This document discusses key concepts in probability and statistics such as population, sample, random experiments, sample space, events, and types of events. It provides examples and exercises to illustrate these concepts. Specifically, it defines a random experiment as a process that can be repeated under similar conditions leading to well-defined but unpredictable outcomes. The sample space represents all possible outcomes, while an event is a subset of outcomes of interest. Events can be elementary, impossible, or sure depending on whether they consist of one, no, or all possible outcomes.
Random Experiment, Sample Space, EventsSandeep Patel
The document discusses random experiments, events, and sample spaces. It provides examples of tossing a coin and rolling a die as random experiments. For coin tossing, the sample space is {Heads, Tails} and the possible events are obtaining heads or tails. For die rolling, the sample space is the set of numbers that can appear on the die {1,2,3,4,5,6} and an event could be rolling a 1, 2, or 3. An event is any outcome or subset of outcomes from the sample space, which is the set of all possible results of an experiment.
This document discusses evaluating point estimators. It defines mean square error as an indicator for determining the worth of an estimator. There is rarely a single estimator that minimizes mean square error for all possible parameter values. Unbiased estimators, where the expected value equals the parameter, are commonly used. Bias is defined as the expected value of the estimator minus the parameter. Combining independent unbiased estimators results in an estimator with variance equal to the weighted sum of the individual variances. The mean square error of any estimator is equal to its variance plus the square of its bias. Examples are provided to illustrate evaluating bias and finding mean and variance of estimators.
This presentation discusses binomial probability distributions through the following key points:
- It defines basic terminology related to random experiments, events, and variables. The binomial distribution specifically describes discrete data from Bernoulli processes.
- It outlines the notation and assumptions for binomial distributions, including that there are two possible outcomes for each trial (success/failure), a fixed number of trials, and constant probabilities of success/failure.
- It presents three methods for calculating binomial probabilities: the binomial probability formula, table method, and using technology like Excel.
- It discusses measures of central tendency and dispersion for binomial distributions and how the shape of the distribution depends on the number of trials and probability of success.
- Real-world
The central limit theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, even if the population is not normally distributed. It provides the mean and standard deviation of the sampling distribution of the sample mean. The document gives the definition of the central limit theorem and provides an example of how to use it to calculate probabilities related to the sample mean of a large normally distributed population.
: 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 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.
1) The document introduces basic concepts of probability such as sample spaces, events, outcomes, and how to calculate classical and empirical probabilities.
2) It discusses approaches to determining probability including classical, empirical, and subjective probabilities. Simulations can also be used to estimate probabilities.
3) Examples are provided to illustrate calculating probabilities using classical and empirical approaches for single and compound events with different sample spaces.
Random Experiment, Sample Space, EventsSandeep Patel
The document discusses random experiments, events, and sample spaces. It provides examples of tossing a coin and rolling a die as random experiments. For coin tossing, the sample space is {Heads, Tails} and the possible events are obtaining heads or tails. For die rolling, the sample space is the set of numbers that can appear on the die {1,2,3,4,5,6} and an event could be rolling a 1, 2, or 3. An event is any outcome or subset of outcomes from the sample space, which is the set of all possible results of an experiment.
This document discusses evaluating point estimators. It defines mean square error as an indicator for determining the worth of an estimator. There is rarely a single estimator that minimizes mean square error for all possible parameter values. Unbiased estimators, where the expected value equals the parameter, are commonly used. Bias is defined as the expected value of the estimator minus the parameter. Combining independent unbiased estimators results in an estimator with variance equal to the weighted sum of the individual variances. The mean square error of any estimator is equal to its variance plus the square of its bias. Examples are provided to illustrate evaluating bias and finding mean and variance of estimators.
This presentation discusses binomial probability distributions through the following key points:
- It defines basic terminology related to random experiments, events, and variables. The binomial distribution specifically describes discrete data from Bernoulli processes.
- It outlines the notation and assumptions for binomial distributions, including that there are two possible outcomes for each trial (success/failure), a fixed number of trials, and constant probabilities of success/failure.
- It presents three methods for calculating binomial probabilities: the binomial probability formula, table method, and using technology like Excel.
- It discusses measures of central tendency and dispersion for binomial distributions and how the shape of the distribution depends on the number of trials and probability of success.
- Real-world
The central limit theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, even if the population is not normally distributed. It provides the mean and standard deviation of the sampling distribution of the sample mean. The document gives the definition of the central limit theorem and provides an example of how to use it to calculate probabilities related to the sample mean of a large normally distributed population.
: 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 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.
1) The document introduces basic concepts of probability such as sample spaces, events, outcomes, and how to calculate classical and empirical probabilities.
2) It discusses approaches to determining probability including classical, empirical, and subjective probabilities. Simulations can also be used to estimate probabilities.
3) Examples are provided to illustrate calculating probabilities using classical and empirical approaches for single and compound events with different sample spaces.
Discrete probability distribution (complete)ISYousafzai
This document discusses discrete random variables. It begins by defining a random variable as a function that assigns a numerical value to each outcome of an experiment. There are two types of random variables: discrete and continuous. Discrete random variables have a countable set of possible values, while continuous variables can take any value within a range. Examples of discrete variables include the number of heads in a coin flip and the total value of dice. The document then discusses how to describe the probabilities associated with discrete random variables using lists, histograms, and probability mass functions.
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.
Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
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
The document discusses random variables and vectors. It defines random variables as functions that assign outcomes of random experiments to real numbers. There are two types of random variables: discrete and continuous. Random variables are characterized by their expected value, variance/standard deviation, and other moments. Random vectors are multivariate random variables. Key concepts covered include probability mass functions, probability density functions, expected value, variance, and how these properties change when random variables are scaled or combined linearly.
