Learning Competencies
- to recall statistical experiment and sample space
- to illustrate a random variable (discrete and continuous).
- to distinguish between a discrete and a continuous random variable.
Chapter 4 part3- Means and Variances of Random Variablesnszakir
Statistics, study of probability, The Mean of a Random Variable, The Variance of a Random Variable, Rules for Means and Variances, The Law of Large Numbers,
The document discusses discrete and continuous random variables. It defines discrete random variables as variables that can take on countable values, like the number of heads from coin flips. Continuous random variables can take any value within a range, like height. The document explains how to calculate and interpret the mean, standard deviation, and probabilities of events for both types of random variables using examples like Apgar scores for babies and heights of young women.
Probability Distribution (Discrete Random Variable)Cess011697
Learning Competencies:
- to find the possible values of a random variable.
illustrates a probability distribution for a discrete random variable and its properties.
- to compute probabilities corresponding to a given random variable.
There are some exercises for you to answer.
Random variables can be either discrete or continuous. A discrete random variable takes on countable values, while a continuous random variable can take on any value within a range. The probability distributions for discrete and continuous random variables are different. A discrete probability distribution lists each possible value and its probability, while a continuous distribution is described using a probability density function. Random variables are used widely in statistics and probability to model outcomes of experiments and random phenomena.
: Random Variable, Discrete Random variable, Continuous random variable, Probability Distribution of Discrete Random variable, Mathematical Expectations and variance of a discrete random variable.
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.
Probability Distributions for Discrete Variablesgetyourcheaton
This document discusses probability distributions for discrete variables. It begins by defining a probability distribution as a relative frequency distribution of all possible outcomes of an experiment. It provides examples of probability distributions for discrete variables like the binomial distribution. It discusses key aspects of probability distributions like the mean, standard deviation, and different types of distributions like binomial. It provides examples of calculating probabilities, means, and standard deviations for binomial distributions. It discusses the basic characteristics of the binomial distribution and provides an example of constructing a binomial distribution and calculating related probabilities.
Determining the Mean, Variance, and Standard Deviation of a Discrete Random Variable
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
Chapter 4 part3- Means and Variances of Random Variablesnszakir
Statistics, study of probability, The Mean of a Random Variable, The Variance of a Random Variable, Rules for Means and Variances, The Law of Large Numbers,
The document discusses discrete and continuous random variables. It defines discrete random variables as variables that can take on countable values, like the number of heads from coin flips. Continuous random variables can take any value within a range, like height. The document explains how to calculate and interpret the mean, standard deviation, and probabilities of events for both types of random variables using examples like Apgar scores for babies and heights of young women.
Probability Distribution (Discrete Random Variable)Cess011697
Learning Competencies:
- to find the possible values of a random variable.
illustrates a probability distribution for a discrete random variable and its properties.
- to compute probabilities corresponding to a given random variable.
There are some exercises for you to answer.
Random variables can be either discrete or continuous. A discrete random variable takes on countable values, while a continuous random variable can take on any value within a range. The probability distributions for discrete and continuous random variables are different. A discrete probability distribution lists each possible value and its probability, while a continuous distribution is described using a probability density function. Random variables are used widely in statistics and probability to model outcomes of experiments and random phenomena.
: Random Variable, Discrete Random variable, Continuous random variable, Probability Distribution of Discrete Random variable, Mathematical Expectations and variance of a discrete random variable.
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.
Probability Distributions for Discrete Variablesgetyourcheaton
This document discusses probability distributions for discrete variables. It begins by defining a probability distribution as a relative frequency distribution of all possible outcomes of an experiment. It provides examples of probability distributions for discrete variables like the binomial distribution. It discusses key aspects of probability distributions like the mean, standard deviation, and different types of distributions like binomial. It provides examples of calculating probabilities, means, and standard deviations for binomial distributions. It discusses the basic characteristics of the binomial distribution and provides an example of constructing a binomial distribution and calculating related probabilities.
Determining the Mean, Variance, and Standard Deviation of a Discrete Random Variable
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
1. The document discusses basic concepts in probability and statistics, including sample spaces, events, probability distributions, and random variables.
