Decision theory, an important topic in operations research. now included in the paper business statistics and analytics (KMB104) of AKTU MBA I sem. various environments of decision making explained through solved numerical examples
Statistical analysis of Crude oil prices of historical data. Tried to analyze the data using statistical models and graphs and interpreted the meaning of results found. A thorough research on the industry is done. The report uses the following mathematical calculations: Mean & its types, Median, Mode, Variance, Standard Deviation, Range, Correlation & Regression, and Kurtosis Coefficient of Skewness.
Tried to calculate it both manually and using MS Excel in built macros.
When fitting loss data (insurance) to a distribution, often the parameters that provide a good overall fit will understate the density in the tail.
This method allows one to split the distribution into 2 portions, and use a Pareto distribution to fit the tail.
Presented at the CAS Spring Meeting in Seattle, May 2016.
The document contains sample questions from previous years' business statistics exams. It includes two questions:
1) A question from 2006 that involves calculating the mean, standard deviation, and coefficient of variation for age data grouped into classes with frequency counts.
2) A question from 2007 that involves calculating the mean and median income from frequency data grouped into classes. The document shows the work and calculations to arrive at the answers for both questions.
This document provides an introduction to fuzzy logic and fuzzy systems. It discusses classical set theory versus fuzzy set theory and membership functions. Types of fuzzy membership functions like triangular, trapezoidal, and Gaussian are shown. The key components of a fuzzy logic controller including fuzzification, fuzzy inference system, and defuzzification are described. Several defuzzification methods such as mean of maxima, centroid, and approximate centroid are explained. Examples of fuzzy applications in areas like washing machines and autonomous vehicles are presented. The document also discusses building fuzzy systems using MATLAB/Simulink and at the command line. Finally, it briefly introduces PID fuzzy controllers.
4. uzdevums. Aprēķini un analizē!
SIA «ZZ» dienā saražo 45 bērnu zābaku pārus, kuru kopējās ražošanas izmaksas EUR 910, un 60 sieviešu zābaku pārus, kuru kopējās ražošanas izmaksas EUR 1220.
Aprēķini ražošanas uzņēmuma kopējās izmaksas, bērnu zābaku, sieviešu zābaku vidējās izmaksas!
TC=2130 € ; ATC_b≈20,22 € ; ATC_s≈20,33 €
Kura produkta ražošanas izmaksas ir lielākas?
Sieviešu zābaku vidējās izmaksas ir lielākas nekā bērnu zābaku vidējās izmaksas:
ATC_s>ATC_b
Dots:
Q_b=45
TC_b=910 €
Q_s=60
TC_s=1220 €
Atrast:
TC - ?
ATC_b - ?
ATC_s - ?
ATC_b ∨ ATC_s - ?
Risinājums:
TC=TC_b+ TC_s
TC=910 €+1220 €=2130 €
ATC_b=(TC_b)/Q_b
ATC_b=(910 €)/45 ≈20,22 €
ATC_s=(TC_s)/Q_s
ATC_s=(1220 €)/60≈20,33 €
ATC_b<ATC_s
5. uzdevums. IK «Rotas» saražoto rotaslietu viena komplekta mainīgās izmaksas ir Ls 5,50, pastāvīgās izmaksas Ls 24 mēnesi. Mēnesī viņa izgatavo 40 komplektus. Komplekta tirgus cena ir Ls 15,00.
1) Kādas ir šo komplektu kopējās izmaksas? TC=244 Ls
2) Kāda ir pirms nodokļu peļņa, ja tiek pārdoti visi komplekti? Peļņa=356
- The workshop will provide an introductory crash course in inferential statistics, covering key concepts like identifying appropriate statistical procedures, checking assumptions, and correctly interpreting p-values.
- The first half will focus on foundational theories like understanding data descriptions and hypothesis tests. The second half will cover tools for regression, count, and multiple data/predictor situations.
- Key resources include the STATS 1024 textbook and selected chapters.
