Real life use of probability.
•Download as PPTX, PDF•
4 likes•5,700 views
Probability is a measure of how likely events are to occur, calculated as the number of desired outcomes divided by the total number of possible outcomes. For a coin toss with two possible outcomes (heads or tails), the probability of getting heads is 1/2 or 0.5. Some boys playing cricket decided to toss a coin to determine teams, with heads as the desired outcome. The probability of getting heads from a single coin toss can be calculated as 1 (the number of desired outcomes) divided by 2 (the total number of possible outcomes from the coin toss).
Report
Share
Report
Share
Recommended
Ppt uses of probability by rambabu
This document discusses various examples of how probability is used in real life. It describes how meteorological departments use probability to predict the weather based on past data trends. It also gives examples of how probability is used in sports like basketball, board games with spinners, medical decisions about surgery success rates, insurance premium calculations, exit polls, and predicting life expectancy. The document emphasizes that while the future cannot be predicted exactly, probability helps determine the likelihood of outcomes and can help with decision making.
PROBABILITY
This is benefical for student mainly for class 10. And the ppt material is extracted from R.D. Sharma and 10 NCERT Text book.
Basic concept of probability
This presentation provides an introduction to basic probability concepts. It defines probability as the study of randomness and uncertainty, and describes how probability was originally associated with games of chance. Key concepts discussed include random experiments, sample spaces, events, unions and intersections of events, and Venn diagrams. The presentation establishes the axioms of probability, including that a probability must be between 0 and 1, the probability of the sample space is 1, and probabilities of mutually exclusive events sum to the total probability. Formulas for computing probabilities of unions, intersections, and complements of events are also presented.
Probability
This document defines key terminology related to probability, including:
- Sample space: The set of all possible outcomes of a random experiment.
- Events: One or more possible outcomes of an experiment.
- Equally likely events: Events with an equal chance of occurring.
- Mutually exclusive events: Events that cannot occur together.
- Exhaustive events: A set of events where one of the events must occur.
It also provides examples and introduces the classical definition of probability as the number of favorable outcomes divided by the total number of outcomes.
Probability
Probability is the chance that something will happen or the likelihood of an event. It is measured by the number of favorable outcomes divided by the total number of possible outcomes. Some key contributors to the development of probability include Buffon, Kerrich, and Pearson who performed coin toss experiments to determine experimental probabilities. Probability is used in various real-life domains like weather forecasting, insurance policies, sports strategies, and medical decisions.
Probability
This document discusses key concepts in probability. It defines basic terms like experiment, sample space, event, and probability. It provides examples of calculating probability for coin tosses and dice rolls using the classical method of dividing the number of ways an event can occur by the total number of possible outcomes. The document also discusses limitations of the classical method and introduces the empirical and subjective methods of determining probability based on observed frequencies and personal judgment respectively.
Probability Overview
There are two ways to count the number of possible outcomes of an experiment:
1) Using a tree diagram to list out all the combinations
2) Using the Fundamental Counting Principle, which involves multiplying the number of choices for each event together.
To calculate the probability of compound events (events made up of two or more simple events), you first determine if the events are independent or dependent. For independent events, the probability of one event does not affect the other event. You calculate the probability by multiplying the individual probabilities together.
Intro to probability
This document provides an introduction to probability. It defines probability as a numerical index of the likelihood that a certain event will occur, with a value between 0 and 1. It discusses examples of using probability terms like chance and likelihood. It also covers key probability concepts such as experiments, outcomes, events, and sample spaces. It explains different types of probability including subjective, objective/classic, and empirical probabilities. It provides examples of calculating probabilities of events using various approaches.
Recommended
Ppt uses of probability by rambabu
This document discusses various examples of how probability is used in real life. It describes how meteorological departments use probability to predict the weather based on past data trends. It also gives examples of how probability is used in sports like basketball, board games with spinners, medical decisions about surgery success rates, insurance premium calculations, exit polls, and predicting life expectancy. The document emphasizes that while the future cannot be predicted exactly, probability helps determine the likelihood of outcomes and can help with decision making.
PROBABILITY
This is benefical for student mainly for class 10. And the ppt material is extracted from R.D. Sharma and 10 NCERT Text book.
