The document discusses the formulation and procedure of the Simplex Method for solving linear programming problems. It explains how problems are converted to standard form by introducing slack variables and making the objective function a constraint. The Simplex Method then searches through basic feasible solutions by choosing an entering variable and leaving variable at each step until an optimal solution is found where all objective function coefficients are non-negative.
The document discusses transportation problems (TPs), which involve determining the optimal way to route products from multiple supply locations to multiple demand destinations to minimize total transportation costs. It provides the mathematical formulation of a TP as a linear programming problem (LPP) with decision variables representing the quantity transported between each origin-destination pair. Methods for solving TPs include the simplex method by formulating it as an LPP or specialized transportation methods like the northwest corner rule to find an initial feasible solution and stepping stone/modified distribution methods to check for optimality. An example TP is presented to illustrate these concepts.
It gives detail description about probability, types of probability, difference between mutually exclusive events and independent events, difference between conditional and unconditional probability and Bayes' theorem
Below is given the summary from the 112th Congress of Senators whose terms en...Nadeem Uddin
This document summarizes data on US Senators from the 112th Congress whose terms expire in 2013, 2015, or 2017. It provides the numbers of Democratic and Republican Senators in each expiration year, and calculates the probabilities of various events based on this data. Specifically, it calculates: (a) the probability a randomly selected Senator is Democratic and their term expires in 2015, (b) the probability a randomly selected Senator is Republican or their term expires in 2013, and (c) the probability a Senator is Republican given their term expires in 2017. It also determines whether being Republican and having one's term expire in 2015 are independent events.
1. A survey was conducted on 200 respondents aged 36-50 and 200 over 50 on where they get their news. Of those aged 36-50, 82 got news from newspapers. Of those over 50, 104 got news from newspapers. The probability of a randomly selected respondent getting news from newspapers is 186/400 or 46.5%.
2. A survey asked 500 men and 500 women about hiding clothing purchases. 62 men and 116 women said they would hide purchases. The probability that a randomly selected person would hide purchases is 178/1000 or 17.8%.
3. A study looked at the S&P 500's performance over 58 years. In 37 years it was up after 5 days, and in
This chapter discusses basic probability concepts including defining probability as a numerical measure between 0 and 1, explaining sample spaces and events, visualizing events using contingency tables and tree diagrams, and computing joint, marginal, and conditional probabilities. It introduces key terms like probability, event, sample space, mutually exclusive and collectively exhaustive events. It also covers rules for calculating probabilities of joint, union, and conditional events.
This document provides an introduction to probability. It defines probability as the numerical description of how likely an event is to occur or a proposition is true. Some common applications of probability are weather forecasting, coin flipping, lottery tickets, and playing cards. The document then discusses basic probability concepts, defining an experiment as any process with two or more outcomes, and an outcome as the result of a single trial. An event is a collection of outcomes, and a sample space is the set of all possible outcomes. It provides examples of experiments, outcomes, events, and sample spaces for rolling a die and tossing a coin twice.
These slides represent a brief idea about conditional probability along with illustrative examples and discussions. It also consists the use of sets to develop a better understanding for the students having the following theorem in their course.
Probability is the mathematics of chance that describes the likelihood of events. It can be written as a fraction, decimal, percent, or ratio between 0 and 1. There are three types of probability: theoretical, experimental, and subjective. Conditional probability considers the probability of one event occurring given that another event has occurred and restricts the sample space. The multiplication rule states that if events are independent, the probability they both occur is the product of their individual probabilities.
Applied Business Statistics ,ken black , ch 5AbdelmonsifFadl
This document discusses discrete probability distributions from Chapter 5 of the textbook "Business Statistics, 6th ed." by Ken Black. It defines discrete and continuous random variables and distributions. It describes how to calculate the mean and variance of a discrete distribution. It also introduces the binomial and Poisson distributions and provides examples of how to calculate probabilities using them. For the binomial distribution example, it calculates the probability of getting two or fewer unemployed workers in a random sample of 20 Jackson, Mississippi residents. For the Poisson distribution example, it calculates the probability of more than 7 customers arriving at a bank in a 4-minute interval, given an average arrival rate of 3.2 customers every 4 minutes.
This document defines key probability terms and concepts. It begins by defining probability as the mathematics of chance that tells us the relative frequency of events. It then defines theoretical, experimental, and subjective probability. Key concepts explained include sample space, events, complementary events, independence, mutually exclusive events, and conditional probability. Examples are provided to illustrate calculating probabilities from tables or Venn diagrams. Conditional probability is demonstrated using a two-packet seed problem represented with a Venn diagram.