2. Key concepts are explained such as independent and conditional probability, Bayes' theorem, and common probability distributions like the uniform and normal distributions.
3. Statistical analysis methods are introduced including how to estimate the mean and variance from samples from a distribution.
This document provides an overview of key concepts related to random variables and probability distributions. It discusses:
- Two types of random variables - discrete and continuous. Discrete variables can take countable values, continuous can be any value in an interval.
- Probability distributions for discrete random variables, which specify the probability of each possible outcome. Examples of common discrete distributions like binomial and Poisson are provided.
- Key properties and calculations for discrete distributions like expected value, variance, and the formulas for binomial and Poisson probabilities.
- Other discrete distributions like hypergeometric are introduced for situations where outcomes are not independent. Examples are provided to demonstrate calculating probabilities for each type of distribution.
This document defines key concepts related to random variables including:
- A random variable is a numerical measure of outcomes from a random phenomenon.
- Probability distributions describe the probabilities associated with random variables.
- Expected value refers to the mean or weighted average of a probability distribution.
- As the number of trials increases, the actual mean approaches the true mean due to the Law of Large Numbers.
- Binomial and geometric distributions model situations with success/failure outcomes and independence between trials.
This document discusses several discrete probability distributions:
1. Binomial distribution - For experiments with a fixed number of trials, two possible outcomes, and constant probability of success. The probability of x successes is given by the binomial formula.
2. Geometric distribution - For experiments repeated until the first success. The probability of the first success on the xth trial is p(1-p)^(x-1).
3. Poisson distribution - For counting the number of rare, independent events occurring in an interval. The probability of x events is (e^-μ μ^x)/x!, where μ is the mean number of events.
This document discusses parameter estimation and interval estimation. It defines point estimates as single values that estimate population parameters and interval estimates as ranges of values within which population parameters are expected to fall. It provides examples of using the sample mean and variance as point estimators for the population mean and variance. It also discusses how to construct confidence intervals for population parameters based on sample statistics, sample size, and the desired confidence level.
Introduction to Statistics and ProbabilityBhavana Singh
This document provides an introduction to statistics and probability. It discusses key concepts in descriptive statistics including measures of central tendency (mean, median, mode), measures of dispersion (range, standard deviation), and measures of shape (skewness, kurtosis). It also covers correlation analysis, regression analysis, and foundational probability topics such as sample spaces, events, independent and dependent events, and theorems like the addition rule, multiplication rule, and total probability theorem.
This document discusses different types of sampling methods used in statistics. It defines key terms like population, sample, and random sampling. It then explains different random sampling techniques like simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. It also discusses the differences between strata and clusters. Finally, it briefly introduces some non-random sampling methods like quota sampling and convenience sampling.
This document provides an outline for a course on probability and statistics. It begins with an introduction to key concepts like measures of central tendency, dispersion, correlation, and probability distributions. It then lists common probability distributions and the textbook and references used. Later sections define important statistical terms like population, sample, variable types, data collection methods, and ways of presenting data through tables and graphs. It provides examples of each variable scale and ends with assignments for students.
The document discusses the normal curve and standard scores. It defines the normal curve as a continuous probability distribution that is bell-shaped and symmetric. It was developed by Gauss and Pearson. The normal curve can be divided into areas defined by standard deviations from the mean. Standard scores are raw scores converted to other scales, including z-scores, t-scores, and stanines. Z-scores indicate the distance from the mean in standard deviations. T-scores are on a scale of 50 plus or minus 10. Stanines use a nine-point scale with a mean of 5 and standard deviation of 2.
This document discusses the normal distribution and standard normal curve. It defines key properties of the normal distribution including that it is bell-shaped and symmetrical around the mean. The standard normal curve is introduced which has a mean of 0 and standard deviation of 1. The z-score is defined as a way to locate a value within a distribution based on its mean and standard deviation. Various probabilities are associated with areas under the normal curve based on z-scores.
This document discusses graphing rational functions. It defines key concepts like domain, range, intercepts, zeros, and asymptotes. An example rational function f(x)=x-2/(x+2) is used to demonstrate how to find these values and graph the function. The domain is all real numbers except -2, the x-intercept is 2, and the y-intercept is -1. The vertical asymptote is at x=-2. Horizontal asymptotes occur when the degree of the numerator is less than, equal to, or greater than the degree of the denominator.