The document provides an overview of hypothesis testing, including:
1) The process of hypothesis testing involves deciding between a null and alternative hypothesis based on sample data.
2) The null hypothesis states there is no difference from the claimed population parameter, while the alternative hypothesis states there is a difference.
3) Hypothesis tests use critical values and rejection regions based on the level of significance to determine whether to reject or fail to reject the null hypothesis.
4) Examples are provided to demonstrate conducting hypothesis tests using z-tests and t-tests, and interpreting the results.
This presentation covered the following topics :
1. Random experiments
2. Sample space
3. Events and their probability
4. random variable probability distribution
5. t - Test
6. paired t - Test
7. F- Test
8. Comparison of results of above tests
and is useful for B.Sc , M.Sc mathematics and statistics students.
Statistical inference concept, procedure of hypothesis testingAmitaChaudhary19
This document discusses hypothesis testing in statistical inference. It defines statistical inference as using probability concepts to deal with uncertainty in decision making. Hypothesis testing involves setting up a null hypothesis and alternative hypothesis about a population parameter, collecting sample data, and using statistical tests to determine whether to reject or fail to reject the null hypothesis. The key steps are setting hypotheses, choosing a significance level, selecting a test criterion like t, F or chi-squared distributions, performing calculations on sample data, and making a decision to reject or fail to reject the null hypothesis based on the significance level.
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.
- Point estimation involves using sample data to calculate a single number (point estimate) that estimates an unknown population parameter.
- A point estimator is a statistic used to calculate the point estimate. For example, when estimating an unknown population mean μ, the sample mean x̅ is a point estimator for μ.
- An unbiased estimator has an expected value equal to the true population parameter value. A biased estimator has an expected value that is not equal to the true parameter value.
- Common methods for finding estimators include maximum likelihood estimation and the method of moments. Maximum likelihood estimation identifies the value of the parameter that maximizes the likelihood function based on the sample data. The method of moments equates sample moments
A random variable X has a continuous uniform distribution if its probability density function f(x) is constant over the interval (α, β). The uniform distribution has a probability density function f(x) = k for α < x < β, where k is a constant, and is equal to 0 otherwise. All values from α to β are equally likely to occur, meaning the probability of X falling in any sub-interval of (α, β) is equal regardless of the interval's position within the range.
This document provides an introduction to hypothesis testing including:
1. The 5 steps in a hypothesis test: set up null and alternative hypotheses, define test procedure, collect data, decide whether to reject null hypothesis, interpret results.
2. Large sample tests for the mean involve testing if the population mean is equal to or not equal to a specified value using a test statistic that follows a normal distribution.
3. Type I and Type II errors occur when the decision made based on the hypothesis test does not match the actual truth - a Type I error rejects the null hypothesis when it is true, a Type II error fails to reject the null when it is false. The probability of each error can be minimized by choosing
1. Continuous random variables are defined over intervals rather than discrete points. The probability that a continuous random variable takes on a value in an interval from a to b is given by an integral of the probability density function f(x) over that interval.
2. The probability density function f(x) defines the probabilities of intervals of the continuous random variable rather than individual points. It has the properties that it is always nonnegative and its integral over all values is 1.
3. The cumulative distribution function F(x) gives the probability that the random variable takes on a value less than or equal to x. It is defined as the integral of the probability density function from negative infinity to x.
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.
This document provides an overview of hypothesis testing including:
- Defining null and alternative hypotheses
- Types of errors like Type I and Type II
- Test statistics and significance levels for comparing means, proportions, and standard deviations of one and two populations
- Examples are given for hypothesis tests on population means, proportions, and comparing two population means.
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 document provides an overview of probability concepts including:
- Probability is a numerical measure of the likelihood of an event occurring, ranging from 0 (impossible) to 1 (certain).
- An experiment generates outcomes that make up the sample space. Events are collections of outcomes.
- Simple events have a defined probability based on being equally likely. The probability of an event is the sum of probabilities of the simple events it contains.
- Rules like the multiplication rule for independent events and additive rule for unions allow calculating probabilities of composite events.
- Complement and conditional probabilities relate the probabilities of events. Independent events do not influence each other's probabilities.
The document discusses various sampling methods used in research including population, sample, random sampling, cluster sampling, and systematic random sampling. Random sampling methods aim to select a sample that accurately represents the population without bias. Cluster sampling divides the population into clusters or groups and then randomly selects clusters. Systematic random sampling selects every nth unit from a randomly chosen starting point with a fixed interval between selections. Both cluster and systematic random sampling can reduce costs compared to simple random sampling of large, dispersed populations.
This document provides instructions for playing a trivia game that involves dividing into teams, taking turns asking questions, spinning a wheel to determine points for correct answers, and tracking scores. Players ask questions to other teams, spin a wheel if answered correctly to determine points awarded, and record points in a scorebox until out of questions.
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.
Discrete probability distribution (complete)ISYousafzai
This document discusses discrete random variables. It begins by defining a random variable as a function that assigns a numerical value to each outcome of an experiment. There are two types of random variables: discrete and continuous. Discrete random variables have a countable set of possible values, while continuous variables can take any value within a range. Examples of discrete variables include the number of heads in a coin flip and the total value of dice. The document then discusses how to describe the probabilities associated with discrete random variables using lists, histograms, and probability mass functions.
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.
Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
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
The document discusses random variables and vectors. It defines random variables as functions that assign outcomes of random experiments to real numbers. There are two types of random variables: discrete and continuous. Random variables are characterized by their expected value, variance/standard deviation, and other moments. Random vectors are multivariate random variables. Key concepts covered include probability mass functions, probability density functions, expected value, variance, and how these properties change when random variables are scaled or combined linearly.
