1) there were 32 bales added
2) 8 hours
3) 74 cards
4) 11 packs
5) $57
6) ummm...$3?
7) began with 12
8) $5 per candy bar
9) 44 students on each bus
10) $77 spent on baseball gear
Answer: yes, because it is useful to describe changes in the behavior of the thing we are describing.
Step-by-step explanation:
A piecewise function f(x) is a function that is "different" depending on the range of the values of x, this is for example:
y = x -----> if x < 0
y = x^2 ----- if x ≥ 0
This is used to represent changes in the behavior of the thing we are describing.
For example, suppose an object that is moving with constant velocity, the velocity graph will be a constant line.
Now, the object suddenly, at a time t = t0 accelerates to a constant acceleration A, now the graph of the velocity is a linear graph with slope equal to A.
So this situation can be represent this with a piecewise function:
V(t) = c -----> if t < t0
V(t) = c + A*t -----> if t ≥ t0
So the answer is yes, we can.
Answer:
7.5 square units
Step-by-step explanation:
Base of the triangle = 5 units
Height of the triangle = 3 units
Area of the triangle
The probability that all five end up in alphabetical order is; 1/120.
<h3>What is the probability that the rack ends up in alphabetical order?</h3>
To evaluate the given probability; first, the number of possible arrangements is;
17P5 = 742,560 possible arrangements.
However, the chance of an alphabetical order arrangement in each case is; 1 out of 5! possible arrangements.
Hence, we have that the number of possible alphabetical arrangement is; (1/120) × 742,560 = 6188.
Hence, the required probability is; 6188/742,560 = 1/120
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4,034.06 ,5,965.94 are confidence interval for the population average wages at the factory.
What is confidence interval estimation?
Your estimate's mean plus and minus the range of that estimate's fluctuation is called a confidence interval.
If you repeat your test, you can expect your estimate to fall between these numbers with a reasonable degree of certainty. Another term for probability in statistics is confidence.
The formula for confidence interval estimation is:
μ = M ± Z(sM)
where:
M = sample mean
Z = Z statistic determined by confidence level
sM = standard error = √(s2/n)
M = 50000
Z = 2.58
sM = √(30002/64) = 375
μ = M ± Z(sM)
μ = 50000 ± 2.58*375
μ = 50000 ± 965.94
μ = 4,034.06 ,5,965.94
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