Answer:
Let's talk through this a one step at a time.
*Since f(x) is concave-up with its vertex on the x-axis, we know f(x) ≥ 0.
*We also know that when we shift a function's domain by a positive number, we shift the function left and when we shift a function's domain by a negative number, we shift the function right. So f(x-5) is f(x) shifted to the right by 5.
*At this point, f(x-5) has its vertex at (5,0).
*When we negate f(x-5), the parabola becomes concave down yet the vertex remains at (5,0). Now we're at -f(x-5). At this point we have -f(x-5)≤0 with a range (-∞,0]
*If we add 2 to create g(x)=2-f(x-5), then we have a concave down parabola with its vertex shifted up by 2, at (5,2). So, g(x) is concave down with its vertex at (5,2). Hence
The quotient in polynomial form is C. x² - x ₊ 1.
<h3>What is synthetic division?</h3>
In algebra, synthetic division is a method for manually performing Euclidean division of polynomials, with less calculation than long division.
The given problem is-
-3 | 1 2 -2 3
Using synthetic division method,
-3 | 1 2 -2 3
<u>| -3 3 3 </u>
|1 -1 1 6
So, the remainder from the calculation is 6 .
And the quotient polynomial is x² - x ₊ 1.
Hence, the correct answer for the given question is C. x² - x ₊ 1
More about Synthetic division method :
brainly.com/question/12642065
#SPJ1
B; you can graph the function on a graphic calculator or use algebra find each of the desired aspects
Answer:
$(x-100)
Step-by-step explanation:
- Phil entered the poker tournament with $100.
- He received a price of $x.
His net winnings from the tournament will be the price minus the entry fee.
Therefore:
Phil's net winnings=$(x-100)
Answer:
NO
Step-by-step explanation:
The changeability of a sampling distribution is measured by its variance or its standard deviation. The changeability of a sampling distribution depends on three factors:
- N: The number of observations in the population.
- n: The number of observations in the sample.
- The way that the random sample is chosen.
We know the following about the sampling distribution of the mean. The mean of the sampling distribution (μ_x) is equal to the mean of the population (μ). And the standard error of the sampling distribution (σ_x) is determined by the standard deviation of the population (σ), the population size (N), and the sample size (n). That is
μ_x=p
σ_x== [ σ / sqrt(n) ] * sqrt[ (N - n ) / (N - 1) ]
In the standard error formula, the factor sqrt[ (N - n ) / (N - 1) ] is called the finite population correction. When the population size is very large relative to the sample size, the finite population correction is approximately equal to one; and the standard error formula can be approximated by:
σ_x = σ / sqrt(n).