This document provides an overview of linear programming and the simplex method. It begins with introducing linear programming and its applications. Examples of linear programming problems are presented, including product mix, blending, production scheduling, transportation, and network flow problems. The steps for developing a linear programming model and graphical solution method are described. The document then focuses on explaining the simplex method, using a product mix problem as an example. It walks through applying the simplex method to find the optimal solution in multiple steps.
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.
1) The document discusses concepts related to probability distributions including uniform, normal, and binomial distributions.
2) It provides examples of calculating probabilities and values using the uniform, normal, and binomial distributions as well as the normal approximation to the binomial.
3) Key concepts covered include means, standard deviations, z-values, areas under the normal curve, and the continuity correction factor for approximating binomial with normal.
Chapter 12 Probability and Statistics.pptJoyceNolos
The document discusses probability and statistics concepts including:
1) The counting principle, which states that if there are "a" ways for one activity to occur and "b" ways for a second activity to occur, then there are (a x b) ways for both to occur.
2) Independent and dependent events, where independent events do not affect each other and dependent events do.
3) Probability is calculated as the number of desired outcomes divided by the total number of possible outcomes. Probability must be between 0 and 1.
4) Examples are provided to demonstrate calculating probabilities of independent and dependent events using formulas and tree diagrams.
Transportation and assignment models are network flow problems that can be solved using linear programming. Transportation models are used to determine optimal shipping routes from sources to destinations to minimize costs. Assignment models are used to efficiently match people or tasks based on costs or times. Both models involve distributing goods or assigning tasks from multiple sources to multiple destinations, with the goal of minimizing total costs or time.
This document discusses transportation models and methods for finding an initial basic feasible solution and testing for optimality in transportation problems. It describes three methods - northwest corner, least cost, and Vogel's approximation - for obtaining an initial solution. It then explains how to test if the initial solution is optimal using the MODI or u-v method by calculating opportunity costs for unoccupied cells and finding a closed path if any cells have negative opportunity costs to obtain an improved solution. The process repeats until all opportunity costs are non-negative, indicating an optimal solution.
Math for 800 05 ratios, rates and proportionsEdwin Lapuerta
This document provides an overview of ratios, rates, proportions, and units of measurement. It defines key terms like ratios, equivalent ratios, part-to-part ratios, and combined ratios. It also covers rates, average rates, direct and inverse proportions. Finally, it lists common units of measurement for time, mass, capacity, length, area and volume. The document is intended as a reference for understanding fundamental math concepts involving comparisons of quantities.
This document provides an overview of basic probability concepts covered in Chapter 4 of Basic Business Statistics, 11th Edition. It introduces key probability terms like simple events, joint events, sample space, and contingency tables for visualizing events. It covers how to calculate probabilities of events both with and without conditional dependencies. Formulas are provided for computing joint, marginal, and conditional probabilities using contingency tables. The chapter also explains Bayes' Theorem for revising probabilities based on new information. An example demonstrates how to apply Bayes' Theorem to calculate the probability of a successful oil well given a positive test result.
This chapter introduces key probability concepts including experiments, outcomes, events, classical, empirical and subjective probabilities, and rules for calculating probabilities. It defines probability as a measure between 0 and 1 of the likelihood of an event occurring. The three approaches to assigning probabilities are classical, empirical, and subjective. Classical probability uses equally likely outcomes and counting favorable outcomes. Empirical probability is based on observed frequencies over many trials. Subjective probability is used when there is little past data. Rules of addition and multiplication for probabilities are presented. Conditional probability and joint probability are also defined.
The document discusses elementary theorems and concepts related to probability and conditional probability. It defines the addition rule for mutually exclusive events, the formula for calculating probability of an event as the sum of probabilities of individual outcomes, and the general addition rule for probability. It also defines conditional probability as the probability of an event A given that another event B has occurred, and introduces Bayes' theorem which provides a formula for calculating the probability of an event given certain conditions.
Applied Business Statistics ,ken black , ch 4AbdelmonsifFadl
This document summarizes key concepts from Chapter 4 of the textbook "Business Statistics, 6th ed." by Ken Black. It covers:
- Different methods of assigning probabilities, including classical, relative frequency, and subjective probabilities.
- Calculating probabilities using formulas like the classical probability formula P(E) = n(E)/N.
- Concepts like sample spaces, events, mutually exclusive and independent events, and complementary events.
- Laws of probability, including the general laws of addition and multiplication, and how to apply them to probability problems and matrices.
Applied statistics and probability for engineers solution montgomery && rungerAnkit Katiyar
This document is the copyright page and preface for the book "Applied Statistics and Probability for Engineers" by Douglas C. Montgomery and George C. Runger. The copyright is held by John Wiley & Sons, Inc. in 2003. This book was edited, designed, and produced by various teams at John Wiley & Sons and printed by Donnelley/Willard. The preface states that the purpose of the included Student Solutions Manual is to provide additional help for students in understanding the problem-solving processes presented in the main text.