Basic concept of probability
This presentation provides an introduction to basic probability concepts. It defines probability as the study of randomness and uncertainty, and describes how probability was originally associated with games of chance. Key concepts discussed include random experiments, sample spaces, events, unions and intersections of events, and Venn diagrams. The presentation establishes the axioms of probability, including that a probability must be between 0 and 1, the probability of the sample space is 1, and probabilities of mutually exclusive events sum to the total probability. Formulas for computing probabilities of unions, intersections, and complements of events are also presented.
Probability
This document defines key terminology related to probability, including:
- Sample space: The set of all possible outcomes of a random experiment.
- Events: One or more possible outcomes of an experiment.
- Equally likely events: Events with an equal chance of occurring.
- Mutually exclusive events: Events that cannot occur together.
- Exhaustive events: A set of events where one of the events must occur.
It also provides examples and introduces the classical definition of probability as the number of favorable outcomes divided by the total number of outcomes.
Probability
Probability is the chance that something will happen or the likelihood of an event. It is measured by the number of favorable outcomes divided by the total number of possible outcomes. Some key contributors to the development of probability include Buffon, Kerrich, and Pearson who performed coin toss experiments to determine experimental probabilities. Probability is used in various real-life domains like weather forecasting, insurance policies, sports strategies, and medical decisions.
Probability
This document discusses key concepts in probability. It defines basic terms like experiment, sample space, event, and probability. It provides examples of calculating probability for coin tosses and dice rolls using the classical method of dividing the number of ways an event can occur by the total number of possible outcomes. The document also discusses limitations of the classical method and introduces the empirical and subjective methods of determining probability based on observed frequencies and personal judgment respectively.
Probability Overview
There are two ways to count the number of possible outcomes of an experiment:
1) Using a tree diagram to list out all the combinations
2) Using the Fundamental Counting Principle, which involves multiplying the number of choices for each event together.
To calculate the probability of compound events (events made up of two or more simple events), you first determine if the events are independent or dependent. For independent events, the probability of one event does not affect the other event. You calculate the probability by multiplying the individual probabilities together.
Intro to probability
This document provides an introduction to probability. It defines probability as a numerical index of the likelihood that a certain event will occur, with a value between 0 and 1. It discusses examples of using probability terms like chance and likelihood. It also covers key probability concepts such as experiments, outcomes, events, and sample spaces. It explains different types of probability including subjective, objective/classic, and empirical probabilities. It provides examples of calculating probabilities of events using various approaches.
Probability
History behind the
development of the concept
In 1654, a gambler Chevalier De Metre approached the well known Mathematician Blaise Pascal for certain dice problem. Pascal became interested in these problems and discussed it further with Pierre de Fermat. Both of them solved these problems independently. Since then this concept gained limelight.
Basic Things About The Concept
Probability is used to quantify an attitude of mind towards some uncertain proposition.
The higher the probability of an event, the more certain we are that the event will occur.
Probability ppt by Shivansh J.
The document discusses probability and chance. It defines probability as a measure of how likely an event is to occur from 0 to 1, with 1 being certain and 0 being impossible. Chance is expressed as a percentage, with 50% meaning equally likely. Examples are given of probability in weather forecasting and games. The origins and modern uses of probability are outlined in fields like traffic control, genetics, and investment returns. Predictable versus unpredictable events are distinguished. Formulae for calculating probability from sample data are provided. Random phenomena are described as having uncertain individual outcomes but regular relative frequencies over many repetitions, like coin tosses. Applications in risk assessment and commodity markets are mentioned. Reliability engineering in product design is discussed as using probability of
Probablity ppt maths
The document provides an overview of probability concepts including:
- Probability is a measure of how likely an event is, defined as the number of favorable outcomes divided by the total number of possible outcomes.
- Theoretical probability predicts outcomes without performing experiments, dealing with events as combinations of elementary outcomes.
- Random experiments may have different results each time while deterministic experiments always produce the same outcome.
- Elementary events are individual outcomes, and compound events combine multiple elementary outcomes.
- Theoretical probability of an event is the number of favorable elementary events divided by the total number of possible events.
- The probabilities of an event and its negation must sum to 1.
Probability in daily life
This document provides an introduction to probability and its applications in daily life. It defines probability as a measure of how often an event will occur if an experiment is repeated. Probability is always between 0 and 1, with 1 being a certain event and 0 being an impossible event. The document discusses random experiments, sample spaces, outcomes, events, and favorable events. It provides examples of calculating probability for events like drawing cards from a deck or selecting people with certain characteristics from a population. Overall, the document outlines basic probability concepts and terminology.
