Answer:
C = 18
Step-by-step explanation:
Put the numbers in place of their symbols and do the arithmetic. The diagram is showing you r = 3.
C = 2πr
C = 2(3)(3) . . . . . using 3 for π, as directed
C = 18
First you take 15 and divide it by 5. That leaves you with 3. You also need to decide the x’s so you subtract the exponents (2-3=-1)
so you now have (y^10z^7)/(y^4z^10)3x^-1
Next we will divide the y’s.
Again, subtract the exponents (10-4=6) to get y^6
Now we have (3x^-1)(y^6) (z^7/z^10)
Last is z, we subtract the exponents again (7-10=-3) and get z^-3
Our answer is (3x^-1)(y^6)(z^-3)
Answer:
A. N and Q
Step-by-step explanation:
Look at the order of the triangles: MNL and PQR
MNL and PQR
They are both ordered in the middle, whereas others like M and Q are not. For example, M is ordered first and Q is middle.
N and Q match - The correct letters will always align in the order
90 because there are 5 time as many candies with nuts as candies without nuts and there is 18 so it will be 18×5=90
The possible outcomes of a random experiment and the probability of each outcome is called "a Probability Distribution."
<h3>What is a Probability Distribution?</h3>
A probability is a statistical formula that indicates all of the potential values and probability distributions for a random variable within a specified range.
Some characteristics regarding the Probability Distribution are-
- The range will be bounded by the minimum and greatest possible values, but the precise location of the possible value just on probability distribution relies on a number of factors.
- These variables include the mean (average), standard deviation, skewness, & kurtosis of the distribution.
- Although other regularly used probability distributions exist, the normal distribution, called "bell curve," is perhaps the most common.
- Typically, the technique of generating data for a phenomenon will influence its probability distribution. This is known as the probability density function.
- Likelihood distributions can also be used to generate cumulative distribution functions (CDFs), that cumulatively build up the probability of occurrences and always begin at zero and end at 100%.
To know more about Probability Distribution, here
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