know that the distribution for the sample mean 
is given by:
If the sample size it's not large enough n<30, on that case the distribution would be not normal.