Probability class 9 ____ CBSE
This document discusses probability and related concepts. It defines probability as the study of uncertainty and notes its uses in fields like mathematics, science, and medicine. Key terms are introduced, including random experiments, trials, events, and elementary events. Experimental probability is defined based on the number of trials and outcomes. Examples are provided to demonstrate calculating probabilities and the relationship that the probabilities of all elementary events sum to 1.
Probability
This document defines key concepts in probability and provides examples. It discusses probability vocabulary like sample space, outcome, trial, and event. It defines probability as the number of times a desired outcome occurs over total trials. Events are independent if the outcome of one does not impact others, and mutually exclusive if they cannot occur together. The addition and multiplication rules for probability are explained. Conditional probability describes the probability of a second event depending on the first occurring. Counting techniques are discussed for finding total possible outcomes of combined experiments. Review questions are provided to test understanding of the material.
Conditional-Probability-Powerpoint.pptx
The document defines conditional probability as the probability of an event occurring given that another event has already occurred. It provides the formula for conditional probability as P(B|A) = P(A and B) / P(A). Several examples are worked through applying this formula to calculate conditional probabilities in different contexts like selecting chips from a box, survey responses, exam scores, and dice rolls. Exercises at the end provide additional practice problems for calculating conditional probabilities.
Probability
Probability not Higher Level....This PPT is for beginners or for those who learnt or to study PROBABILITY........... :):):):):)
introduction to probability
The document provides an introduction to probability. It discusses:
- What probability is and the definition of probability as a number between 0 and 1 that expresses the likelihood of an event occurring.
- A brief history of probability including its development in French society in the 1650s and key figures like James Bernoulli, Abraham De Moivre, and Pierre-Simon Laplace.
- Key terms used in probability like events, outcomes, sample space, theoretical probability, empirical probability, and subjective probability.
- The three types of probability: theoretical, empirical, and subjective probability.
- General probability rules including: the probability of impossible/certain events; the sum of all probabilities equaling 1; complements
Bayes Theorem
Bayes' Theorem relates prior probabilities, conditional probabilities, and posterior probabilities. It provides a mathematical rule for updating estimates based on new evidence or observations. The theorem states that the posterior probability of an event is equal to the conditional probability of the event given the evidence multiplied by the prior probability, divided by the probability of the evidence. Bayes' Theorem can be used to calculate conditional probabilities, like the probability of a woman having breast cancer given a positive mammogram result, or the probability that a part came from a specific supplier given that it is non-defective. It is widely applicable in science, medicine, and other fields for revising hypotheses based on new data.
Conditional Probability
Conditional probability is the probability of an event occurring given that another event has occurred. It is calculated as the probability of both events occurring divided by the probability of the first event. An example is given of calculating the probability of drawing two white balls in succession from an urn without replacement. The formula for conditional probability is derived as the probability of events A and B occurring divided by the probability of A. This is demonstrated using an example of finding the percentage of friends who like chocolate that also like strawberry.
Conditional probability
This document discusses conditional probability and independence. It defines conditional probability as P(B|A), the probability of event B given that event A has occurred. To calculate this, one considers only the outcomes where A occurred and calculates the fraction where B also occurred. Two events A and B are independent if P(B|A) = P(B), meaning the probability of B is unaffected by the occurrence of A. The general multiplication rule for any events A and B is P(A and B) = P(A) × P(B|A) or P(A) × P(B|A). Disjoint events cannot be independent as the occurrence of one rules out the other. Conditional probabilities are best understood
probability ,and its terminologies full description
This document presents information on probability and its uses. It defines probability as the chances of an event occurring from a sample space. Probability is expressed as a number from 0 to 1. The document discusses different types of probability such as mathematical, statistical, and subjective probabilities. Key terminology used in probability such as sample space, outcomes, events, and complementary events are explained. Finally, the utility of probability is discussed for solving business, economic, and social problems including risk evaluation and predicting demand.
Mathematical Expectation And Variance
1. Mathematical expectation, or expected value, of a random variable is calculated by taking the sum of each possible outcome value multiplied by its probability of occurring.
2. For a game with multiple outcomes and their associated probabilities and payoffs, the expected value is the average amount a player can expect to win or lose over many plays. A positive expected value means the game favors the player, while a negative value means it is biased against the player.
