A Random Variable and Binomial Experiment

NB: We do not resell your papers. Upon ordering, we do an original paper exclusively for you.

NB: All your data is kept safe from the public.

Table of Contents

Random Variable
Explanation
Binominal Experiment
References

Random Variable

A random variable is one whose values are determined by the results of a randomly occurring phenomenon. In other words, its value is not constant, but it may assume any potential values of that occurrence (experiment) (Lambert, 2018). Y is a discrete random variable if its values or representative area can be counted. On the contrary, it is considered a continuous random variable if it’s quantities or representative array of values are not quantifiable and it accepts any numbers on the reference axis or its interval.

Explanation

Y is a discrete random variable if its values or sample space can be counted. For instance, the number of people who arrived at the office between 7:00 A.M and 8:00 AM on a Monday. Conversely, Y is considered a continuous random variable if the quantities or representative array of values are not quantifiable. For example, the lifespan of a car battery. Here, Y may have any value between 0 and ∞, making it a continuous random variable.

Binominal Experiment

A binomial experiment has four properties and may be utilized in a binomial distribution if the four conditions listed below are met:

The representative sample (n) is constant;

n = 15 in this case; hence this hypothesis is true.

There are only two possibilities that may occur when a function is replicated: success or failure;

In this case, the laptop can either match the standards or fall short; this premise is true.

The likelihood of success is the same and fixed for each duplication;

In this case, the percentage of laptops manufactured that meet standards are set at 0.95; therefore, this hypothesis is fulfilled.

The simulations (or trials) are self-contained.

Given all assumptions are met, a binomial distribution may be employed to depict this operation.

If more than one laptop does not match the criteria, the whole batch must be examined, which is unnecessary. The needed probability is P(X > 1), which may be calculated using the excel function “1-BINOMDIST(1,15,0.05, TRUE)”. The probability obtained is 0.170953.
Accepting the lot would be wrong if the number of defects is less than or equal to 1, provided the faulty rate is 0.25. The needed probability is P(X = 1), which may be calculated using the function “BINOMDIST(1,15,0.25, TRUE)” in Excel. The probability obtained is 0.08018.

References

Lambert, B. (2018). A student’s guide to Bayesian statistics. Sage.

Click Here To Order Now!

A Random Variable and Binomial Experiment

Do you need this or any other assignment done for you from scratch?

We have qualified writers to help you.

We assure you a quality paper that is 100% free from plagiarism and AI.

You can choose either format of your choice ( Apa, Mla, Havard, Chicago, or any other)