Answer:
%
Step-by-step explanation:
We have that 
of the eggs are cracked, so 
of the eggs are not cracked.
Recall that 
. This means that 
.
Rounding this to the nearest whole percent gives %.
Answer:
900 yd²
Step-by-step explanation:
You can draw the pattern that would need to be folded to make the figure, then find the area of that pattern (or "net"). It consists of a rectangle 10 yards wide and 60 yards long, together with another that is 20 yards long and 15 yards wide. The total area is the sum of these ...
... area = (60 yd)(10 yd) + (20 yd)(15 yd)
... = 600 yd² +300 yd²
... = 900 yd²
Answer:
x < -12
Step-by-step explanation:
Given the inequality
-2(x + 3) < 6 - x
Expand
-2x - 6 < 6 - x
Collect the like terms;
-2x + x < 6 + 6
-x < 12
Multiply through by -1
-1(-x) < -1(12)
x < -12
For the given probability mass function of X, the mean is 3.5 and the standard deviation is 1.708.
- A discrete random variable X's probability mass function (PMF) is a function over its sample space that estimates the likelihood that X will have a given value. f(x)=P[X=x].
- The total of all potential values for a random variable X, weighted by their relative probabilities, is known as the mean (or expected value E[X]) of that variable.
- Mean(μ) = 1(1/6) + 2(1/6) + 3(1/6) + 4(1/6) + 5(1/6) + 6(1/6).
- Mean(μ) = (1+2+3+4+5+6)/6
- Mean(μ) = 21/6
- Mean(μ) = 3.5
- The square root of the variance of a random variable, sample, statistical population, data collection, or probability distribution represents its standard deviation. It is denoted by 'σ'.
- A random variable's variance (or Var[X]) is a measurement of the range of potential values. It is, by definition, the squared expectation of the distance between X and μ. It is denoted by 'σ²'.
- σ² = E[X²]−μ²
- σ² = [1²(1/6) + 2²(1/6) + 3²(1/6) + 4²(1/6) + 5²(1/6) + 6²(1/6)] - (3.5)²
- σ² = [(1² + 2²+ 3² + 4²+ 5²+ 6²)/6] - (3.5)²
- σ² = [(1 + 4 + 9 + 16 + 25 + 36)/6] - (3.5)²
- σ² = (91/6) - (3.5)²
- σ² = 15.167-12.25
- σ² = 2.917
- σ = √2.917
- Standard deviation (σ) = 1.708
To learn more about variance, visit :
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