<h3>
Answer: 5 - 4i (choice A)</h3>
===================================================
Work Shown:
x = the other number
(5+4i)*x = 41
x = 41/(5+4i)
x = 41*(5-4i)/( (5+4i)*(5-4i) ) ..... see note below
x = 41*(5-4i)/( 41 )
x = (41/41)*(5-4i)
x = 5 - 4i
As a way to check, (5+4i)*(5-4i) = 5^2+4^2 = 25+16 = 41
The rule used is (a-bi)(a+bi) = a^2 + b^2
-----------
Note: I multiplied top and bottom by (5-4i) to get rid of the imaginary term in the denominator.
The answer is minus 666 that’s the answer byee
Answer: 3/8
Step-by-step explanation:
When three coins are flipped, we get 8 total outcomes: (HHH,HHT,HTH,HTT,THH,THT,TTH,TTT)
Two tails and one head is (HTT, TTH, THT)
Probability = number of favorable outcomes/ number of total outcomes
Number of Favorable outcomes = 3
Number of Total outcomes= 8
Therefore, probability of getting 2 tails and one head
= 3/8
We need the half-life of C-14 which is 5,730 years.
Now, we will need a half-life equation:
elapsed time = half-life * log (bgng amt / ending amt) / log 2
We'll say beginning amount = 100 and ending amount = 41
elapsed time = 5,730 * log (100/41) / log 2
elapsed time = 5,730 * log (
<span>
<span>
<span>
2.4390243902
</span>
</span>
</span>
) / 0.30102999566
elapsed time = 5,730 * 0.38721614327 / 0.30102999566
elapsed time =
<span>
<span>
</span></span><span><span><span>5,730 * 1.2863041851
</span>
</span>
</span>
<span>elapsed time = 7,370.523 years
Source:
http://www.1728.org/halflife.htm </span>
There could be a strong correlation between the proximity of the holiday season and the number of people who buy in the shopping centers.
It is known that when there are vacations people tend to frequent shopping centers more often than when they are busy with work or school.
Therefore, the proximity in the holiday season is related to the increase in the number of people who buy in the shopping centers.
This means that there is a strong correlation between both variables, since when one increases the other also does. This type of correlation is called positive. When, on the contrary, the increase of one variable causes the decrease of another variable, it is said that there is a negative correlation.
There are several coefficients that measure the degree of correlation (strong or weak), adapted to the nature of the data. The best known is the 'r' coefficient of Pearson correlation
A correlation is strong when the change in a variable x produces a significant change in a variable 'y'. In this case, the correlation coefficient r approaches | 1 |.
When the correlation between two variables is weak, the change of one causes a very slight and difficult to perceive change in the other variable. In this case, the correlation coefficient approaches zero
