Discrete Probability Distribution Tables Skills

Total Difference = -0.05In summary, we discussed constructing a discrete probability distribution table for a fair six-sided dice and calculating the mean, variance, and standard deviation for both the theoretical and empirical data from a dice simulator. We also compared the classical and empirical probabilities and noted a difference of -0.05 for the value of 1.
  • #1
drumsticksss
1
0
A problem i made up for some of my friends who need help with discrete distributions tables. Can you do it?
Dice Generator
Part I:
1. Construct a discrete probability distribution table for a fair six-sided dice. (Round according to example)
2. Calculate the mean, variance, and standard deviation based on the probability distribution.

Part II
A dice simulator was used to “roll” sixty six-sided dice. The results are provided below.
2 4 2 4 3 1
4 3 3 1 5 5
6 2 2 1 1 4
4 4 3 1 5 6
1 2 3 2 5 2
1 4 1 5 1 6
5 4 2 3 2 4
6 4 1 4 5 1
3 6 3 3 4 1
6 6 2 1 2 3


1. Construct a discrete probability distribution table based on the data from the simulator. (Round according to example)

2. Calculate the mean, variance, and standard deviation based on the data.

3. Compare the classical probabilities from Part I with the empirical probabilities from Part II. What are the differences in the probabilities for each possible value? Make a table displaying the differences.
Part Ix p(x) x*p(x) x (x-µ)2 (x-µ)2*p(x)
1 0.1667 0.1667 -2.5007 6.2535 1.042
2
3
4
5
6
∑x*p(x) = ∑(x-µ)2*p(x)=Part II
x p(x) x*p(x) x-µ (x-µ)2 (x-µ)2*p(x)
1 0.2167 0.2167 -2.1671 4.6963 1.018
2
3
4
5
6
∑x*p(x)= ∑(x-µ)2*p(x)=Differences:

x Classical (Part I) Empirical (PartII) Differences
1 0.1667 0.2167 -0.05
2
3
4
5
6
 
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  • #2

∑p(x)= ∑p(x)=

The differences in probabilities between the classical and empirical data are small, with the largest difference being -0.05 for the value of 1. This could be due to the small sample size in Part II compared to the theoretical probabilities calculated in Part I. However, overall, the empirical probabilities are close to the theoretical probabilities, indicating that the dice simulator is producing fairly accurate results.
 

Related to Discrete Probability Distribution Tables Skills

1. What is a discrete probability distribution table?

A discrete probability distribution table is a table that lists all possible outcomes of an experiment along with their corresponding probabilities. It shows the chances of each possible outcome occurring, allowing for the calculation of expected values and probabilities.

2. How do I read a discrete probability distribution table?

To read a discrete probability distribution table, first identify the possible outcomes listed in the first column. Then, find the corresponding probabilities for each outcome in the second column. The sum of all the probabilities should equal 1. You can use this table to calculate the expected value and probabilities for specific events or combinations of events.

3. How is a discrete probability distribution table different from a continuous probability distribution table?

A discrete probability distribution table deals with outcomes that can be counted and have a finite number of possible values, whereas a continuous probability distribution table deals with outcomes that can take on any value within a given range. In a discrete distribution, the probabilities are calculated by adding up the individual probabilities for each outcome. In a continuous distribution, probabilities are calculated using integration.

4. What is the purpose of a discrete probability distribution table?

The main purpose of a discrete probability distribution table is to show the probabilities associated with each possible outcome of an experiment. It allows for the calculation of expected values and probabilities for specific events, providing a way to analyze and make predictions based on the likelihood of different outcomes.

5. How can I use a discrete probability distribution table in real-life situations?

Discrete probability distribution tables can be used in various real-life situations, such as in business to analyze the likelihood of different outcomes and make decisions based on those probabilities. They can also be used in sports to predict the chances of a team winning or in gambling to calculate the expected value of a bet. Additionally, they can be used in scientific experiments to analyze the probabilities of different outcomes.

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