Respond to two peers relating to The Normal Distribution

I’m working on a Statistics exercise and need support.

THIS IS A STATISTICS COURSE.

Please respond to two peers.

200 word per peer

Remember this is NOT a Paper, but a Discussion Post. Each peer will have different question to answer.

Please watch video.

TURNITIN Score must be below 12%

APA In Text Citation Please.

Watch the video, then read below.

https://mediaplayer.pearsoncmg.com/assets/_jGvVe5Jbkm44I2D0TWEMnsz6A6Y7fin

This video explains the normal distribution via the binomial distribution: The distribution of the number of heads thrown on 20 coins approximates the normal. This is used to explain that the normal distribution is the mathematical consequence of adding up a large number of random events. Some examples are given of normal distributions in the natural world (mass of ants) and social world (age of marathon runners) and explained in terms of these phenomena resulting from the aggregation of random events.

Respond to peer one regarding: Do natural phenomena such as hemoglobin levels or the weight of ants really follow a normal distribution? If you add up a large number of random events, you get a normal distribution.

Peer One: Ebenezer Tetteh-Ahinakwa post below

Do natural phenomena such as hemoglobin levels or the weight of ants really follow a normal distribution? If you add up a large number of random events, you get a normal distribution.

Most natural phenomena follow a normal distribution. This is because natural phenomena is influenced by many factors, for example, height can be influenced by genes, environmental factors, sex, among others. And as Bennett, Briggs, and Triola (2014) state, any quantity that is influenced by many factors is likely to follow a normal distribution.

Also, for most natural phenomena data sets the data values are clustered near the mean, giving the distribution a nearly normal peak, and they are also spread evenly around the mean making the distribution symmetric. This means that large deviations from the mean become increasingly rare producing the tapering tails for the distribution (Bennett, Briggs, & Triola, 2014).

There is also the aspect of the Central Limit Theorem which shows that the mean of a sufficiently large number of independent random variables, each with finite mean and variance, will be approximately normally distributed. As we have seen, natural phenomena tends to be influenced by a sufficiently large number of independent random variables, each with finite mean and variance. As a result, the occurring data sets from these independent random variables will be normally distributed. However, it is important to remember that no data set can ever perfectly match a theoretical distribution. This is because a data set will sometimes deviate or become skewed making it unable to be a perfect match for a theoretical distribution.

References

Bennett, J., Briggs, W. & Triola, M. (2014). Statistical reasoning for everyday life (4th ed.). Boston, MA: Pearson Education, Inc.

RESPOND to peer two Kyasha Ware regarding: How large a number makes a normal distribution?

How large a number makes a normal distribution?

When it comes to the normal distribution it is the most common and useful distribution to use in statistics in the central limit theorem. Also, known as the bell curve, the normal distribution has a symmetrical shape in which both sides are fifty percent even. This happens because the highest peak will decrease on both sides to create a format. In normal distributions, you can develop your mean, median, mode, standard deviation, and the empirical rule or also known as 68-95-99.7. All of these value results will show you where the height and width is to contrast the rest of the sample question. I personally feel like you can have a normal distribution curve of five to ten data values for standard deviation with a larger number. When it comes to subjects like heights, test scores, house values, and sugar levels normal distribution is the best way to get the averages whether they are low or high.

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