3. Variance measures the average amount that values of a random variable deviate from the mean or expected value. Unfortunately, the document does not provide the formulas for variance or standard deviation of a random variable.
Poisson probability distribution
1. The document discusses the Poisson probability distribution, which models random processes with discrete outcomes.
2. A Poisson experiment has properties including a known average number of successes (μ) that is proportional to the region size, with extremely small regions having virtually zero probability of success.
3. Examples of Poisson applications include the number of car accidents per month or network failures per day.
Introduction to Probability and Probability Distributions
This document provides an overview of teaching basic probability and probability distributions to tertiary level teachers. It introduces key concepts such as random experiments, sample spaces, events, assigning probabilities, conditional probability, independent events, and random variables. Examples are provided for each concept to illustrate the definitions and computations. The goal is to explain the necessary probability foundations for teachers to understand sampling distributions and assessing the reliability of statistical estimates from samples.
Basic concepts of probability
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.
Probability Powerpoint
The document defines probability as the ratio of desired outcomes to total outcomes. It provides examples of calculating probabilities of outcomes from rolling a die or flipping a coin. It explains that probabilities of all outcomes must sum to 1. It also discusses calculating probabilities of multiple events using "and" or "or", and defines experimental probability as the ratio of outcomes to trials from an experiment.
Basic probability concept
This document outlines basic probability concepts, including definitions of probability, views of probability (objective and subjective), and elementary properties. It discusses calculating probabilities of events from data in tables, including unconditional/marginal probabilities, conditional probabilities, and joint probabilities. Rules of probability are presented, including the multiplicative rule that the joint probability of two events is equal to the product of the marginal probability of one event and the conditional probability of the other event given the first event. Examples are provided to illustrate key concepts.
Basic Probability
This document discusses the concept of probability. It defines probability as a measure of how likely an event is to occur. Probabilities can be described using terms like certain, likely, unlikely, and impossible. Mathematically, probabilities are often expressed as fractions, with the numerator representing the number of possible outcomes for an event and the denominator representing the total number of possible outcomes. The document provides examples to illustrate concepts like independent and conditional probabilities, as well as complementary events and the gambler's fallacy.
Hindi varnamala | hindi alphabet PPT.pdf
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
Community pharmacy- Social and preventive pharmacy UNIT 5
Covered community pharmacy topic of the subject Social and preventive pharmacy for Diploma and Bachelor of pharmacy
More Related Content
What's hot
Probability
History behind the
development of the concept
In 1654, a gambler Chevalier De Metre approached the well known Mathematician Blaise Pascal for certain dice problem. Pascal became interested in these problems and discussed it further with Pierre de Fermat. Both of them solved these problems independently. Since then this concept gained limelight.
Basic Things About The Concept
Probability is used to quantify an attitude of mind towards some uncertain proposition.
The higher the probability of an event, the more certain we are that the event will occur.
Probability ppt by Shivansh J.
The document discusses probability and chance. It defines probability as a measure of how likely an event is to occur from 0 to 1, with 1 being certain and 0 being impossible. Chance is expressed as a percentage, with 50% meaning equally likely. Examples are given of probability in weather forecasting and games. The origins and modern uses of probability are outlined in fields like traffic control, genetics, and investment returns. Predictable versus unpredictable events are distinguished. Formulae for calculating probability from sample data are provided. Random phenomena are described as having uncertain individual outcomes but regular relative frequencies over many repetitions, like coin tosses. Applications in risk assessment and commodity markets are mentioned. Reliability engineering in product design is discussed as using probability of
Probablity ppt maths
The document provides an overview of probability concepts including:
- Probability is a measure of how likely an event is, defined as the number of favorable outcomes divided by the total number of possible outcomes.
- Theoretical probability predicts outcomes without performing experiments, dealing with events as combinations of elementary outcomes.
- Random experiments may have different results each time while deterministic experiments always produce the same outcome.
- Elementary events are individual outcomes, and compound events combine multiple elementary outcomes.
- Theoretical probability of an event is the number of favorable elementary events divided by the total number of possible events.
- The probabilities of an event and its negation must sum to 1.
