We have 89 ounces of 47% iodine.
We will add "x" ounces of 14% iodine to get a 29% iodine solution.
89 * .47 + .14x = .29 * (89 + x)
41.83 + .14x = 25.81 + .29x
16.02 = .15x
x = 106.80
So, we must add 106.80 ounces of 14% iodine to get a 29% iodine solution.
(for a total of 195.80 ounces)
Source:
http://www.1728.org/mixture.htm
Answer:
Population of dice mice after one year = 2000
Population of wood rats after one year = 1000
The population of deer mice is growing faster than the popular of wood rats
Step-by-step explanation:
The expression for population = dN/dt = rN
Upon integration N = rN²/2
Therefore for population N = 200 and r= 0.1
N after one year = (0.1 x 200²)/ 2 = 2000
Therefore for population N = 100 and r= 0.2
N after one year = (0.2 x 100²)/ 2 = 1000
Hence the population of deer mice is growing faster than the popular of wood rats
We'll represent the distance as d and the time as t.
d= 3(t+7) because Gilda is 7 minutes late when travelling 3 mph
d= 4(t-5) because Gilda is 5 minutes early at 4 mph
d= 3t + 21
d= 4t -20
3t +21 =4t -20
41=t
d= 3(41) +21= 144
The distance is 144 miles.
Answer:
Hypothesis
Step-by-step explanation:
When people make researches, they make a statement and then go further to either price that statement wrong or right.
Once the statement is proven right, either by virtue of field work or laboratory work, it becomes a theory.
That unproven statement that can either be true or wrong, but baseless because it is yet to be proven is called HYPOTHESIS
Answer:
I believe it is 0.5
Step-by-step explanation:
If you flip a normal coin (called a “fair” coin in probability parlance), you normally have no way to predict whether it will come up heads or tails. Both outcomes are equally likely. There is one bit of uncertainty; the probability of a head, written p(h), is 0.5 and the probability of a tail (p(t)) is 0.5. The sum of the probabilities of all the possible outcomes adds up to 1.0, the number of bits of uncertainty we had about the outcome before the flip. Since exactly one of the four outcomes has to happen, the sum of the probabilities for the four possibilities has to be 1.0. To relate this to information theory, this is like saying there is one bit of uncertainty about which of the four outcomes will happen before each pair of coin flips. And since each combination is equally likely, the probability of each outcome is 1/4 = 0.25. Assuming the coin is fair (has the same probability of heads and tails), the chance of guessing correctly is 50%, so you'd expect half the guesses to be correct and half to be wrong. So, if we ask the subject to guess heads or tails for each of 100 coin flips, we'd expect about 50 of the guesses to be correct. Suppose a new subject walks into the lab and manages to guess heads or tails correctly for 60 out of 100 tosses. Evidence of precognition, or perhaps the subject's possessing a telekinetic power which causes the coin to land with the guessed face up? Well,…no. In all likelihood, we've observed nothing more than good luck. The probability of 60 correct guesses out of 100 is about 2.8%, which means that if we do a large number of experiments flipping 100 coins, about every 35 experiments we can expect a score of 60 or better, purely due to chance.