1. The document provides examples for calculating the mean, variance, and standard deviation of discrete random variables. It defines these statistical terms and shows the formulas.
2. An example is provided of a teacher giving a 5 question test and the probabilities of getting each number of questions correct. The mean of this distribution is calculated as 3.41 and the variance is 1.5819.
3. A second example calculates the mean, variance, and standard deviation of the number of heads from tossing a coin twice. The mean is 1, the variance is 0.5, and the standard deviation is 0.71.
This document provides a lesson on calculating and interpreting the variance of a discrete random variable. It begins with objectives and essential questions. It then reviews standard deviation with an instructional video. Examples are provided to demonstrate how to calculate variance and standard deviation from a probability distribution by finding the mean, constructing columns for values squared and multiplied by probabilities, and using the variance and standard deviation formulas. Practice problems are given for individuals and groups. Key points define variance and standard deviation of a discrete random variable and properties of variance. Synthesis questions ask about determining variance and standard deviation from a distribution, measuring dispersion's importance, and constructing distributions for continuous variables.
The document discusses concepts related to mean, variance, and standard deviation of discrete random variables. It provides examples of calculating the mean, variance, and standard deviation from probability distributions. It also covers the normal probability distribution and properties such as being bell-shaped and symmetrical about the mean.
Mean and Variance of Discrete Random Variable.pptxMarkJayAquillo
油
The document discusses computing the mean, variance, and standard deviation of discrete random variables. It provides examples of calculating the mean of different probability distributions by taking the sum of each value multiplied by its probability. It also gives examples of finding variance by taking the sum of the squared differences between each value and the mean multiplied by its probability, and defines standard deviation as the square root of variance. The document aims to help readers understand how to calculate and interpret these statistical measures for discrete random variables.
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Chapter 5: Discrete Probability Distribution
5.1: Probability Distribution
This document discusses different types of discrete probability distributions:
- The uniform distribution where all outcomes are equally likely. Rolling a fair die is given as an example.
- The Bernoulli distribution which has only two possible outcomes (success/failure) with probabilities p and q=1-p.
- The binomial distribution which models experiments with a fixed number of trials, two possible outcomes per trial (success/failure), and where trials are independent.
This document discusses probability distributions and related concepts. It defines joint probability, conditional probability, and Bayes' theorem. It then discusses discrete and continuous probability distributions, giving examples of constructing probability mass functions for discrete random variables. It also defines key metrics for probability distributions like mean, variance, and standard deviation, providing examples of calculating these for given data sets. Finally, it discusses examples of discrete probability distributions for scenarios involving dice rolls, coin tosses, and raffle tickets.
This document provides information about calculating the mean, variance, and standard deviation of a discrete probability distribution. It defines these key terms and provides the relevant formulas. Two examples are worked through, calculating these values for given probability distributions. In the first example, the mean is 5.45, variance is 1.95, and standard deviation is 1.40. In the second example, the mean is 10.5, variance is 57.25, and standard deviation is 7.57. The document concludes with an activity asking students to answer problems calculating these values.
The document discusses different types of discrete probability distributions:
- Uniform distribution where all outcomes are equally likely, like when rolling a fair die.
- Bernoulli distribution which has only two possible outcomes (success/failure) with probabilities p and q=1-p.
- Binomial distribution which describes experiments with a fixed number of trials, two possible outcomes per trial (success/failure), and where trials are independent with constant probability of success p.
This document discusses how to calculate the variance and standard deviation of a discrete probability distribution. There are two main procedures: 1) subtract the mean from each value, square it, multiply by the probability, and sum; 2) multiply each value squared by its probability, sum, then subtract the mean squared. Examples demonstrate finding the variance and standard deviation for distributions of number of heads from coin tosses and customer inquiries. The key steps are finding the mean, squaring deviations from the mean, weighting by probability, and summing.
This document introduces key concepts related to random variables and probability distributions:
- A random variable is a function that assigns a numerical value to each possible outcome of an experiment. Random variables can be discrete or continuous.
- A probability distribution specifies the possible values of a random variable and their probabilities. For discrete random variables, this is called a probability mass function.
- Key properties of a probability distribution are that each probability is between 0 and 1, and the sum of all probabilities equals 1.