These slides represent a brief idea about conditional probability along with illustrative examples and discussions. It also consists the use of sets to develop a better understanding for the students having the following theorem in their course.
Probability is the mathematics of chance that describes the likelihood of events. It can be written as a fraction, decimal, percent, or ratio between 0 and 1. There are three types of probability: theoretical, experimental, and subjective. Conditional probability considers the probability of one event occurring given that another event has occurred and restricts the sample space. The multiplication rule states that if events are independent, the probability they both occur is the product of their individual probabilities.
Applied Business Statistics ,ken black , ch 5AbdelmonsifFadl
This document discusses discrete probability distributions from Chapter 5 of the textbook "Business Statistics, 6th ed." by Ken Black. It defines discrete and continuous random variables and distributions. It describes how to calculate the mean and variance of a discrete distribution. It also introduces the binomial and Poisson distributions and provides examples of how to calculate probabilities using them. For the binomial distribution example, it calculates the probability of getting two or fewer unemployed workers in a random sample of 20 Jackson, Mississippi residents. For the Poisson distribution example, it calculates the probability of more than 7 customers arriving at a bank in a 4-minute interval, given an average arrival rate of 3.2 customers every 4 minutes.
This document defines key probability terms and concepts. It begins by defining probability as the mathematics of chance that tells us the relative frequency of events. It then defines theoretical, experimental, and subjective probability. Key concepts explained include sample space, events, complementary events, independence, mutually exclusive events, and conditional probability. Examples are provided to illustrate calculating probabilities from tables or Venn diagrams. Conditional probability is demonstrated using a two-packet seed problem represented with a Venn diagram.
This document provides an overview of linear programming and the simplex method. It begins with introducing linear programming and its applications. Examples of linear programming problems are presented, including product mix, blending, production scheduling, transportation, and network flow problems. The steps for developing a linear programming model and graphical solution method are described. The document then focuses on explaining the simplex method, using a product mix problem as an example. It walks through applying the simplex method to find the optimal solution in multiple steps.
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.
1) The document discusses concepts related to probability distributions including uniform, normal, and binomial distributions.
2) It provides examples of calculating probabilities and values using the uniform, normal, and binomial distributions as well as the normal approximation to the binomial.
3) Key concepts covered include means, standard deviations, z-values, areas under the normal curve, and the continuity correction factor for approximating binomial with normal.
Chapter 12 Probability and Statistics.pptJoyceNolos
The document discusses probability and statistics concepts including:
1) The counting principle, which states that if there are "a" ways for one activity to occur and "b" ways for a second activity to occur, then there are (a x b) ways for both to occur.
2) Independent and dependent events, where independent events do not affect each other and dependent events do.
3) Probability is calculated as the number of desired outcomes divided by the total number of possible outcomes. Probability must be between 0 and 1.
4) Examples are provided to demonstrate calculating probabilities of independent and dependent events using formulas and tree diagrams.
Transportation and assignment models are network flow problems that can be solved using linear programming. Transportation models are used to determine optimal shipping routes from sources to destinations to minimize costs. Assignment models are used to efficiently match people or tasks based on costs or times. Both models involve distributing goods or assigning tasks from multiple sources to multiple destinations, with the goal of minimizing total costs or time.
This document discusses transportation models and methods for finding an initial basic feasible solution and testing for optimality in transportation problems. It describes three methods - northwest corner, least cost, and Vogel's approximation - for obtaining an initial solution. It then explains how to test if the initial solution is optimal using the MODI or u-v method by calculating opportunity costs for unoccupied cells and finding a closed path if any cells have negative opportunity costs to obtain an improved solution. The process repeats until all opportunity costs are non-negative, indicating an optimal solution.
Math for 800 05 ratios, rates and proportionsEdwin Lapuerta
This document provides an overview of ratios, rates, proportions, and units of measurement. It defines key terms like ratios, equivalent ratios, part-to-part ratios, and combined ratios. It also covers rates, average rates, direct and inverse proportions. Finally, it lists common units of measurement for time, mass, capacity, length, area and volume. The document is intended as a reference for understanding fundamental math concepts involving comparisons of quantities.
This document provides an overview of basic probability concepts covered in Chapter 4 of Basic Business Statistics, 11th Edition. It introduces key probability terms like simple events, joint events, sample space, and contingency tables for visualizing events. It covers how to calculate probabilities of events both with and without conditional dependencies. Formulas are provided for computing joint, marginal, and conditional probabilities using contingency tables. The chapter also explains Bayes' Theorem for revising probabilities based on new information. An example demonstrates how to apply Bayes' Theorem to calculate the probability of a successful oil well given a positive test result.
