How would you differentiate a discrete from a continuous random variable|Essay helper

Posted: January 22nd, 2023

Deonicia post

What is a random variable?

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A random variable is a variable whose value is unknown or a function that assigns values to each of the outcomes of an experiment and it can also have different values every time you run the experiment to where the variable is linked.

How would you differentiate a discrete from a continuous random variable?

Discrete data is a numerical type of data that includes whole, concrete numbers with specific and fixed data values determined by counting. Continuous data includes complex numbers and varying data values measured over a particular time interval and is measurable.

The 4 characteristics of a binomial experiment are the following 1) Each observation falls into one of two categories called success or failure. 2) There is a fixed number of observations. 3) The observations are all independent. 4) The probability of “success” p is the same for each outcome.

Can we use a binomial distribution to model this process?

Yes, I think it is possible to use the binomial distribution to model this process.

What is the probability that the entire batch unnecessarily must be tested if, in fact, 95% of its laptops conform to specifications? (Hint: Use Excel’s =BINOMDIST() function to find the probability.) 

The probability that the entire batch is unnecessarily tested if, in fact, 95% of the laptops conform to the specification is =BINOM.DIST(1,15,0.05, TRUE) = 0.829047

What is the probability that the batch is incorrectly accepted if only 75% of its laptops conform to specifications?

The probability that the back is incorrectly accepted if only 75% of the laptops conform to specifications is =BINOM.DIST(1,15,0.25, TRUE) = .0.080181

T. Pyror post

 

What is a random variable?

A random variable is useful in mathematics stating you can prove something without assuming the value of a variable and that make a general statement over a range of values for that variable.

How would you differentiate a discrete from a continuous random variable ?

the difference is T he outcome of rolling a die is a discrete random variable, due to it that one of six possible numbers. Will be a Continuous random variables, the difference of any value in a given interval.

What are the 4 characteristics of a binomial experiment?

1: The number of observations n is fixed. 2: Each observation is independent. 3: Each observation represents one of two outcomes (“success” or “failure”). 4: The probability of “success” p is the same for each outcome.

It is possible to use binomial distribution to model a process

What is the probability that the entire batch unnecessarily has to be tested if in fact 95% of its laptops conform to specifications? (Hint: Use Excel’s =BINOMDIST() function to find the probability.)

after inputting it into excel I believe liability of 95% of laptops conform to specifications is

=BINOMDIST (1,15,0.05,1) = .829047

to be true.

What is the probability that the batch is incorrectly accepted if only 75% of its laptops conform to specifications.

after inputting it into excel with 75% the batch needed to be tested I believe it to be profitable.

https://www.radfordmathematics.com/probabilities-and-statistics/binomial-distribution/binomial-distribution-probability-distribution-function.html

Kelly post 

TVM calculations are used a lot in my life but I do not often know I am using them. We make these calculations when making decisions like, should I drive to get a better deal? Well how far do you have to drive, how much would the gas cost for that drive and would you still be saving money? If you aren’t saving money on gas it doesn’t make sense to drive to purchase the item on sale.

I also used the time value of money when I made the decision to purchase my house. I knew that there was going to be a season of working hard and saving money so that I could purchase a house and make my money work for me in equity. The time sent to purchase the house was well used because my house has since kicked back a lot of equity for me and my husband.

Christina post

When it comes to this week’s discussion board it asks us to share an example of a situation when you used TVM calculations to support a financial decision either in your professional or personal life. First and for most when it comes to TVM, the time value of money is a useful tool in helping you understand the worth of money in relation to time. It is a formula often used by investors to better understand the value of money as it compares to its value in the future. Time value of money is important because it helps investors and people saving for retirement determine how to get the most out of their dollars. This concept is fundamental to financial and applies to your savings, investments and purchasing power. When it comes to this weeks discussion board, Im looking foward to the class and learning about finances because when it comes to finances time is money and money is time and that effects your future. Good luck in class this week.

DISCUSSION QUESTION

 

We’ve all heard the expression, “Time is money,” and time value of money calculations certainly depict this sentiment.

Respond to the following in a minimum of 175 words:

  • Share an example of a situation when you used TVM calculations to support a financial decision either in your professional or personal life. (If necessary, share a hypothetical example of when you might use TVM calculations.)
  • Discuss which TVM calculations you used to support your financial decision and the benefits this provided.
  • In the example you shared, how was cash flow impacted by your decisions?

SOLUTION

A discrete random variable is a random variable that can only take on a countable number of distinct values, such as integers. A continuous random variable is a random variable that can take on any value within a certain interval on the real number line. The main difference is that the set of possible values for a discrete random variable is countable, while the set of possible values for a continuous random variable is uncountable.

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