- The mean, variance, and standard deviation can be calculated from a probability distribution. The mean is the expected value, while variance and standard deviation measure dispersion around the mean.
Statistics and Probability-Random Variables and Probability DistributionApril Palmes
油
Here are the solutions to the problems:
1. a) Mean = 0.05 rotten tomatoes
b) P(x>1) = 0.03
2. a) Mean = 3.5
b) Variance = 35/12 = 2.91667
c) Standard deviation = 1.7321
3. a) Mean = $0.80
b) Variance = $2.40
4. X Probability
0 1/8
1 3/8
2 3/8
3 1/8
Determining the Mean, Variance, and Standard Deviation of a Discrete Random Variable
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
This document contains a lesson on probability distributions and calculating the mean, variance, and standard deviation. It includes examples of probability distributions and step-by-step instructions for calculating the key statistics. Students are asked questions to practice these calculations and assess their understanding of mean, variance, and standard deviation in the context of probability distributions.
This document provides an overview of random variables and various discrete probability distributions. It defines random variables and describes discrete and continuous random variables. It also covers the mean, variance, and standard deviation of discrete random variables. Various discrete probability distributions are introduced, including the discrete uniform distribution, Bernoulli distribution, and binomial distribution. Examples are provided to illustrate key concepts.
You pay $5 to play a card game where drawing the ace of hearts pays $100, other aces pay $10, other hearts pay $5, and other cards are losers. The document discusses concepts such as expected value, variance, standard deviation, and how these statistics change for linear transformations and combinations of random variables. It provides examples of calculating expected value and standard deviation for various games involving dice rolls.
Computing the Variance of a Discrete Probability Distribution.pptxJohnReyLanguidoQuija
油
This document discusses how to compute the variance of a discrete probability distribution. It begins by defining variance as the average of the squared differences from the mean. It then outlines the steps to calculate variance: 1) find the mean, 2) multiply the square of each value by its probability, 3) sum these results, and 4) subtract the mean. Several examples are provided to demonstrate how to apply these steps to compute the variance and standard deviation of given probability distributions.
This document discusses probability distributions and related concepts. It defines joint probability, conditional probability, and Bayes' theorem. It then discusses discrete and continuous probability distributions, giving examples of constructing probability mass functions for discrete random variables. It also defines key metrics for probability distributions like mean, variance, and standard deviation, providing examples of calculating these for given data sets. Finally, it discusses examples of discrete probability distributions for scenarios involving dice rolls, coin tosses, and raffle tickets.
This document provides information about calculating the mean, variance, and standard deviation of a discrete probability distribution. It defines these key terms and provides the relevant formulas. Two examples are worked through, calculating these values for given probability distributions. In the first example, the mean is 5.45, variance is 1.95, and standard deviation is 1.40. In the second example, the mean is 10.5, variance is 57.25, and standard deviation is 7.57. The document concludes with an activity asking students to answer problems calculating these values.
The document discusses different types of discrete probability distributions:
- Uniform distribution where all outcomes are equally likely, like when rolling a fair die.
- Bernoulli distribution which has only two possible outcomes (success/failure) with probabilities p and q=1-p.
- Binomial distribution which describes experiments with a fixed number of trials, two possible outcomes per trial (success/failure), and where trials are independent with constant probability of success p.
This document discusses how to calculate the variance and standard deviation of a discrete probability distribution. There are two main procedures: 1) subtract the mean from each value, square it, multiply by the probability, and sum; 2) multiply each value squared by its probability, sum, then subtract the mean squared. Examples demonstrate finding the variance and standard deviation for distributions of number of heads from coin tosses and customer inquiries. The key steps are finding the mean, squaring deviations from the mean, weighting by probability, and summing.
This document introduces key concepts related to random variables and probability distributions:
- A random variable is a function that assigns a numerical value to each possible outcome of an experiment. Random variables can be discrete or continuous.
- A probability distribution specifies the possible values of a random variable and their probabilities. For discrete random variables, this is called a probability mass function.
- Key properties of a probability distribution are that each probability is between 0 and 1, and the sum of all probabilities equals 1.