This chapter introduces key probability concepts including experiments, outcomes, events, classical, empirical and subjective probabilities, and rules for calculating probabilities. It defines probability as a measure between 0 and 1 of the likelihood of an event occurring. The three approaches to assigning probabilities are classical, empirical, and subjective. Classical probability uses equally likely outcomes and counting favorable outcomes. Empirical probability is based on observed frequencies over many trials. Subjective probability is used when there is little past data. Rules of addition and multiplication for probabilities are presented. Conditional probability and joint probability are also defined.
The document discusses elementary theorems and concepts related to probability and conditional probability. It defines the addition rule for mutually exclusive events, the formula for calculating probability of an event as the sum of probabilities of individual outcomes, and the general addition rule for probability. It also defines conditional probability as the probability of an event A given that another event B has occurred, and introduces Bayes' theorem which provides a formula for calculating the probability of an event given certain conditions.
Applied Business Statistics ,ken black , ch 4AbdelmonsifFadl
This document summarizes key concepts from Chapter 4 of the textbook "Business Statistics, 6th ed." by Ken Black. It covers:
- Different methods of assigning probabilities, including classical, relative frequency, and subjective probabilities.
- Calculating probabilities using formulas like the classical probability formula P(E) = n(E)/N.
- Concepts like sample spaces, events, mutually exclusive and independent events, and complementary events.
- Laws of probability, including the general laws of addition and multiplication, and how to apply them to probability problems and matrices.
Applied statistics and probability for engineers solution montgomery && rungerAnkit Katiyar
This document is the copyright page and preface for the book "Applied Statistics and Probability for Engineers" by Douglas C. Montgomery and George C. Runger. The copyright is held by John Wiley & Sons, Inc. in 2003. This book was edited, designed, and produced by various teams at John Wiley & Sons and printed by Donnelley/Willard. The preface states that the purpose of the included Student Solutions Manual is to provide additional help for students in understanding the problem-solving processes presented in the main text.
This document provides publishing information for the third edition of the textbook "Applied Statistics and Probability for Engineers" including the authors, editors, production staff, publisher, and copyright details. It indicates that the book was set in Times Roman font and printed by Donnelley/Willard on acid-free paper. The copyright is held by John Wiley & Sons, Inc. in 2003.
[Junoon - E - Jee] - Probability - 13th Nov.pdfPrakashPatra7
This document provides information about the probability concepts of random experiments, sample space, events, classical definition of probability, odds in favor and against an event, problems on double dice, and conditional probability. It defines key terms like random experiment, sample space, event, complement of an event, classical probability, odds in favor and against, double dice problems, and conditional probability. Examples are given for each concept to illustrate the definitions. Formulas for classical probability, odds, addition theorem on probability, and multiplication theorem on probability are also presented.
The document discusses the four operations of integers: addition, subtraction, multiplication, and division. It provides examples of each operation using both positive and negative integers. There are four cases: 1) starting with positive numbers and ending with more positive numbers, 2) starting with positive numbers and ending with more negative numbers, 3) starting with negative numbers and ending with more negative numbers, and 4) starting with negative numbers and ending with more positive numbers. The document also discusses the associative law of multiplication and provides the mathematical equation for it.
The document discusses probability, mutually exclusive events, Venn diagrams, and conditional probability. It provides examples of how to calculate probabilities using Venn diagrams, including the probability of events occurring together or separately. It also gives an example of how to calculate conditional probabilities, such as the probability of an event occurring given that another event has already occurred. Key concepts covered are probability, outcomes, mutually exclusive events, Venn diagrams, and how to use them to calculate probabilities of events.
This chapter discusses probability concepts and definitions. It aims to explain basic probability, use diagrams to illustrate probabilities, apply probability rules, and determine conditional probabilities and independence. Key terms are defined, such as sample space, events, intersections and unions of events. Common probability rules like complement, addition, and multiplication rules are covered. Examples are provided to demonstrate conditional probability, independence, and use of trees to calculate probabilities.
Unit 4--probability and probability distribution (1).pptxakshay353895
This document provides an overview of probability concepts and probability distributions. It begins by defining basic probability terms like random experiments, sample spaces, events, and outcomes. It then discusses how to calculate probabilities of simple, joint, and compound events using formulas and diagrams like Venn diagrams and contingency tables. The document also covers conditional probability, independence, Bayes' theorem, and counting methods like permutations and combinations. Finally, it introduces discrete random variables and how to calculate the expected value, variance, and standard deviation of discrete random variables.
This document contains solutions to problems from the 4th edition of Introduction to Electrodynamics by David J. Griffiths. It includes solutions to vector analysis problems from Chapter 1 on topics like the dot product, cross product, and triple cross product. The solutions provide step-by-step working to arrive at the answers to problems involving vector operations. The document also contains prefaces, contents pages, and copyright information.