This document discusses basic concepts of probability, including:
- The addition rule and multiplication rule for calculating probabilities of compound events.
- Events can be disjoint (mutually exclusive) or not disjoint.
- The probability of an event occurring or its complement must equal 1.
- How to calculate the probability of at least one occurrence of an event using the complement.
- When applying the multiplication rule, you must consider whether events are independent or dependent.
This lesson plan teaches students about inverse functions. It begins with objectives, materials, and a teaching strategy of lecture. Examples are provided to show how to find the inverse of one-to-one functions by interchanging x and y and solving for the new y. Properties are discussed, such as the inverse of an inverse is the original function. Students are asked to find inverses and solve word problems. The lesson concludes by having students generalize their understanding and complete an evaluation with additional inverse problems.
1. The document discusses different sampling methods including simple random sampling, systematic random sampling, stratified sampling, and cluster sampling.
2. It provides examples of how each sampling method works and how samples are selected from the overall population.
3. Exercises are provided to determine which sampling method should be used for different scenarios involving selecting samples from identified populations.
The document discusses the standard normal distribution. It defines the standard normal distribution as having a mean of 0, a standard deviation of 1, and a bell-shaped curve. It provides examples of how to find probabilities and z-scores using the standard normal distribution table or calculator. For example, it shows how to find the probability of an event being below or above a given z-score, or between two z-scores. It also shows how to find the z-score corresponding to a given cumulative probability.
Probability distribution of a random variable moduleMelody01082019
This document provides an overview of a module on statistics and probability for senior high school students. It covers basic concepts of random variables and probability distributions through two lessons. The first lesson defines random variables and distinguishes between discrete and continuous variables. The second lesson defines discrete probability distributions and shows how to construct a histogram for a probability mass function. Examples are provided to illustrate key concepts. Learning competencies focus on understanding and applying random variables and probability distributions to real-world problems.
The document shows the steps to calculate the mean of a probability distribution. A table lists the possible values (X) of a random variable, their respective probabilities (P(x)), and the product of each x and P(x). These products are summed to obtain 1.7, which is equal to the mean (μ) of the probability distribution.
1. The document discusses basic concepts in probability and statistics, including sample spaces, events, probability distributions, and random variables.
2. Key concepts are explained such as independent and conditional probability, Bayes' theorem, and common probability distributions like the uniform and normal distributions.
3. Statistical analysis methods are introduced including how to estimate the mean and variance from samples from a distribution.
This document provides an overview of key concepts related to random variables and probability distributions. It discusses:
- Two types of random variables - discrete and continuous. Discrete variables can take countable values, continuous can be any value in an interval.
- Probability distributions for discrete random variables, which specify the probability of each possible outcome. Examples of common discrete distributions like binomial and Poisson are provided.
- Key properties and calculations for discrete distributions like expected value, variance, and the formulas for binomial and Poisson probabilities.
- Other discrete distributions like hypergeometric are introduced for situations where outcomes are not independent. Examples are provided to demonstrate calculating probabilities for each type of distribution.
This document defines key concepts related to random variables including:
- A random variable is a numerical measure of outcomes from a random phenomenon.
- Probability distributions describe the probabilities associated with random variables.
- Expected value refers to the mean or weighted average of a probability distribution.
- As the number of trials increases, the actual mean approaches the true mean due to the Law of Large Numbers.
- Binomial and geometric distributions model situations with success/failure outcomes and independence between trials.
This document discusses several discrete probability distributions:
1. Binomial distribution - For experiments with a fixed number of trials, two possible outcomes, and constant probability of success. The probability of x successes is given by the binomial formula.
2. Geometric distribution - For experiments repeated until the first success. The probability of the first success on the xth trial is p(1-p)^(x-1).
3. Poisson distribution - For counting the number of rare, independent events occurring in an interval. The probability of x events is (e^-μ μ^x)/x!, where μ is the mean number of events.