- The mean, variance, and standard deviation can be calculated from a probability distribution. The mean is the expected value, while variance and standard deviation measure dispersion around the mean.
Statistics and Probability-Random Variables and Probability DistributionApril Palmes
油
Here are the solutions to the problems:
1. a) Mean = 0.05 rotten tomatoes
b) P(x>1) = 0.03
2. a) Mean = 3.5
b) Variance = 35/12 = 2.91667
c) Standard deviation = 1.7321
3. a) Mean = $0.80
b) Variance = $2.40
4. X Probability
0 1/8
1 3/8
2 3/8
3 1/8
Determining the Mean, Variance, and Standard Deviation of a Discrete Random Variable
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
This document contains a lesson on probability distributions and calculating the mean, variance, and standard deviation. It includes examples of probability distributions and step-by-step instructions for calculating the key statistics. Students are asked questions to practice these calculations and assess their understanding of mean, variance, and standard deviation in the context of probability distributions.
This document provides an overview of random variables and various discrete probability distributions. It defines random variables and describes discrete and continuous random variables. It also covers the mean, variance, and standard deviation of discrete random variables. Various discrete probability distributions are introduced, including the discrete uniform distribution, Bernoulli distribution, and binomial distribution. Examples are provided to illustrate key concepts.
You pay $5 to play a card game where drawing the ace of hearts pays $100, other aces pay $10, other hearts pay $5, and other cards are losers. The document discusses concepts such as expected value, variance, standard deviation, and how these statistics change for linear transformations and combinations of random variables. It provides examples of calculating expected value and standard deviation for various games involving dice rolls.
Computing the Variance of a Discrete Probability Distribution.pptxJohnReyLanguidoQuija
油
This document discusses how to compute the variance of a discrete probability distribution. It begins by defining variance as the average of the squared differences from the mean. It then outlines the steps to calculate variance: 1) find the mean, 2) multiply the square of each value by its probability, 3) sum these results, and 4) subtract the mean. Several examples are provided to demonstrate how to apply these steps to compute the variance and standard deviation of given probability distributions.
14th International Conference on Advanced Computer Science and Information Te...ijitcs
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Call for Research Papers!!
Welcome to ICAIT 2025
14th International Conference on Advanced Computer Science and Information Technology (ICAIT 2025)
September 20 ~ 21, 2025, Copenhagen, Denmark
Webpage URL: https://itcse2025.org/icait/index
Submission URL: https://itcse2025.org/submission/index
Submission Deadline: May 24, 2025
Contact Us
Here's where you can reach us : icait@itcse2025.org (or) icaitconf@yahoo.com
For our eighth webinar, we explored what crime statistics are and how we measure them. We also answered some complex questions on crime statistics, like whether crime is going up or down, or whether there is a 'best' measure to understand trends in overall crime.
15 Data Quality Issues Identify & Resolve Errors.pdfAffinityCore
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Identify and resolve 15 common data quality issues to improve accuracy, reduce errors, and enhance decision-making. Learn effective strategies for cleaner, reliable data.
2. THE MEAN
a. The average of all possible outcomes. It is
otherwise referred to as the expected value
of a probability distribution.
b. Means that if we repeat any given experiment
infinite times, the theoretical mean would be the
expected value.
c. Any discrete probability distribution has a
mean.
3. THE VARIANCE AND STANDARD
DEVIATION
The VARIANCE of a random
variable displays the variability or the
dispersions of the random variables.
It shows the distance of a random
variable from its mean.
4. THE VARIANCE AND STANDARD
DEVIATION
a. If the values of the variance and standard deviation
are HIGH, that means that the individual outcomes
of the experiment are far relative to each other. In
other words, the values differ greatly.
b. A large value of standard deviation (or variance)
means that the distribution is spread out, with
some possibility of observing values at some
distance from the mean.
5. THE VARIANCE AND STANDARD
DEVIATION
c. If the variance and standard deviation are LOW, that
means that the individual outcomes of the
experiment are closely spaced with each other. In
other words, the values are almost the same values
or if they do differ, the difference is small.
d. A small value of standard deviation (or variance)
means that the dispersion of the random variable is
narrowly concentrated around the mean.