Probability in daily life
This document provides an introduction to probability and its applications in daily life. It defines probability as a measure of how often an event will occur if an experiment is repeated. Probability is always between 0 and 1, with 1 being a certain event and 0 being an impossible event. The document discusses random experiments, sample spaces, outcomes, events, and favorable events. It provides examples of calculating probability for events like drawing cards from a deck or selecting people with certain characteristics from a population. Overall, the document outlines basic probability concepts and terminology.
Probability class 9 ____ CBSE
This document discusses probability and related concepts. It defines probability as the study of uncertainty and notes its uses in fields like mathematics, science, and medicine. Key terms are introduced, including random experiments, trials, events, and elementary events. Experimental probability is defined based on the number of trials and outcomes. Examples are provided to demonstrate calculating probabilities and the relationship that the probabilities of all elementary events sum to 1.
Probability
This document defines key concepts in probability and provides examples. It discusses probability vocabulary like sample space, outcome, trial, and event. It defines probability as the number of times a desired outcome occurs over total trials. Events are independent if the outcome of one does not impact others, and mutually exclusive if they cannot occur together. The addition and multiplication rules for probability are explained. Conditional probability describes the probability of a second event depending on the first occurring. Counting techniques are discussed for finding total possible outcomes of combined experiments. Review questions are provided to test understanding of the material.
Conditional-Probability-Powerpoint.pptx
The document defines conditional probability as the probability of an event occurring given that another event has already occurred. It provides the formula for conditional probability as P(B|A) = P(A and B) / P(A). Several examples are worked through applying this formula to calculate conditional probabilities in different contexts like selecting chips from a box, survey responses, exam scores, and dice rolls. Exercises at the end provide additional practice problems for calculating conditional probabilities.
Probability
Probability not Higher Level....This PPT is for beginners or for those who learnt or to study PROBABILITY........... :):):):):)
introduction to probability
The document provides an introduction to probability. It discusses:
- What probability is and the definition of probability as a number between 0 and 1 that expresses the likelihood of an event occurring.
- A brief history of probability including its development in French society in the 1650s and key figures like James Bernoulli, Abraham De Moivre, and Pierre-Simon Laplace.
- Key terms used in probability like events, outcomes, sample space, theoretical probability, empirical probability, and subjective probability.
- The three types of probability: theoretical, empirical, and subjective probability.
- General probability rules including: the probability of impossible/certain events; the sum of all probabilities equaling 1; complements
Bayes Theorem
Bayes' Theorem relates prior probabilities, conditional probabilities, and posterior probabilities. It provides a mathematical rule for updating estimates based on new evidence or observations. The theorem states that the posterior probability of an event is equal to the conditional probability of the event given the evidence multiplied by the prior probability, divided by the probability of the evidence. Bayes' Theorem can be used to calculate conditional probabilities, like the probability of a woman having breast cancer given a positive mammogram result, or the probability that a part came from a specific supplier given that it is non-defective. It is widely applicable in science, medicine, and other fields for revising hypotheses based on new data.
Conditional Probability
Conditional probability is the probability of an event occurring given that another event has occurred. It is calculated as the probability of both events occurring divided by the probability of the first event. An example is given of calculating the probability of drawing two white balls in succession from an urn without replacement. The formula for conditional probability is derived as the probability of events A and B occurring divided by the probability of A. This is demonstrated using an example of finding the percentage of friends who like chocolate that also like strawberry.
Conditional probability
This document discusses conditional probability and independence. It defines conditional probability as P(B|A), the probability of event B given that event A has occurred. To calculate this, one considers only the outcomes where A occurred and calculates the fraction where B also occurred. Two events A and B are independent if P(B|A) = P(B), meaning the probability of B is unaffected by the occurrence of A. The general multiplication rule for any events A and B is P(A and B) = P(A) × P(B|A) or P(A) × P(B|A). Disjoint events cannot be independent as the occurrence of one rules out the other. Conditional probabilities are best understood
probability ,and its terminologies full description
This document presents information on probability and its uses. It defines probability as the chances of an event occurring from a sample space. Probability is expressed as a number from 0 to 1. The document discusses different types of probability such as mathematical, statistical, and subjective probabilities. Key terminology used in probability such as sample space, outcomes, events, and complementary events are explained. Finally, the utility of probability is discussed for solving business, economic, and social problems including risk evaluation and predicting demand.
Mathematical Expectation And Variance
1. Mathematical expectation, or expected value, of a random variable is calculated by taking the sum of each possible outcome value multiplied by its probability of occurring.
