Normal and uniform probability distribution.

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  • #1
Kinetica
88
0
Am I right? Thank you!Problem:
A cereal brand of cereal claims that the mean number of raisins in each box is 80 with a standard deviation of 6. If the raisins are normally distributed, what are the chances that an arbitrary box has

1) fewer than 70 raisins and
2) more than 90 raisins.

What should be your answers if the raisins are uniformly distributed.

Solution:

{70-80}/{6}=-1.67; we disregard the negative sign in order to find the value in the table. The value is 0.9520. 1-0.9520=0.048 or 4.80% chance that an arbitrary box has fewer than 70 raisins.

{90-80}/{6}=1.67, which is also 0.9525.
1-0.9520=0.048 or 4.80% chance that an arbitrary box has more than 90 raisins. If the raisins are uniformly distributed, the probability that an arbitrary box has fewer than 70 raisins is 1/80 * 70=0.875 or 87.5%.
Probability that there are more than 90 raisins is 0..
 
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  • #2
Kinetica said:
Am I right? Thank you!


Problem:
A cereal brand of cereal claims that the mean number of raisins in each box is 80 with a standard deviation of 6. If the raisins are normally distributed, what are the chances that an arbitrary box has

1) fewer than 70 raisins and
2) more than 90 raisins.

What should be your answers if the raisins are uniformly distributed.

Solution:

{70-80}/{6}=-1.67; we disregard the negative sign in order to find the value in the table. The value is 0.9520. 1-0.9520=0.048 or 4.80% chance that an arbitrary box has fewer than 70 raisins.

{90-80}/{6}=1.67, which is also 0.9525.
1-0.9520=0.048 or 4.80% chance that an arbitrary box has more than 90 raisins.


If the raisins are uniformly distributed, the probability that an arbitrary box has fewer than 70 raisins is 1/80 * 70=0.875 or 87.5%.
Probability that there are more than 90 raisins is 0..

You need to find the uniform distribution with mean 80 and variance 62= 36. Do you know the formulas for mean and variance of a uniform distribution on the interval [a,b]? Once you have determined a and b the rest is straightforward.

RGV
 

FAQ: Normal and uniform probability distribution.

What is a normal probability distribution?

A normal probability distribution, also known as a Gaussian distribution, is a type of probability distribution that is commonly used to describe continuous random variables. It is characterized by a bell-shaped curve and is symmetrical around the mean. The majority of the data falls within one standard deviation of the mean, making it a useful tool for statistical analysis.

What is a uniform probability distribution?

A uniform probability distribution is a type of probability distribution where all outcomes have an equal chance of occurring. This means that the probability of any given outcome is the same as any other outcome. It is often used to model situations where all outcomes are equally likely, such as rolling a fair die or drawing a card from a deck.

What is the difference between a normal and uniform distribution?

The main difference between a normal and uniform distribution is the shape of their probability curves. While a normal distribution has a bell-shaped curve, a uniform distribution has a flat, rectangular curve. Additionally, a normal distribution is characterized by a mean and standard deviation, while a uniform distribution has a defined minimum and maximum value.

How are normal and uniform distributions used in science?

Normal and uniform distributions are used in a variety of scientific fields, including statistics, physics, and biology. In statistics, they are used to model and analyze continuous data. In physics, they are used to describe the behavior of random variables, such as the velocity of particles. In biology, they are used to model population growth and genetic variation.

What is the central limit theorem and how does it relate to normal distribution?

The central limit theorem states that when independent random variables are added, their sum tends to follow a normal distribution, regardless of the distribution of the individual variables. This means that even if the underlying data is not normally distributed, the sample means will tend to follow a normal distribution. The central limit theorem is important in statistics because it allows us to make inferences about a population based on a sample.

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