The document summarizes key points from a lecture on combinations of events. It discusses intersections of events, unions of events, and examples involving quality checks of television sets and games of chance. Intersections refer to outcomes that are contained within both events A and B. Unions refer to outcomes contained within at least one of events A or B. Examples calculate probabilities of intersections and unions for events like appliances passing quality checks or dice rolls resulting in certain scores. The document also introduces combinations of three or more events using unions and partitions of a sample space.
chap03--Discrete random variables probability ai and ml R2021.pdfmitopof121
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness, happiness and focus.
The document discusses probability concepts including:
- The addition law of probability, which states that for any two events A and B, the probability of their union P(A ∪ B) equals the sum of their individual probabilities P(A) + P(B) minus the probability of their intersection P(A ∩ B).
- This law is applied to several examples involving rolling dice and selecting numbers to calculate probabilities.
- Mutually exclusive events are defined as events whose intersection has probability 0, so for these events the addition law simplifies to P(A ∪ B) = P(A) + P(B).
The document describes an algorithm for finding the largest integer in a list of numbers. It first defines an algorithm as a precise sequence of instructions to solve a problem. It then provides an English description of the maximum finding algorithm and expresses it in pseudocode. The algorithm assigns the first number as the maximum value, then compares each subsequent number to the current maximum, updating it if a larger number is found. It notes the algorithm uses 2n-1 comparisons to find the maximum number.
This document provides study material for a course on probability and statistics. It covers topics such as sample spaces, events, axioms of probability, conditional probability, Bayes' theorem, random variables, probability distributions including binomial, Poisson, normal and other continuous distributions, joint and marginal distributions, mathematical expectation, decision making, sampling distributions and statistical inference. Various examples are provided to illustrate concepts such as probability calculations for events from finite sample spaces, conditional probability, independence of events and finding probabilities of unions and intersections of events.
This document discusses different types of numbers including real numbers, imaginary numbers, rational numbers, irrational numbers, integers, fractions, and prime numbers. It provides examples and definitions for each number type. Properties of number comparisons such as less than, greater than, addition, subtraction, multiplication and division are also covered. Rules for operations on numbers including commutative, associative, cancellation, distribution and identity properties are defined. Finally, the document outlines the sign rules for addition, subtraction, multiplication and division of positive and negative numbers.
1. The cross product of two vectors gives a vector perpendicular to both vectors, with magnitude equal to the area of the parallelogram formed by the two vectors.
2. If two adjacent sides of a parallelogram are given by vectors a and b, the area of the parallelogram is |a x b|.
3. If the position vectors of three vertices of a triangle are given, the area of the triangle can be found as 1/2 times the magnitude of the cross product of any two sides of the triangle.
Here the concept of "TRUE" is defined according to Alfred Tarski, and the concept "OCCURING EVENT" is derived from this definition.
From here, we obtain operations on the events and properties of these operations and derive the main properties of the CLASSICAL PROB-ABILITY. PHYSICAL EVENTS are defined as the results of applying these operations to DOT EVENTS.
Next, the 3 + 1 vector of the PROBABILITY CURRENT and the EVENT STATE VECTOR are determined.
The presence in our universe of Planck's constant gives reason to\linebreak presume that our world is in a CONFINED SPACE. In such spaces, functions are presented by Fourier series. These presentations allow formulating the ENTANGLEMENT phenomenon.
Global Journal of Science Frontier Research: FMathematics and Decision Sciences Volume 18 Issue 2 Version 1.0 Year 2018
The document contains multiple math and word problems. It provides the steps to solve union of sets A and B where A = {-4,-2,0,2,4} and B = {-4,-2,0,3,4}. It also calculates the breakeven point given total sales and costs for a business. A third problem determines the number of participants from private teacher education institutions given percentages from different groups.
The document provides examples of calculating confidence intervals from sample data. It includes steps for finding 95%, 99%, and 90% confidence intervals using the t-distribution and z-distribution. Sample sizes, means, standard deviations and confidence levels are given for multiple data sets, and confidence intervals are calculated and interpreted for each example.
This document provides statistical information and calculations for 5 questions regarding different data sets. For each question, it lists the data, calculates order statistics like median and quartiles, and provides measures of center, spread, and variation for both the population and a sample, including range, variance, standard deviation, and coefficient of variation. It also calculates the mean absolute deviation for each data set.
Management and Organization Behavior REPORT, MBAIshaq Ahmed
The document discusses problems facing an organization called RIMERS Tea Estate. It identifies several issues including lack of coordination among employees, a weak distribution channel, low sales margins and production, high competition, and lack of motivation and monitoring. It analyzes the root causes of these problems, such as rising living costs reducing employee satisfaction with salaries, fuel price increases raising transportation costs, competitor companies offering better pay luring away efficient managers, and management gaps emerging from lost managers. Solutions proposed include improving compensation packages to motivate employees.