The document provides an overview of hypothesis testing, including:
1) The process of hypothesis testing involves deciding between a null and alternative hypothesis based on sample data.
2) The null hypothesis states there is no difference from the claimed population parameter, while the alternative hypothesis states there is a difference.
3) Hypothesis tests use critical values and rejection regions based on the level of significance to determine whether to reject or fail to reject the null hypothesis.
4) Examples are provided to demonstrate conducting hypothesis tests using z-tests and t-tests, and interpreting the results.
This presentation covered the following topics :
1. Random experiments
2. Sample space
3. Events and their probability
4. random variable probability distribution
5. t - Test
6. paired t - Test
7. F- Test
8. Comparison of results of above tests
and is useful for B.Sc , M.Sc mathematics and statistics students.
Statistical inference concept, procedure of hypothesis testingAmitaChaudhary19
This document discusses hypothesis testing in statistical inference. It defines statistical inference as using probability concepts to deal with uncertainty in decision making. Hypothesis testing involves setting up a null hypothesis and alternative hypothesis about a population parameter, collecting sample data, and using statistical tests to determine whether to reject or fail to reject the null hypothesis. The key steps are setting hypotheses, choosing a significance level, selecting a test criterion like t, F or chi-squared distributions, performing calculations on sample data, and making a decision to reject or fail to reject the null hypothesis based on the significance level.
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.
- Point estimation involves using sample data to calculate a single number (point estimate) that estimates an unknown population parameter.
- A point estimator is a statistic used to calculate the point estimate. For example, when estimating an unknown population mean μ, the sample mean x̅ is a point estimator for μ.
- An unbiased estimator has an expected value equal to the true population parameter value. A biased estimator has an expected value that is not equal to the true parameter value.
- Common methods for finding estimators include maximum likelihood estimation and the method of moments. Maximum likelihood estimation identifies the value of the parameter that maximizes the likelihood function based on the sample data. The method of moments equates sample moments
A random variable X has a continuous uniform distribution if its probability density function f(x) is constant over the interval (α, β). The uniform distribution has a probability density function f(x) = k for α < x < β, where k is a constant, and is equal to 0 otherwise. All values from α to β are equally likely to occur, meaning the probability of X falling in any sub-interval of (α, β) is equal regardless of the interval's position within the range.
This document provides an introduction to hypothesis testing including:
1. The 5 steps in a hypothesis test: set up null and alternative hypotheses, define test procedure, collect data, decide whether to reject null hypothesis, interpret results.
2. Large sample tests for the mean involve testing if the population mean is equal to or not equal to a specified value using a test statistic that follows a normal distribution.
3. Type I and Type II errors occur when the decision made based on the hypothesis test does not match the actual truth - a Type I error rejects the null hypothesis when it is true, a Type II error fails to reject the null when it is false. The probability of each error can be minimized by choosing
1. Continuous random variables are defined over intervals rather than discrete points. The probability that a continuous random variable takes on a value in an interval from a to b is given by an integral of the probability density function f(x) over that interval.
2. The probability density function f(x) defines the probabilities of intervals of the continuous random variable rather than individual points. It has the properties that it is always nonnegative and its integral over all values is 1.
3. The cumulative distribution function F(x) gives the probability that the random variable takes on a value less than or equal to x. It is defined as the integral of the probability density function from negative infinity to x.
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.
This document provides an overview of hypothesis testing including:
- Defining null and alternative hypotheses
- Types of errors like Type I and Type II
- Test statistics and significance levels for comparing means, proportions, and standard deviations of one and two populations
- Examples are given for hypothesis tests on population means, proportions, and comparing two population means.
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 document provides an overview of probability concepts including:
- Probability is a numerical measure of the likelihood of an event occurring, ranging from 0 (impossible) to 1 (certain).
- An experiment generates outcomes that make up the sample space. Events are collections of outcomes.
- Simple events have a defined probability based on being equally likely. The probability of an event is the sum of probabilities of the simple events it contains.
- Rules like the multiplication rule for independent events and additive rule for unions allow calculating probabilities of composite events.
- Complement and conditional probabilities relate the probabilities of events. Independent events do not influence each other's probabilities.
The document discusses various sampling methods used in research including population, sample, random sampling, cluster sampling, and systematic random sampling. Random sampling methods aim to select a sample that accurately represents the population without bias. Cluster sampling divides the population into clusters or groups and then randomly selects clusters. Systematic random sampling selects every nth unit from a randomly chosen starting point with a fixed interval between selections. Both cluster and systematic random sampling can reduce costs compared to simple random sampling of large, dispersed populations.
This document provides instructions for playing a trivia game that involves dividing into teams, taking turns asking questions, spinning a wheel to determine points for correct answers, and tracking scores. Players ask questions to other teams, spin a wheel if answered correctly to determine points awarded, and record points in a scorebox until out of questions.
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.
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.
What do you know about probability? Design a game, or find a game that uses probability, and explain the chance of each possible outcome (eg: the chance of throwing a 6 on 2 dice, landing on red on a spinner).
1. Probability is the study of randomness and uncertainty of outcomes from experiments or processes. It allows us to make statements about the likelihood of events occurring.
2. Events are outcomes or sets of outcomes from random experiments. The probability of an event is calculated based on the number of outcomes in the event compared to the total number of possible outcomes.
3. Conditional probability is the likelihood of one 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. Conditional probabilities are useful for problems involving dependent events.
