The second system of equations,

is correct.
We know that Bethany's age is x. Since Laura is 13 years older, her age is x+13. The product of their ages is equal to twice Amanda's age, and Amanda's age is y. This gives us:
x(x+13) = 2y
Using the distributive property, we have
x²+13x=2y
Dividing everything by 2 (to isolate y), we have:
x²/2 + (13/2)x = y
If we take 20 years off of Bethany's age, it is now represented as x-20. Taking 20 years off of Laura's age would be (x+13-20) or x-7. The product of their ages now is equal to David's age; David is 11 years older than Amanda, so his age is y+11. This gives us:
(x-20)(x-7)=y+11
Multiplying the binomials we have:"
x*x - 7*x - 20*x - 20(-7) = y+11
x²-7x-20x--140=y+11
x²-27x+140=y+11
To isolate y, subtract 11 from both sides:
x²-27x+140-11 = y+11-11
x²-27x+129 = y
Answer:
68.4
Step-by-step explanation:
Divide 342 by 5
Answer:
if you mean the power of these letters then the answer is
x^2,y^2 and z
as they are common in both lists
Answer:
Thats FALSE.
The concept CORRELATION not automatically means CAUSATION.
Step-by-step explanation:
Previous concepts
The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.
And in order to calculate the correlation coefficient we can use this formula:
Solution to the problem
Some useful concepts are:
Correlation is a statistical measure described with a number that describes the size and direction of a relationship between two or more variables. The correlation between variables, NOT mean that the change in one variable is the cause of the change in the values of the other variable.
Causation is a term refered to one event as the result of the occurrence of the other event for example, there is a causal relationship between event A and B like a cause and effect. One important thing to mention here is that the causation is not an easy concept to measure.
"The difference between the two types of relationships are easy to identify since one can be measured with a numerical value and the other no (Here is one example: smoking causes an increase in the risk of developing lung cancer), or it can correlate with another ( smoking is correlated with alcoholism, but it does not cause alcoholism)".
So is clear that the concept CORRELATION not automatically means CAUSATION.