Management and Organization Behavior PPT, MBAIshaq Ahmed
This document discusses a decision made by Rimers Tea Estate to implement significant changes in response to problems they were facing. The key issues they were facing included lack of coordination, weak distribution channels, low sales and production, and high labor turnover. Their decision was to maintain current employees but change their compensation package to increase pay across different levels by 6-12% and provide additional benefits and incentives. They created an action plan to implement this decision, estimate additional costs, set new sales targets, and monitor performance. The goal of the changes was to improve employee satisfaction, organizational performance, and profits.
Cocola Food Company Ltd. is a growing private food company in Bangladesh that was established in 1975. The report provides details about Cocola, including its mission, history, products, factory, organizational structure, functional departments, and SWOT analysis. It also analyzes the production efficiency of various Cocola products over time, noting seasonal impacts and identifying reasons for production gaps. Overall, the report finds that Cocola is meeting consumer demand through a wide range of quality food products and playing an important role in the Bangladeshi economy.
How to Manage Upselling in Odoo 18 SalesCeline George
In this slide, we’ll discuss on how to manage upselling in Odoo 18 Sales module. Upselling in Odoo is a powerful sales technique that allows you to increase the average order value by suggesting additional or more premium products or services to your customers.
Ajanta Paintings: Study as a Source of HistoryVirag Sontakke
This Presentation is prepared for Graduate Students. A presentation that provides basic information about the topic. Students should seek further information from the recommended books and articles. This presentation is only for students and purely for academic purposes. I took/copied the pictures/maps included in the presentation are from the internet. The presenter is thankful to them and herewith courtesy is given to all. This presentation is only for academic purposes.
Redesigning Education as a Cognitive Ecosystem: Practical Insights into Emerg...Leonel Morgado
Slides used at the Invited Talk at the Harvard - Education University of Hong Kong - Stanford Joint Symposium, "Emerging Technologies and Future Talents", 2025-05-10, Hong Kong, China.
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003
what is pulse ?
Purpose
physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
for BSC Nursing 1st semester
for Gnm Nursing 1st year
Students .
vitalsign
How to Add Customer Note in Odoo 18 POS - Odoo SlidesCeline George
In this slide, we’ll discuss on how to add customer note in Odoo 18 POS module. Customer Notes in Odoo 18 POS allow you to add specific instructions or information related to individual order lines or the entire order.
How to Configure Public Holidays & Mandatory Days in Odoo 18Celine George
In this slide, we’ll explore the steps to set up and manage Public Holidays and Mandatory Days in Odoo 18 effectively. Managing Public Holidays and Mandatory Days is essential for maintaining an organized and compliant work schedule in any organization.
How to Configure Scheduled Actions in odoo 18Celine George
Scheduled actions in Odoo 18 automate tasks by running specific operations at set intervals. These background processes help streamline workflows, such as updating data, sending reminders, or performing routine tasks, ensuring smooth and efficient system operations.
This slide is an exercise for the inquisitive students preparing for the competitive examinations of the undergraduate and postgraduate students. An attempt is being made to present the slide keeping in mind the New Education Policy (NEP). An attempt has been made to give the references of the facts at the end of the slide. If new facts are discovered in the near future, this slide will be revised.
This presentation is related to the brief History of Kashmir (Part-I) with special reference to Karkota Dynasty. In the seventh century a person named Durlabhvardhan founded the Karkot dynasty in Kashmir. He was a functionary of Baladitya, the last king of the Gonanda dynasty. This dynasty ruled Kashmir before the Karkot dynasty. He was a powerful king. Huansang tells us that in his time Taxila, Singhpur, Ursha, Punch and Rajputana were parts of the Kashmir state.
Happy May and Taurus Season.
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Ancient Stone Sculptures of India: As a Source of Indian HistoryVirag Sontakke
This Presentation is prepared for Graduate Students. A presentation that provides basic information about the topic. Students should seek further information from the recommended books and articles. This presentation is only for students and purely for academic purposes. I took/copied the pictures/maps included in the presentation are from the internet. The presenter is thankful to them and herewith courtesy is given to all. This presentation is only for academic purposes.
This chapter provides an in-depth overview of the viscosity of macromolecules, an essential concept in biophysics and medical sciences, especially in understanding fluid behavior like blood flow in the human body.
Key concepts covered include:
✅ Definition and Types of Viscosity: Dynamic vs. Kinematic viscosity, cohesion, and adhesion.
⚙️ Methods of Measuring Viscosity:
Rotary Viscometer
Vibrational Viscometer
Falling Object Method
Capillary Viscometer
🌡️ Factors Affecting Viscosity: Temperature, composition, flow rate.