Walmart prioritizes low prices over health and nutrition, using prominent promotional displays and deals to draw in customers across all demographics. The store focuses on covering many product categories at low prices in bulk while also appealing to customers' desires for candy, junk food, and nostalgic items through large aisles and displays that help alleviate guilt over such purchases. The goal is providing the best value for the money to attract and retain curious but loyal customers.
Kicak Media Holdings will run Snickers' 2013 advertising campaign with the goal of increasing sales by 15% and market share by 0.2% through a "Spin to Win" promotion. The $178.5 million campaign uses fully integrated media like TV, radio, internet and direct mail featuring a prize wheel. Consumers can enter codes from packages online for a chance to win prizes totaling $520,000, including $100,000 grand prize. The promotion aims to reinforce Snickers' fun image and reinvigorate consumers from June to September 2013. Post-campaign evaluations will assess awareness, recall and customer feedback.
The document lists 5 Galaxy chocolate advertisements from different years between 1980-1998, including ones with the slogans "Country Taste" from 1980, "Reading Companion" with no specified year, and "Why Have Cotton, When You Could Have Silk?" running in both 1990 and 1998. It also includes an advertisement featuring a train from an unspecified year.
The document analyzes several Snickers advertisements. It discusses the purpose, tone, argument, emotional appeals, logical fallacies, and implications of advertisements promoting Snickers as a solution for hunger-induced irritability and poor decision making. The advertisements generally use humor and celebrity endorsements to argue that eating a Snickers will improve mood and prevent irrational behavior when hungry.
This document provides an introduction to random variables. It defines random variables as functions that assign real numbers to outcomes of an experiment. Random variables can be either discrete or continuous depending on whether their possible values are countable or uncountable. The document also defines probability mass functions (pmf) which describe the probabilities of discrete random variables taking on particular values. Expectation is introduced as a way to summarize random variables using a single number by taking a weighted average of all possible outcomes.
Snickers Ad Campaign “You’re Not You When You’re Hungry”Edmund Siah-Armah
The results indicate that contingency approach followed by the global advertising strategy contributes to the powerful effect of advertising through the best delivery of the value to the consumers.
Snickers nació en 1930 y es una de las barras de chocolate más conocidas y vendidas en el mundo. Se le nombró en honor a un caballo favorito de la familia Mars y tuvo éxito construyendo mercados individuales con campañas de marketing locales antes de proponerse una posición global con un discurso más homogéneo.
Real life situation's example on PROBABILITYJayant Namrani
This document discusses probability and how it relates to real-life situations through examples. It explains that probability is a number between 0 and 1 that indicates the likelihood of an event occurring. Even though outcomes like a coin toss result in either heads or tails, probability allows for numbers between 0 and 1 by considering the long run of many trials. The document then gives examples of calculating probabilities using a bag of marbles and tossing a coin to illustrate how probability models are used to simplify real-world scenarios.
Power Point Presentation on Question TagsNayana Thampi
The document discusses question tags, which are small questions added to the end of statements. Question tags are used to seek agreement or confirmation from the listener. The document provides examples of positive and negative question tags that can be added to both positive and negative statements. It also gives examples of question tags being used correctly with different statements.
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.
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
The document discusses probability and introduces several online activities and games for students to learn about probability. Students are instructed to visit specific pages on BrainPop, Maths Online, Interactivate: Activities, Skillswise: Numbers, Johnnie's Math Page, and take an online quiz. After completing the activities, students are asked to write a paragraph about what they learned about probability and come up with their own probability experiment.
This document discusses question tags (QTs), which are short questions added to the end of statements. It provides the following information:
- QTs are used to ask someone to agree with a statement or to check if something is true, with intonation going down or up respectively.
- QTs are formed using the same subject as the statement but in pronoun form, and the corresponding affirmative or negative auxiliary verb depending on if the main verb is negative or affirmative.
- Exceptions include using "shall we" after "Let's", "aren't I" after "I'm", and "will/would/could you" after imperatives. Negative words use a positive tag. Indefinite pronou
This document provides an overview of key concepts in probability, including experiment, event, sample space, unions and intersections of events, mutually exclusive events, and methods of assigning probabilities. It discusses experiments, events, and sample spaces. It defines union and intersection of sets. It covers classical, relative frequency, and subjective probabilities. It also discusses rules for counting sample points, including multiplication, permutation, and combination. Examples are provided to illustrate calculating probabilities.
Probability Theory: Probabilistic Model of an Experiment & Sample-point ApproachAr-ar Agustin
The document outlines key concepts in probability theory including definitions, approaches to calculating probability, axioms, and the sample-point approach. It defines terms like experiment, sample space, event, and probability. It describes three approaches to assigning probability values: relative frequency based on experimental results, classical/theoretical based on an experimental model, and subjective based on researcher assignment. It provides examples using a dice experiment to illustrate events as subsets of the sample space.
A random variable is a mathematical concept used to describe the outcome of a random process or experiment. It is a variable that takes on different values based on the outcome of a random event. In mathematical terms, a random variable is a function that maps the outcomes of a random experiment to a set of real numbers.
There are two types of random variables: discrete and continuous. A discrete random variable can take on only a finite or countably infinite number of values, such as the number of heads in a sequence of coin flips. A continuous random variable, on the other hand, can take on any value within a given range, such as the height of a person.
The probability distribution of a random variable defines the likelihood of each possible outcome of the random process or experiment. For a discrete random variable, this is represented by a probability mass function (PMF), which gives the probability of each possible outcome. For a continuous random variable, the probability distribution is represented by a probability density function (PDF), which gives the relative likelihood of different outcomes within a given range.