2. For a game with multiple outcomes and their associated probabilities and payoffs, the expected value is the average amount a player can expect to win or lose over many plays. A positive expected value means the game favors the player, while a negative value means it is biased against the player.
3. Variance measures the average amount that values of a random variable deviate from the mean or expected value. Unfortunately, the document does not provide the formulas for variance or standard deviation of a random variable.
Poisson probability distribution
1. The document discusses the Poisson probability distribution, which models random processes with discrete outcomes.
2. A Poisson experiment has properties including a known average number of successes (μ) that is proportional to the region size, with extremely small regions having virtually zero probability of success.
3. Examples of Poisson applications include the number of car accidents per month or network failures per day.
Introduction to Probability and Probability Distributions
This document provides an overview of teaching basic probability and probability distributions to tertiary level teachers. It introduces key concepts such as random experiments, sample spaces, events, assigning probabilities, conditional probability, independent events, and random variables. Examples are provided for each concept to illustrate the definitions and computations. The goal is to explain the necessary probability foundations for teachers to understand sampling distributions and assessing the reliability of statistical estimates from samples.
Basic concepts of probability
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.
Probability Powerpoint
The document defines probability as the ratio of desired outcomes to total outcomes. It provides examples of calculating probabilities of outcomes from rolling a die or flipping a coin. It explains that probabilities of all outcomes must sum to 1. It also discusses calculating probabilities of multiple events using "and" or "or", and defines experimental probability as the ratio of outcomes to trials from an experiment.
Basic probability concept
This document outlines basic probability concepts, including definitions of probability, views of probability (objective and subjective), and elementary properties. It discusses calculating probabilities of events from data in tables, including unconditional/marginal probabilities, conditional probabilities, and joint probabilities. Rules of probability are presented, including the multiplicative rule that the joint probability of two events is equal to the product of the marginal probability of one event and the conditional probability of the other event given the first event. Examples are provided to illustrate key concepts.
Basic Probability
This document discusses the concept of probability. It defines probability as a measure of how likely an event is to occur. Probabilities can be described using terms like certain, likely, unlikely, and impossible. Mathematically, probabilities are often expressed as fractions, with the numerator representing the number of possible outcomes for an event and the denominator representing the total number of possible outcomes. The document provides examples to illustrate concepts like independent and conditional probabilities, as well as complementary events and the gambler's fallacy.
What's hot (20)
probability ,and its terminologies full description
probability ,and its terminologies full description
Introduction to Probability and Probability Distributions
Introduction to Probability and Probability Distributions
Recently uploaded
Hindi varnamala | hindi alphabet PPT.pdf
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
Community pharmacy- Social and preventive pharmacy UNIT 5
Covered community pharmacy topic of the subject Social and preventive pharmacy for Diploma and Bachelor of pharmacy
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
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.
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Main Java[All of the Base Concepts}.docx
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar KanvariaHow to Setup Warehouse & Location in Odoo 17 Inventory
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS Template 2023-2024 by: Irene S. Rueco
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
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.
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
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.
Digital Artifact 1 - 10VCD Environments Unit
Digital Artifact 1 - 10VCD Environments Unit - NGV Pavilion Concept Design
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Recently uploaded (20)
Community pharmacy- Social and preventive pharmacy UNIT 5
Community pharmacy- Social and preventive pharmacy UNIT 5
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
Pride Month Slides 2024 David Douglas School District
Pride Month Slides 2024 David Douglas School District
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Executive Directors Chat Leveraging AI for Diversity, Equity, and Inclusion
Real life use of probability.
- 3. PROBABILITY • An event for an experiment is the collection of some outcomes of the experiment. • The experiment or emprical probability of an event (E) . P(E) = Number of trials in E has happened Total number of trials • Probability of an event lies between 0 & 1.
- 4. Real Life Use Of Probability • The boys of streets wants to play cricket . • But are not able to understand which team will take what Bat or Ball. • So, they decide to toss the coin of Rs. 1
- 5. The boys choose Head. Now, they wants to know what is the probability of getting Head.
- 6. S = Sample space = maximum number of outcomes. • When single coin is tossed S = (head , tales) • Therefore, P(E) = No. (E) No. (S) • P (E) = 1 2. = 0.5
- 7. Made By - Diyansh Patel