🩺 Clinical Relevance: Impact of blood viscosity in cardiovascular health.
🌊 Fluid Dynamics: Laminar vs. turbulent flow, Reynolds number.
🔬 Extension Techniques:
Chromatography (adsorption, partition, TLC, etc.)
Electrophoresis (protein/DNA separation)
Sedimentation and Centrifugation methods.
Rock Art As a Source of Ancient Indian HistoryVirag Sontakke
This Presentation is prepared for Graduate Students. A presentation that provides basic information about the topic. Students should seek further information from the recommended books and articles. This presentation is only for students and purely for academic purposes. I took/copied the pictures/maps included in the presentation are from the internet. The presenter is thankful to them and herewith courtesy is given to all. This presentation is only for academic purposes.
Computer crime and Legal issues Computer crime and Legal issuesAbhijit Bodhe
• Computer crime and Legal issues: Intellectual property.
• privacy issues.
• Criminal Justice system for forensic.
• audit/investigative.
• situations and digital crime procedure/standards for extraction,
preservation, and deposition of legal evidence in a court of law.
All About the 990 Unlocking Its Mysteries and Its Power.pdfTechSoup
In this webinar, nonprofit CPA Gregg S. Bossen shares some of the mysteries of the 990, IRS requirements — which form to file (990N, 990EZ, 990PF, or 990), and what it says about your organization, and how to leverage it to make your organization shine.
1. 1) A corporation takes delivery of some new machinery that must be installed and checked before
it becomes operational. The accompanying table shows a manager’s probability assessment for
the number of days required before the machinery becomes operational.
Number of days 3 4 5 6 7
Probability .08 .24 .41 .20 .07
Let, A be the event “it will be more than 4 days before the merchandise becomes operational”
and B the event “it will be less than 6 days before the merchandise becomes available”
Ans:
Let,
A denotes that, “it will be more than 4 days before the merchandise becomes operational.”
B denotes that, “it will be less than 6 days before the merchandise becomes available.”
A= [5, 6, 7] and B= [3, 4, 5]
a) P (A)= P (5)+P (6)+P (7)
=. 41 +. 20 +. 07
= .68
The probability of event A = .68
So, there will be .68 chances that it will be more than 4 days before the machinery becomes
operational.
b) P(B) = P(3) + P(4) + P(5)
= .08+. 24 +. 41
=. 73
So, there will be .73 chances that it will be less than 6 days before the machinery becomes
available.
c) The symbol of A complement is A . So, A denotes that, it will be at most 4 days before the
machinery becomes operational.
d) P ( A ) = P (S) – P (A)
= 1-.68
=. 32
e) The symbol of intersection of events A and B are AÇ B denotes is the set of all the basic
outcomes that are both in events A and B. So,
AÇ B = [5, 6, 7] Ç [3, 4, 5] = [5]
So, it will be 5 days before the machinery becomes available or operational.
f) (AÇ B) = (A) Ç (B)
=[5,6,7] Ç [3,4,5]
= [5]
P (AÇ B) = P (5)
=. 41
g) The symbol of union of events A and B are AÈ B denotes is the set of all the basic outcomes
will occur at least one of these two events. So, A È B = [5,6,7] È [3,4,5] = [3, 4, 5, 6, and 7]
So, it will be at most 7 days before the machine operational or available.
1
2. h) P(AÈ B) = P (3)+P(4)+P(5)+P(6)+P(7)
= .08+.24+.41+.20+.07
= 1
i) No. The events A and B are not mutually exclusive. Because they have to something share
among them.
j) Yes, The events A and B are collectively exhaustive because, P (A È B)=P (S) = 1
2) A fund manager is considering investment in the stock of a health care provider. The
manager assessments of probabilities for rates of return on the stock over the next year are
summarized in the accompanying table. Let A be the event “Rate of return will be more than
10%” and B the event “Rate of return will be negative”
Rate of return Less than-
10%
-10% to 0% 0% to 10% 10% to
20%
More
than 20%
Probability .04 .14 .28 .33 .21
Ans:
Let, A = Rate of return will be more than 10%
B = Rate of return will be negative
So, A = [10% to 20%, more than 20%] and B = [Less than -10%, -10% to 0%]
a) P (A) = P (10% to 20%) + P (more than 20%)
= .33+.21 = .54
b) P (B) = P(Less than -10%) + P (-10% to 0%)
= .04+.14 = .18
c) The symbols of A complement is A . The element have of the event A that are must not have
in the A . So, A = [Less than -10%, -10% to 0%, 0% to 10% ]
d) P ( A ) = P(Less than -10%) + P (-10% to 0%) + P (0% to 10%)
= .04 + .14 + .28 = .46
e) The symbol of intersection of events A and B are AÇ B denotes is the set of all the basic
outcomes that are both in events A and B. So, AÇ B = [ ], because nothing to share among
them.