Another important concept in the study of random variables is expected value, also known as the mean or average of a random variable. The expected value represents the long-term average of the values that a random variable takes on, and is calculated as the weighted sum of the possible values, where the weights are the probabilities of each outcome.
Random variables are used in a variety of mathematical and statistical applications, including decision theory, finance, and quality control. They also play a central role in the study of probability and statistics, and are used to model and analyze complex systems and phenomena in fields such as physics, engineering, and economics.
In conclusion, random variables are a powerful tool for describing the outcomes of random processes and experiments, and provide a framework for understanding the probabilistic behavior of complex systems. Understanding and using random variables is essential for making informed decisions and solving problems in a variety of fields and applications.A random variable is a mathematical concept that describes the outcome of a random event. It is a variable that can take on different values based on the outcome of the event. There are two types of random variables: discrete and continuous. A discrete random variable can take on only a finite or countably infinite number of values, such as the number of heads in a sequence of coin flips. A continuous random variable, on the other hand, can take on any value within a given range, such as the height of a person. The probability distribution of a random variable defines the likelihood of each possible outcome and is used to calculate the expected value, which is the long-term average of the values that a random variable takes on. Random variables are widely used in mathematics and statistics, and are essen
This document provides an overview of probability concepts including chance experiments, sample spaces, events, Venn diagrams, independence, conditional probability, and Bayes' rule. Key points covered include defining probability as a limit of relative frequency, and how to calculate probabilities of events using formulas like the addition rule, multiplication rule, and Bayes' rule. Examples are provided to illustrate concepts like conditional probability and working through word problems step-by-step.
This document introduces probability and key probability concepts. It defines an experiment as any process with uncertain outcomes, and a sample space as the set of all possible outcomes of an experiment. Events are defined as subsets of outcomes from the sample space, and can be simple (a single outcome) or compound (multiple outcomes). Several examples are provided to illustrate sample spaces and events.
Basic probability Concepts and its application By Khubaib Razakhubiab raza
introduction of probability probability defination and its properties after that difference between probability and permutation in the last Discuss about imporatnace of Probabilty in Computer Science
7-Experiment, Outcome and Sample Space.pptxssuserdb3083
1) 1.56 and 1.0 cannot be probabilities because probabilities must be between 0 and 1.
2) 0.46, 0.09, 0.96, 0.25, 0.02 can be probabilities because they are between 0 and 1.
3) a) The probability of obtaining a number less than 4 is 3/6 = 1/2
b) The probability of obtaining a number between 3 and 6 is 4/6 = 2/3
This document provides an introduction to probability theory. It defines key concepts like random experiments, sample spaces, events, and probabilities. Random experiments are experiments with unpredictable outcomes but a known set of possible results. The sample space is the set of all possible outcomes. Events are subsets of outcomes. Probabilities assign a numerical value between 0 and 1 to events, representing the likelihood they will occur. Two common methods to assign probabilities are the classical method, which uses equally likely outcomes, and the relative frequency method, which uses the limit of observed frequencies over many trials. Probability theory models and studies random processes, while statistics draws inferences from random process data.
This document defines common statistical terms and provides examples of sample spaces and events for random experiments involving a coin toss, dice roll, and child gender combinations. It defines statistics, parameters, probability, sample space, event, and random experiment. The examples construct sample spaces for coin tossing, dice rolling, and child gender combinations, then identify events within those sample spaces, such as rolling an even number or having two boys.
PROBABILITY BY ZOEN CUTE KAAYO SA KATANAN.pptxZorennaPlanas1
Probability is a branch of mathematics that enables us to predict the occurrence of an event as a result of an experiment. The probability of an event is calculated by taking the number of outcomes in the event and dividing it by the total number of possible outcomes. Simple events consist of a single outcome, while compound events consist of two or more simple events. Examples of calculating probability are given for simple events like rolling a die or drawing balls from a jar, and compound events like rolling two dice. The importance of probability in decision making in real life is discussed.
This document provides an overview of key concepts in probability theory:
- An experiment yields possible outcomes called a sample space. Events are subsets of outcomes. Random variables assign values to outcomes.
- Probability is a measure of certainty that an event will occur, ranging from 0 (impossible) to 1 (certain). It can be defined in different ways.
- The frequentist definition is the limit of relative frequencies of an event over many trials. The Bayesian definition is a degree of belief in an event. The Laplacian definition assumes all outcomes are equally likely initially.
- Examples demonstrate random variables, events, and calculating probabilities based on the sample space and outcomes of an experiment. Key terms like sample space, event,
The document defines key terms related to experimental probability such as experiment, outcome, sample space, and experimental probability. It then provides examples of experiments with different outcomes and sample spaces. Students are asked to identify outcomes and sample spaces for given experiments. They are also asked to calculate experimental probabilities based on data from experiments involving selecting marbles from a bag, cards from a deck, and coin tosses.
The document defines key terms related to experimental probability such as experiment, outcome, sample space, and experimental probability. It then provides examples of experiments with different outcomes and sample spaces. Students are asked to identify outcomes and sample spaces for given experiments. They are also asked to calculate experimental probabilities based on data from experiments involving selecting marbles from a bag, cards from a deck, and coin tosses.
Math 1300: Section 8-1 Sample Spaces, Events, and ProbabilityJason Aubrey
The document discusses probability theory and its application to random experiments. It defines key terms like sample space, event, simple event, and compound event. The sample space is the set of all possible outcomes of an experiment. Events are subsets of the sample space and can be simple (contain one outcome) or compound (contain multiple outcomes). An example experiment is rolling two dice, where the sample space is the set of all possible (number on die 1, number on die 2) outcomes.