This document discusses parameter estimation and interval estimation. It defines point estimates as single values that estimate population parameters and interval estimates as ranges of values within which population parameters are expected to fall. It provides examples of using the sample mean and variance as point estimators for the population mean and variance. It also discusses how to construct confidence intervals for population parameters based on sample statistics, sample size, and the desired confidence level.
Introduction to Statistics and ProbabilityBhavana Singh
This document provides an introduction to statistics and probability. It discusses key concepts in descriptive statistics including measures of central tendency (mean, median, mode), measures of dispersion (range, standard deviation), and measures of shape (skewness, kurtosis). It also covers correlation analysis, regression analysis, and foundational probability topics such as sample spaces, events, independent and dependent events, and theorems like the addition rule, multiplication rule, and total probability theorem.
This document discusses different types of sampling methods used in statistics. It defines key terms like population, sample, and random sampling. It then explains different random sampling techniques like simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and multi-stage sampling. It also discusses the differences between strata and clusters. Finally, it briefly introduces some non-random sampling methods like quota sampling and convenience sampling.
This document provides an outline for a course on probability and statistics. It begins with an introduction to key concepts like measures of central tendency, dispersion, correlation, and probability distributions. It then lists common probability distributions and the textbook and references used. Later sections define important statistical terms like population, sample, variable types, data collection methods, and ways of presenting data through tables and graphs. It provides examples of each variable scale and ends with assignments for students.
The document discusses the normal curve and standard scores. It defines the normal curve as a continuous probability distribution that is bell-shaped and symmetric. It was developed by Gauss and Pearson. The normal curve can be divided into areas defined by standard deviations from the mean. Standard scores are raw scores converted to other scales, including z-scores, t-scores, and stanines. Z-scores indicate the distance from the mean in standard deviations. T-scores are on a scale of 50 plus or minus 10. Stanines use a nine-point scale with a mean of 5 and standard deviation of 2.
This document discusses the normal distribution and standard normal curve. It defines key properties of the normal distribution including that it is bell-shaped and symmetrical around the mean. The standard normal curve is introduced which has a mean of 0 and standard deviation of 1. The z-score is defined as a way to locate a value within a distribution based on its mean and standard deviation. Various probabilities are associated with areas under the normal curve based on z-scores.
This document discusses graphing rational functions. It defines key concepts like domain, range, intercepts, zeros, and asymptotes. An example rational function f(x)=x-2/(x+2) is used to demonstrate how to find these values and graph the function. The domain is all real numbers except -2, the x-intercept is 2, and the y-intercept is -1. The vertical asymptote is at x=-2. Horizontal asymptotes occur when the degree of the numerator is less than, equal to, or greater than the degree of the denominator.
This document discusses basic concepts of probability, including:
- The addition rule and multiplication rule for calculating probabilities of compound events.
- Events can be disjoint (mutually exclusive) or not disjoint.
- The probability of an event occurring or its complement must equal 1.
- How to calculate the probability of at least one occurrence of an event using the complement.
- When applying the multiplication rule, you must consider whether events are independent or dependent.
This lesson plan teaches students about inverse functions. It begins with objectives, materials, and a teaching strategy of lecture. Examples are provided to show how to find the inverse of one-to-one functions by interchanging x and y and solving for the new y. Properties are discussed, such as the inverse of an inverse is the original function. Students are asked to find inverses and solve word problems. The lesson concludes by having students generalize their understanding and complete an evaluation with additional inverse problems.
1. The document discusses different sampling methods including simple random sampling, systematic random sampling, stratified sampling, and cluster sampling.
2. It provides examples of how each sampling method works and how samples are selected from the overall population.
3. Exercises are provided to determine which sampling method should be used for different scenarios involving selecting samples from identified populations.
The document discusses the standard normal distribution. It defines the standard normal distribution as having a mean of 0, a standard deviation of 1, and a bell-shaped curve. It provides examples of how to find probabilities and z-scores using the standard normal distribution table or calculator. For example, it shows how to find the probability of an event being below or above a given z-score, or between two z-scores. It also shows how to find the z-score corresponding to a given cumulative probability.
