62 percent of 80 is more
Step-by-step explanation:
the 62 percent of 80 comes out to 49.6 people and the other one comes out to 42 people
i dont know how to explain how to do it
Y - 11 = 3(x + 4)....distribute thru the parenthesis
y - 11 = 3x + 12....add 11 to both sides
y = 3x + 12 + 11...simplify
y = 3x + 23 <==
The correct answer is C) 1/3 and (0, 1)
The y-intercept part is somewhat simple. In order for it to be a y-intercept, the x value of the ordered pair must be 0. That is only true in B and C, therefore they are the only possible answers.
To find the slope, we must choose two points on the line and use the slope formula. We can start by using the y-intercept (0, 1) and also (3, 2). Now we use the slope formula.
m = (y2 - y1)/(x2 - x1)
In this equation m is equal to slope, the first point is (x1, y1) and the second point is (x2, y2)
m = (y2 - y1)/(x2 - x1)
m = (2 - 1)/(3 - 0)
m = 1/3
Now knowing the slope, you can match this with answer C.
Answer:
- scientific or graphing calculator
- TVM solver
- spreadsheet
Step-by-step explanation:
For many future-value calculations, a scientific calculator is a sufficient tool. Of course, one must know the appropriate formula to use.
A good alternative when the calculation is a little messy is a TVM solver or special-purpose financial calculator. I prefer this tool because it requires little more than entering numbers in to the right slots.
Most modern spreadsheet programs and apps come with financial formulas built in. So, they, too, can be easy tools to use for calculating future value. These are especially handy when a number of scenarios need to be explored. (I always have to look up the formulas to see which one is appropriate and what its inputs are. So, I find a spreadsheet less useful for a simple calculation.)
A complex mathematical topic, the asymptotic behavior of sequences of random variables, or the behavior of indefinitely long sequences of random variables, has significant ramifications for the statistical analysis of data from large samples.
The asymptotic behavior of the sample estimators of the eigenvalues and eigenvectors of covariance matrices is examined in this claim. This work focuses on limited sample size scenarios where the number of accessible observations is comparable in magnitude to the observation dimension rather than usual high sample-size asymptotic .
Under the presumption that both the sample size and the observation dimension go to infinity while their quotient converges to a positive value, the asymptotic behavior of the conventional sample estimates is examined using methods from random matrix theory.
Closed form asymptotic expressions of these estimators are obtained, demonstrating the inconsistency of the conventional sample estimators in these asymptotic conditions, assuming that an asymptotic eigenvalue splitting condition is satisfied.
To learn more about asymptotic behavior visit:brainly.com/question/17767511
#SPJ4