Random Variable and Probability Distribution.pptxNancy Madarang
This document introduces random variables and probability distributions. It defines key terms like random variable, sample space, discrete and continuous random variables. It provides examples of discrete random variables like the number of heads from coin tosses and examples of continuous random variables like height. The document illustrates how to determine the possible values a random variable can take and introduces the concept of a probability distribution.
The document discusses the sample mean formula and provides examples of calculating the sample mean. The sample mean formula is defined as the sum of all terms divided by the number of terms. Three examples are given calculating the sample mean of various data sets, including test scores, heights of friends, and time taken to finish homework.
This document discusses key concepts of probability, including experimental probability, theoretical probability, outcomes, events, sample space, impossible events, sure events, equally likely events, and mutually exclusive events. It provides examples to illustrate experimental probability, which is calculated based on results of repeated experiments, and theoretical probability, which is calculated based on the number of possible outcomes. The document aims to help readers understand the different types of probability and how to distinguish between experimental and theoretical probability.
The document provides an introduction to probability. It defines probability as a numerical index of the likelihood of an event occurring between 0 and 1. Examples are given where probability is expressed as a percentage or decimal. Key terms are defined, including experiment, outcome, event, and sample space. Common types of probability such as subjective, objective/classic, and empirical probabilities are explained. Formulas and examples are provided to demonstrate how to calculate probabilities of events.
The document discusses statistical methods including probability fundamentals, inference, and multivariate methods. It covers topics such as probability review, random variables, sampling distributions, the central limit theorem, confidence intervals, bootstrapping, principal component analysis, singular value decomposition, and independence component analysis. Formulas and examples are provided for key concepts like the sample mean, sample variance, and confidence intervals.
The document discusses probability theory and concepts like sample space, outcomes, events, and the probability of events occurring. It provides examples of calculating probabilities, including rolling dice. The key points are:
- Probability theory is the study of chance using mathematics
- An experiment is a situation with possible results called outcomes
- The sample space includes all possible outcomes
- An event is a subset of outcomes of interest within the sample space
- Probability is calculated as the number of favorable outcomes divided by the total number of possible outcomes
- Examples are provided to demonstrate calculating probabilities, such as the probability of rolling a 7 when rolling two fair dice.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
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.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
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.
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 workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
1. What have we learned so far?What have we learned so far?
POPULATION
SAMPLE
• We describe our sample using measurements and data presentation tools
• We study the sample to make inferences about the population
• What permits us to make the inferential jump from sample to population?
FDT, Graphs, Mean,
Median, Mode,
Standard Deviation,
Variance
3. Example: TOSSING A COINExample: TOSSING A COIN
Suppose a coin is tossed onceSuppose a coin is tossed once
and the up face is recordedand the up face is recorded
The result we see andThe result we see and
recorded is called anrecorded is called an
OBSERVATION oror
MEASUREMENT
The process of making an
observation is called an
EXPERIMENT.
4. Definition:Definition:
RANDOM EXPERIMENTRANDOM EXPERIMENT
Is aIs a processprocess oror procedureprocedure,, repeatable underrepeatable under
basically the same conditionbasically the same condition (this repetition(this repetition
is commonly called ais commonly called a TRIALTRIAL)), leading to, leading to well-well-
defined outcomesdefined outcomes..
It isIt is randomrandom because we can never tell inbecause we can never tell in
advance what the outcome/realization is goingadvance what the outcome/realization is going
to be, even if we can specify what the possibleto be, even if we can specify what the possible
outcomes are.outcomes are.
5. Example: TOSSING A DIEExample: TOSSING A DIE
Consider the simpleConsider the simple
random experiment ofrandom experiment of
tossing a die andtossing a die and
observing the numberobserving the number
on the up face.on the up face.
There are sixThere are six basic
possible outcomes toto
this random experiment.this random experiment.
1.1. Observe aObserve a 11
2.2. Observe aObserve a 22
3.3. Observe aObserve a 33
4.4. Observe aObserve a 44
5.5. Observe aObserve a 55
6.6. Observe aObserve a 66
6. Definitions:Definitions:
SAMPLE POINT & SAMPLE SPACESAMPLE POINT & SAMPLE SPACE
AA SAMPLE POINT is the most basicis the most basic
outcome of a random experiment.outcome of a random experiment.
TheThe SAMPLE SPACE is the set of allis the set of all
possible outcomes of a randompossible outcomes of a random
experiment. It isexperiment. It is denoted by the Greekdenoted by the Greek
letter omega (letter omega (ΩΩ) or S) or S. This is also known. This is also known
as theas the universal setuniversal set..
7. Examples:Examples:
Sample space of the “Tossing of Coin”Sample space of the “Tossing of Coin”
experiment:experiment:
ΩΩ == {Head, Tail}{Head, Tail}
Sample spaceSample space of the “Tossing of Die”of the “Tossing of Die”
experiment:experiment:
ΩΩ == {1, 2, 3, 4, 5, 6}{1, 2, 3, 4, 5, 6}
8. Exercise 1:Exercise 1:
1.1. Two coins are tossed, and their up faces areTwo coins are tossed, and their up faces are
recorded. What is the sample space for thisrecorded. What is the sample space for this
experiment?experiment?
Coin 1Coin 1 Coin 2Coin 2
HeadHead HeadHead
TailTail HeadHead
HeadHead TailTail
TailTail TailTail
ΩΩ = {HH, TH, HT, TT}= {HH, TH, HT, TT}
9. Exercise 2:Exercise 2:
2.2. Suppose a pair of dice is tossed . What is the sampleSuppose a pair of dice is tossed . What is the sample
space for this experiment?space for this experiment?