f) P(AÇ B) = P( ) = 0
g) The symbol of union of events A and B are A È B denotes is the set of all the basic outcomes
will occur at least one of these two events. So, AÈ B = [Less than -10% , -10% to 0% , 10%
to 20% , more than 20% ]
h) P (AÈ B) = P(Less than -10%) + P (-10% to 0%) + P (10% to 20%) + P (more than 20%)
= .04 + .14 + .33 + .21 = .72
i) Yes. The events A and B are mutually exclusive. Because they have nothing to share among
them, and their intersection is 0.
j) No, the events A and B are not collectively exhaustive because, their union is not equal to the
sample space. So, P (AÈ B) ¹ P (S) ¹ 1
3) A manager has available a pool of eight employees who could be assigned to a project
monitoring task. Four of the employees are women and four are men. Two of the men are
brothers. The manager is to make the assignment at random, so that each of the eight
employees is equally likely to be chosen.
Let A be the event “chosen employee is a man.”
And B the event “chosen employee is of the brothers.”
2
3. Ans:
Let,
A denotes that, “chosen the employee is a man.”
B denotes that, “chosen the employee is of the brother.”
a) P (A) = 4/8 = ½
b) P (B) = 2/8 = ¼
c) P (AÇ B) = 2/8 = ¼
d) P (AÈ B) = P (A) + P (B) – P (A Ç B)
= ½ + ¼ - ¼
= ½
4) In Section 3.4, we saw that if pair of events are mutually exclusive, the probability of their
union is the sum of their individual probabilities. However, this is not the case for the events
that are not mutually exclusive. Verify this assertion by considering the events A and B of
exercise 1.
Ans: The assertion by considering the events A and B of exercise 1 are not mutually exclusive
because, they have to something share. When the two events are mutually exclusive then, they have
nothing to share among them. So their intersection will be 0, AÇ B = 0. And their probability P
(AÇ B) = 0. But in exercise-1, AÇ B¹ 0, and their probability P (AÇ B) ¹ 0. In this why these two
events are not mutually exclusive.
5) A department store manager has monitored the number of complaints received per week
about poor service. The probabilities for numbers of complaints in a week, established by the
review, are shown in the table. Let A be the event “There will be at least one component in a
work.” and B the event, “There will be less than ten components in a work.”
NUMBER OF
0 1-3 4-6 7-9 10-12 More than
COMPLAINTS
12
PROBABILITY .14 .39 .23 .15 .06 .03
Ans:
Let,
A denotes that, “at least one component in a work.”
B denotes that, “less than ten components in a work.”
A= [1-3, 4-6, 7-9, 10-12, more than 12] and B= [0, 1-3, 4-6, 7-9].
a) P (A) = P (1-3)+P (4-6)+P (7-9)+P (10-12)+P (more than 12)
= .39+. 23 +. 15 +. 06 +. 03
= .86
So, there will be 53% chances to occur the events A in a week.
b) P (B) = P (0) + P (1-3) +P (4-6) +P (7-9)
= .14+. 39 +. 23 +. 15
=. 91
So, there will be 91% chances to occur the events B in a week.
c) P ( A ) = 1 – P (A)
= 1 - .86
3
4. = .14
d) The probability of the union A and B is;
P (AÈ B) = P (A) + P (B) – P (A Ç B)
= .86 + .91 - .77
= 1
e) The probability of the intersection A and B is:
P (AÇ B) = P (1-3) + P (4-6) +P (7-9)
= .39+. 23 +. 15
= .77
f) The events A &B are not mutually exclusive because they have something to
Share.
g) The events A & B are collectively exhaustive because P (AÈ B) equal to 1.
6) A corporation receives a particular part in shipments of 100.Research has indicated the
probabilities shown in the accompanying table for number of defective parts in a shipments.
NUMBER DEFECTIVE 0 1 2 3 More than 3
PROBABILITY .29 .36 .22 .10 .03
Ans:
Let, A denotes that “there will be less than 3 defective part in a shipment”
B denotes that “there will be more than 1 defective part in a shipment”
A =[0,1,2] & B= [2, 3, More than 3]
a) P (A)=P (0)+ P (1)+ P (2)
=. 29 +. 36 +. 22
=. 87
So, there will be 87% chances to occur the event A in a shipment.
b) P (A)=P (2)+ P (3)+ P (More than 3)
=. 22 +. 10 +. 03
= .35
So, there will be 35% chances to occur the event B in a shipment.
c) The events A & B are collectively exhaustive because P (A È B) equal to 1.
4