Probability distribution of a random variable moduleMelody01082019
This document provides an overview of a module on statistics and probability for senior high school students. It covers basic concepts of random variables and probability distributions through two lessons. The first lesson defines random variables and distinguishes between discrete and continuous variables. The second lesson defines discrete probability distributions and shows how to construct a histogram for a probability mass function. Examples are provided to illustrate key concepts. Learning competencies focus on understanding and applying random variables and probability distributions to real-world problems.
The document shows the steps to calculate the mean of a probability distribution. A table lists the possible values (X) of a random variable, their respective probabilities (P(x)), and the product of each x and P(x). These products are summed to obtain 1.7, which is equal to the mean (μ) of the probability distribution.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
1. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
RandomVariable:
Discrete & Continuous
Statistics and Probability
PRINCESS P. DIPASUPIL
Special ScienceTeacher I
2. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
STATISTICAL
EXPERIMENT
activity
outcome/s
any activity that
produces outcomes
3. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
a process that will generate data where the
outcomes have a corresponding chance of
occurrence.
STATISTICAL EXPERIMENT
4. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
SAMPLE
SPACE
the set of all possible
outcomes
5. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
What is the
sample space?
tossing a die
𝑆 = 1,2,3,4,5,6
6. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
What is the
sample space?
tossing two
coins
7. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
What is the
sample space?
Tossing a coin
and a die
8. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
Drawing a jack from
standard deck of
cards
What is the SAMPLE SPACE?
S={J♠, J♣, J⧫, J♥}
9. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
What is the sample
space?
tossing a
pair of dice
S={ 11 21 31 41 51 61
12 22 32 42 52 62
13 23 33 43 53 63
14 24 34 44 54 64
15 25 35 45 55 65
16 26 36 46 56 66}
10. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
What is the sample
space?
spinning
this wheel
11. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
What is the sample
space?
getting a sum
of 7 when a
pair of dice
is tossed
12. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
RANDOM
VARIABLE
a variable whose possible
values are determined by
chance
13. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
typically represented by an uppercase letter,
usually X, while its corresponding lowercase
letter, x, is used to represent one of its
values
RANDOM VARIABLE
14. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
X
Random
Variable
x
Value of a
Random
Variable
RANDOM VARIABLE: Notations
15. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
can only take a finite number
of distinct values
DISCRETE RANDOM VARIABLE
countable
16. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
can assume an infinite number
of values in an interval between
two specific values.
CONTINUOUS RANDOM VARIABLE
measurable
17. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
measurable
countable
infinite
finite
nonnegative
whole
numbers
fractions and
decimals
DISCRETE CONTINUOUS
VS
18. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
If the example illustrates a discrete
random variable, type “D”.
If the example illustrates a continuous
random variable, type “C”.
DISCRETE VS CONTINUOUS
19. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
5
4
the number of electric fans in
your house
3
DISCRETE or CONTINUOUS
2
1
20. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
amount of street food
consumed by Nicholasians per
day
DISCRETE or CONTINUOUS
5
4
3
2
1
21. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
the number of friends a person
can have
DISCRETE or CONTINUOUS
5
4
3
2
1
22. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
the number of beautiful
female students in SHS in
San Nicholas III, Bacoor
City, Cavite
DISCRETE or CONTINUOUS
5
4
3
2
1
23. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
the weight of all male students
in this class
DISCRETE or CONTINUOUS
5
4
3
2
1
24. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
Determine whether the random variable X orY is
discrete or continuous.
1. X = number of players in a randomly selected PBA team
2.Y = the height in inches of a randomly selected person
inside the coffee shop
3. X = the number of things in your personal collection
4.Y = the number of COVID-19 tests done in a randomly
selected day
5. X = the amount in pesos of fund allegedly stolen by
former officials of a randomly selected government agency
25. Statistics and Probability • PRINCESS P. DIPASUPIL
• Special ScienceTeacher I
Determine whether the random variable X orY is
discrete or continuous.
1. X = number of players in a randomly selected PBA
team
2.Y = the height in inches of a randomly selected
person inside the coffee shop
3. X = the number of things in your personal collection
4.Y = the number of COVID-19 tests done in a
randomly selected day
5. X = the amount in pesos of fund allegedly stolen by
former officials of a randomly selected government
agency
• discrete
• continuous
• discrete
• discrete
• continuous