ΩΩ = {1-1,1-2,1-3,1-4,1-5,1-6,= {1-1,1-2,1-3,1-4,1-5,1-6,
2-1,2-2,2-3,2-4,2-5,2-6,2-1,2-2,2-3,2-4,2-5,2-6,
3-1,3-2,3-3,3-4,3-5,3-6,3-1,3-2,3-3,3-4,3-5,3-6,
4-1,4-2,4-3,4-4,4-5,4-6,4-1,4-2,4-3,4-4,4-5,4-6,
5-1,5-2,5-3,5-4,5-5,5-6,5-1,5-2,5-3,5-4,5-5,5-6,
6-1,6-2,6-3,6-4,6-5,6-6}6-1,6-2,6-3,6-4,6-5,6-6}
10. Exercise 3:Exercise 3:
3.3. A sociologist wants to determine the gender ofA sociologist wants to determine the gender of
the first two children of families with at least twothe first two children of families with at least two
(2) children in a baranggay in Dasmarinas,(2) children in a baranggay in Dasmarinas,
Cavite. He then observes and records the genderCavite. He then observes and records the gender
of the first 2 children of these families.of the first 2 children of these families.
ΩΩ == {MM, MF, FM, FF}{MM, MF, FM, FF}
where, M represents Male and F represents Female
11. Exercise 4:Exercise 4:
4.4. Consider the experiment of recording theConsider the experiment of recording the
number of customers placing their order atnumber of customers placing their order at
the “Drive Thru” of a particular McDonald’sthe “Drive Thru” of a particular McDonald’s
branch per day. What is the sample space forbranch per day. What is the sample space for
this random experiment?this random experiment?
ΩΩ == {0, 1, 2, 3, 4, 5, …}{0, 1, 2, 3, 4, 5, …}
12. Exercise 5:Exercise 5:
5.5. Suppose GMA Foundation wanted to know theSuppose GMA Foundation wanted to know the
effectiveness of their feeding program in aeffectiveness of their feeding program in a
particular baranggay in Dasmarinas. Theparticular baranggay in Dasmarinas. The
coordinator records the change in the children’scoordinator records the change in the children’s
weight to height ratio (BMI). What is the sampleweight to height ratio (BMI). What is the sample
space for this random experiment?space for this random experiment?
ΩΩ == {y / y{y / y ≥ 0≥ 0 }}
where, y = the change in a child’s BMI, assuming it
is not possible for a child to have a decrease in BMI while
enrolled in the feeding program.
13. Types of Sample Spaces:Types of Sample Spaces:
1.1. FINITE SAMPLE SPACEFINITE SAMPLE SPACE
Is a sample space withIs a sample space with finite numberfinite number of possibleof possible
outcomes (sample points).outcomes (sample points).
Exercises 1 to 3Exercises 1 to 3 are examples of finite sample spaces.are examples of finite sample spaces.
1.1. INFINITE SAMPLE SPACEINFINITE SAMPLE SPACE
Is a sample withIs a sample with infinite numberinfinite number of possible outcomes.of possible outcomes.
Exercise 4Exercise 4 is an example of ais an example of a countablecountable infiniteinfinite
sample space.sample space.
Exercise 5Exercise 5 is an example of ais an example of a uncountableuncountable infiniteinfinite
sample spacesample space..
14. Natures of Sample SpacesNatures of Sample Spaces
1.1. DISCRETE SAMPLE SPACEDISCRETE SAMPLE SPACE
Is a sample space with aIs a sample space with a countable (finite orcountable (finite or
infinite) number of possible outcomesinfinite) number of possible outcomes..
Examples areExamples are Exercises 1 to 4Exercises 1 to 4
1.1. CONTINUOUS SAMPLE SPACECONTINUOUS SAMPLE SPACE
Is a sample space with aIs a sample space with a continuum of possiblecontinuum of possible
outcomesoutcomes..
Example isExample is Exercise 5Exercise 5..
15. Recall the “Tossing of Die” experiment.Recall the “Tossing of Die” experiment.
Suppose we are interested in the
outcome that an even number will
come up.
1
5
3
2
4 6
A
ΩΩ
Let EVENT A, be
the collection of
sample points that
fulfill the outcome
we are interested in,
i.e., an even number
will come up.
16. Definition: EVENTDefinition: EVENT
AnAn EVENTEVENT is ais a subset of the sample spacesubset of the sample space..
It is denoted by any letter of the EnglishIt is denoted by any letter of the English
alphabet.alphabet.
An event is anAn event is an outcome of a randomoutcome of a random
experimentexperiment..
An event is aAn event is a specific collection of samplespecific collection of sample
pointspoints..
17. Examples:Examples:
1.1. ΩΩ == {Head, Tail}{Head, Tail}
Let A =Let A = {Head}{Head},, the event of a Head turning up.
Let B =Let B = {Tail}{Tail},, the event of a Tail turning up.
2.2. ΩΩ == {{Head-Head, Head-Tail, Tail-Head, Tail-Tail}Head-Head, Head-Tail, Tail-Head, Tail-Tail}
Let X =Let X = {Head-Head, Head-Tail, Tail-Head}{Head-Head, Head-Tail, Tail-Head},,
the event of at least one Head will turn up.
Let Y =Let Y = {Tail-Tail, Tail-Head, Head-Tail}{Tail-Tail, Tail-Head, Head-Tail},,
the event of a at least one Tail will turn up.