6. Mean, variance, and
standard deviation can
be illustrated by looking
pattern and analyzing given
illustrations and diagrams.
7. During Town Fiesta, people used to go to Carnival
that most folks call it Perya. Mang Ben used to play
Betobeto hoping that he would win. While he is
thinking about what possible outcomes in every roll
would be, he is always hoping that his bet is right.
X 1 2 3 4 5 6
P(X)
EXAMPLE
8. 1.Is the probability of X lies between 0 and 1?
2.What is the sum of all probabilities of X?
3.Is there a negative probability? Is it possible
to have a negative probability?
4.How will you illustrate the average or mean
of the probabilities of discrete random
variable?
QUESTIONS:
9. 1.Is the probability of x lies between 0
and 1?
Yes, the probability of X lies between 0
and 1.
X 1 2 3 4 5 6
P(X)
SOLUTION:
10. 2. What is the sum of all probabilities of X?
犒 = + + + + +
=
+
+
+
+
+
=
+++++
=
The sum of all probabilities of X is exactly 1.
X 1 2 3 4 5 6
P(X)
SOLUTION:
11. 3. Is there a negative probability? Is it possible
to have a negative probability?
No negative probabilities because it is
impossible to have it based on the
characteristic of the probability of
discrete random variables.
SOLUTION:
12. In a seafood restaurant, the
manager wants to know if their
customers like their new raw large
oysters. According to their sales
representative, in the past 4 months,
the number of oysters consumed by
a customer, along with its
corresponding probabilities, is
shown in the succeeding table.
Compute the mean, variance and
standard deviation.
Number of
oysters
consumed
X
Probabilit
y
P(X)
0
1
2
3
4
EXAMPLE 1
14. 1.What is the mean?
= 犒[ ()]
= + . + . + . + .
= .
SOLUTION:
15. 2. What is the standard deviation?
2
= 犒 ( )族 ()
= 0.448 + 0.128 + 0.012 + 0.288 + 0.484
.
SOLUTION:
16. 3. What is the standard deviation?
= ( )族 () = 2
= 1.56
= .
SOLUTION:
17. Mr. Umali, a Mathematics
teacher, regularly gives a
formative assessment composed
of 5 multiple-choice items. After
the assessment, he used to
check the probability
distribution of the correct
responses, and the data is
presented below:
TEST ITEM
(X)
Probability
()
0 0.03
1 0.05
2 0.12
3 0.30
4 0.28
5 0.22
EXAMPLE 2
18. 1.What is the average or mean of the given
probability distribution?
2.What are the values of the variance of the
probability distribution?
3.What are the values of standard deviation
of the probability distribution?
QUESTIONS:
21. 2. What is the standard deviation?
2
= 犒 ( )族 ()
= . + . + . + . + . +
= .
SOLUTION:
22. 3. What is the standard deviation?
= ( )族 () = 2
= 1.59
= .
SOLUTION:
23. 1. The number of shoes sold per day at a retail store is shown in the
table below. Illustrate the mean, variance, and standard deviation
of this distribution.
Write all the necessary formula and show the complete solution.
Formula to be used: (a)Mean, (b)Variance,(c) Standard
Deviation
X 19 20 21 22 23
P(X) 0.4 0.2 0.2 0.1 0.1
ACTIVITY 1
24. 1. The Land Bank of the Philippines Manager claimed
that each saving account customer has several
credit cards. The following distribution showing the
number of credits cards people own.
a. Calculate the mean, variance, and standard deviation
of a discrete random variable.
X 0 1 2 3 4
P(X) 0.18 0.44 0.27 0.08 0.03
ASSIGNMENT
25. 2. Suppose an unfair die is rolled and let X be the
random variable representing the number of dots
that would appear with a probability distribution
below.
a. Calculate the mean, variance, and standard deviation
of a discrete random variable.
OUTCOME (X) 1 2 3 4 5 6
Probability (X) 0.1 0.1 0.1 0.5 0.1 0.1
26. 1. The number of cellular phones sold per day at
the E-Cell Retail Store with the corresponding
probabilities is shown in the table below.
Compute the mean, variance, and standard
deviation and interpret the result.
() 15 18 19 20 22
Probability () 0.30 0.20 0.20 0.15 0.15