Uniform Discrete Sample Distribution

In summary, the conversation was about calculating the sample mean and variance, and using them to find the probability of a certain range of values for the sum of 36 random variables. The initial calculation resulted in a numerical answer of 0.232574, but after using interpolation from the Z table, the correct answer was found to be 0.2311. However, using a computer algebra package like Maple, an exact answer of 0.2379 was obtained.
  • #1
Lifprasir
16
0

Homework Statement


3515bb2e33e80e378afb257eb1a37e30.png

2. Homework Equations [/B]

So the sample mean is 2. the sample variance would be [[(3-1+1)-1]/12]/36 = 8/432.

The Attempt at a Solution



Is it, P[ (2.1-2)/sqrt(8/432) < z < (2.5-2)/sqrt(8/432)] = 0.232574.

The book answer is 0.2312. I just want to be sure.
 
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  • #2
Your calculation looks fine, but I get a different numerical answer than you do. I get 0.2311.
 
  • #3
It must be due to my rounding, I am using the Z table from the back of my book so I lose precision I guess.
 
  • #4
It shouldn't be off by that much, though. What values of ##z## did you get and the corresponding values from the table?
 
  • #5
I see what probably happened. You're rounding the values of ##z## to two decimal places, right? When you use the tables, you need to interpolate to get a more accurate answer.
 
  • #6
Lifprasir said:

Homework Statement


3515bb2e33e80e378afb257eb1a37e30.png

2. Homework Equations [/B]

So the sample mean is 2. the sample variance would be [[(3-1+1)-1]/12]/36 = 8/432.

The Attempt at a Solution



Is it, P[ (2.1-2)/sqrt(8/432) < z < (2.5-2)/sqrt(8/432)] = 0.232574.

The book answer is 0.2312. I just want to be sure.

Using the computer algebra package Maple (instead of tables) I get the answer = 0.2310970815 ≈ 0.2311. However, this is an approximation, based on use of the normal distribution to get the distribution of the sum. We can also get an exact answer (by looking at the probability generating function for the sum). Since ##2.1 < \sum_{i=1}^{36} X_i \,/36 < 2.5## we have ## 75.6 < \sum X_i < 90##, so (interpreting the inequalities as strict, the way they are written), the sum ##\sum X_i## must lie between 76 and 89. Therefore, with ##S \equiv \sum_{i=1}^{36} X_i## we have:
[tex] \text{exact answer} = \sum_{k=76}^{89} P(S = k) = 0.2379271979 \approx 0.2379 [/tex]
 
  • #7
Oh, well then. I will know to interpolate next time around from the tables. Thanks for the help.
 

Related to Uniform Discrete Sample Distribution

1. What is a uniform discrete sample distribution?

A uniform discrete sample distribution is a probability distribution that describes the likelihood of obtaining a specific outcome in a random experiment where the possible outcomes are limited and equally likely. It is characterized by a constant probability for each outcome, resulting in a flat or uniform shape on a graph.

2. How is a uniform discrete sample distribution different from a continuous distribution?

A uniform discrete sample distribution is different from a continuous distribution in that it only applies to discrete variables, where the possible outcomes are countable and distinct. A continuous distribution, on the other hand, applies to continuous variables, where the possible outcomes can take on any value within a given range.

3. What are some real-world examples of a uniform discrete sample distribution?

Some real-world examples of a uniform discrete sample distribution include rolling a fair die, flipping a coin, and selecting a card from a deck of cards. In each of these situations, the possible outcomes are limited and equally likely, resulting in a uniform distribution.

4. How is a uniform discrete sample distribution calculated?

The probability of each outcome in a uniform discrete sample distribution is calculated by dividing 1 by the total number of possible outcomes. For example, if there are 6 possible outcomes, each outcome would have a probability of 1/6 or approximately 16.67%.

5. What is the significance of a uniform discrete sample distribution in scientific research?

A uniform discrete sample distribution is often used in scientific research to model situations where the possible outcomes are equally likely, such as in randomized controlled experiments. It allows researchers to make predictions about the likelihood of obtaining a specific outcome and helps to ensure fairness and unbiased results in their studies.

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