18. Exercise 6:Exercise 6:
Given the sample spaceGiven the sample space
ΩΩ,, for the single tossfor the single toss
of a pair of fair dice,of a pair of fair dice,
list the elements oflist the elements of
the following events:the following events:
AA = event of= event of
obtaining a sum thatobtaining a sum that
is anis an even numbereven number..
BB = event of obtaining= event of obtaining
a sum that is ana sum that is an oddodd
number.number.
ΩΩ = {1-1,1-2,1-3,1-4,1-5,1-6,= {1-1,1-2,1-3,1-4,1-5,1-6,
2-1,2-2,2-3,2-4,2-5,2-6,2-1,2-2,2-3,2-4,2-5,2-6,
3-1,3-2,3-3,3-4,3-5,3-6,3-1,3-2,3-3,3-4,3-5,3-6,
4-1,4-2,4-3,4-4,4-5,4-6,4-1,4-2,4-3,4-4,4-5,4-6,
5-1,5-2,5-3,5-4,5-5,5-6,5-1,5-2,5-3,5-4,5-5,5-6,
6-1,6-2,6-3,6-4,6-5,6-6}6-1,6-2,6-3,6-4,6-5,6-6}
20. Types of EventsTypes of Events
1.1. ELEMENTARY EVENTELEMENTARY EVENT
An event consisting ofAn event consisting of ONE possible outcomeONE possible outcome..
Example is the elementary events ofExample is the elementary events of
ΩΩ == {Head, Tail}{Head, Tail}
AA == {Head} and B{Head} and B == {Tail}{Tail}
ΩΩ == {Pass, Fail}{Pass, Fail}
CC == {Pass} and D{Pass} and D == {Fail}{Fail}
21. Types of EventsTypes of Events
2.2. IMPOSSIBLE EVENTIMPOSSIBLE EVENT
An event consisting ofAn event consisting of NO outcomeNO outcome..
Given the sample space of all possible productsGiven the sample space of all possible products
that can be purchased from a shoe store.that can be purchased from a shoe store.
ΩΩ == {Sandals, Slippers, Pumps, Moccasins, Rubber{Sandals, Slippers, Pumps, Moccasins, Rubber
Shoes, Bags, Belts, Accessories}Shoes, Bags, Belts, Accessories}
Let A be the event that one can buy a chain sawLet A be the event that one can buy a chain saw
in a shoe store. Thus Ain a shoe store. Thus A == { } or{ } or ϕϕ (null)(null)
22. Types of EventsTypes of Events
3.3. SURE EVENTSURE EVENT
An event consisting ofAn event consisting of ALL the possible outcomesALL the possible outcomes..
Given the “Tossing of a Die” experiment.Given the “Tossing of a Die” experiment.
Let K be the event that a number less than or equalLet K be the event that a number less than or equal
to 6 will occur if a die is thrown.to 6 will occur if a die is thrown.
23. Types of EventsTypes of Events
4.4. COMPLEMENT EVENTCOMPLEMENT EVENT
Is the set of all elements of the sample spaceIs the set of all elements of the sample space
which are not in the event, A.which are not in the event, A.
Denoted by ADenoted by Acc
or Aor A''
Given the “Tossing of a Die” experiment.Given the “Tossing of a Die” experiment.
ΩΩ == {1, 2, 3, 4, 5, 6}{1, 2, 3, 4, 5, 6}
If A =If A = {2, 4, 6}, the event that an even number{2, 4, 6}, the event that an even number
will come up,will come up,
ThenThen AAcc
== {1, 3, 5}{1, 3, 5}
24. Operations On EventsOperations On Events
1.1. INTERSECTION of 2 events A and B, denoted byINTERSECTION of 2 events A and B, denoted by
AA∩B, is the event containing all elements that are∩B, is the event containing all elements that are
common to events A and B.common to events A and B.
Example:Example:
ΩΩ == {a, b, c, d, e, f}{a, b, c, d, e, f}
A = {a, b, c, d}A = {a, b, c, d}
B = {c, d, e, f}B = {c, d, e, f}
A ∩ B = {c, d}A ∩ B = {c, d}
ΩΩ ∩ A = {a, b, c, d}∩ A = {a, b, c, d}
25. Definition:Definition:
MUTUALLY EXCLUSIVE EVENTSMUTUALLY EXCLUSIVE EVENTS
Two events are mutually exclusive if theyTwo events are mutually exclusive if they
cannot both occur simultaneously.cannot both occur simultaneously.
That is, AThat is, A∩B = { } or∩B = { } or ϕϕ
Example Let C = {1, 2, 3} and D = {a, b, c}Example Let C = {1, 2, 3} and D = {a, b, c}
ThenThen CC∩D = { }∩D = { }
26. Operations On EventsOperations On Events
2.2. UNION of 2 events A and B, denoted byUNION of 2 events A and B, denoted by
AA⋃⋃B, is the set containing all elements thatB, is the set containing all elements that
belong to A or to B or both.belong to A or to B or both.
Example:Example:
E = {1, 2, 3, 4, 5}E = {1, 2, 3, 4, 5}
F = {2, 5, 6, 7, 8}F = {2, 5, 6, 7, 8}
EE ⋃⋃ F = {1, 2, 3, 4, 5, 6, 7, 8}F = {1, 2, 3, 4, 5, 6, 7, 8}
27. Operations On EventsOperations On Events
3.3. Other OperationsOther Operations..
AA ⋃⋃ ΩΩ == ΩΩ
AA ⋂ A' =⋂ A' = ϕϕ
ΩΩ' =' = ϕϕ
(A')' = A(A')' = A
AA ⋃⋃ ϕϕ = A= A
AA ⋃ A'⋃ A' == ΩΩ
ϕϕ'' == ΩΩ