Answer:
The balance is 
Step-by-step explanation:
we know that
The compound interest formula is equal to
where
A is the Final Investment Value
P is the Principal amount of money to be invested
r is the rate of interest in decimal
t is Number of Time Periods
n is the number of times interest is compounded per year
in this problem we have
substitute in the formula above
Answer:
b. Hannah is likely to be incorrect because 9 is not contained in the interval.
Step-by-step explanation:
Hello!
Hannah estimated per CI the difference between the average time that people spend outside in southern states and the average time people spend outside in northern states.
The CI is a method of estimation of population parameters that propose a range of possible values for them. The confidence level you use to construct the interval can be interpreted as, if you were to calculate 100 confidence interval, you'd expect that 99 of them will contain the true value of the parameter of interest.
In this example, the 99%CI resulted [0.4;8.0]hs
Meaning that with a 99% confidence level you'd expect the value of the difference between the average time people from southern states spend outside than the average time people from northern states spend outside is included in the interval [0.4;8.0]hs.
Now, she claims that people living in southern states spend 9 more hours outside than people living in northern states, symbolized μ₁ - μ₂ > 9
Keep in mind that if you were to test her claim, the resulting hypothesis test would be one-tailed
H₀: μ₁ - μ₂ ≤ 9
H₁: μ₁ - μ₂ > 9
And that the calculated Ci is tow-tailed, so it is not valid to use it to decide over the hypotheses pair. This said, considering that the calculated interval doesn't contain 9, it is most likely that Hannah's claim is incorrect.
I hope this helps!
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.
I will assume that 0.62 is an exponent then
amount left after t seconds = f(6) = 5 - 0.82(6)^0.62
= 2.51 gallons to nearest hundredth.
11. 9/20
12. 13/20
13. 12/20
that's if it doesn't have to be in percentage form