Quicksort is a sorting algorithm that uses a divide and conquer approach. It works by selecting a pivot element and partitioning the array around the pivot so that elements less than the pivot are to its left and greater elements are to its right. It then recursively applies this process to the subarrays until each contains a single element, at which point the array is fully sorted. The example demonstrates quicksort sorting an array from 0 to 7.
Statistical analysis of Crude oil prices of historical data. Tried to analyze the data using statistical models and graphs and interpreted the meaning of results found. A thorough research on the industry is done. The report uses the following mathematical calculations: Mean & its types, Median, Mode, Variance, Standard Deviation, Range, Correlation & Regression, and Kurtosis Coefficient of Skewness.
Tried to calculate it both manually and using MS Excel in built macros.
When fitting loss data (insurance) to a distribution, often the parameters that provide a good overall fit will understate the density in the tail.
This method allows one to split the distribution into 2 portions, and use a Pareto distribution to fit the tail.
Presented at the CAS Spring Meeting in Seattle, May 2016.
The document contains sample questions from previous years' business statistics exams. It includes two questions:
1) A question from 2006 that involves calculating the mean, standard deviation, and coefficient of variation for age data grouped into classes with frequency counts.
2) A question from 2007 that involves calculating the mean and median income from frequency data grouped into classes. The document shows the work and calculations to arrive at the answers for both questions.
This document provides an introduction to fuzzy logic and fuzzy systems. It discusses classical set theory versus fuzzy set theory and membership functions. Types of fuzzy membership functions like triangular, trapezoidal, and Gaussian are shown. The key components of a fuzzy logic controller including fuzzification, fuzzy inference system, and defuzzification are described. Several defuzzification methods such as mean of maxima, centroid, and approximate centroid are explained. Examples of fuzzy applications in areas like washing machines and autonomous vehicles are presented. The document also discusses building fuzzy systems using MATLAB/Simulink and at the command line. Finally, it briefly introduces PID fuzzy controllers.
4. uzdevums. Aprēķini un analizē!
SIA «ZZ» dienā saražo 45 bērnu zābaku pārus, kuru kopējās ražošanas izmaksas EUR 910, un 60 sieviešu zābaku pārus, kuru kopējās ražošanas izmaksas EUR 1220.
Aprēķini ražošanas uzņēmuma kopējās izmaksas, bērnu zābaku, sieviešu zābaku vidējās izmaksas!
TC=2130 € ; ATC_b≈20,22 € ; ATC_s≈20,33 €
Kura produkta ražošanas izmaksas ir lielākas?
Sieviešu zābaku vidējās izmaksas ir lielākas nekā bērnu zābaku vidējās izmaksas:
ATC_s>ATC_b
Dots:
Q_b=45
TC_b=910 €
Q_s=60
TC_s=1220 €
Atrast:
TC - ?
ATC_b - ?
ATC_s - ?
ATC_b ∨ ATC_s - ?
Risinājums:
TC=TC_b+ TC_s
TC=910 €+1220 €=2130 €
ATC_b=(TC_b)/Q_b
ATC_b=(910 €)/45 ≈20,22 €
ATC_s=(TC_s)/Q_s
ATC_s=(1220 €)/60≈20,33 €
ATC_b<ATC_s
5. uzdevums. IK «Rotas» saražoto rotaslietu viena komplekta mainīgās izmaksas ir Ls 5,50, pastāvīgās izmaksas Ls 24 mēnesi. Mēnesī viņa izgatavo 40 komplektus. Komplekta tirgus cena ir Ls 15,00.
1) Kādas ir šo komplektu kopējās izmaksas? TC=244 Ls
2) Kāda ir pirms nodokļu peļņa, ja tiek pārdoti visi komplekti? Peļņa=356
- The workshop will provide an introductory crash course in inferential statistics, covering key concepts like identifying appropriate statistical procedures, checking assumptions, and correctly interpreting p-values.
- The first half will focus on foundational theories like understanding data descriptions and hypothesis tests. The second half will cover tools for regression, count, and multiple data/predictor situations.
- Key resources include the STATS 1024 textbook and selected chapters.
Quicksort is a sorting algorithm that uses a divide and conquer approach. It works by selecting a pivot element and partitioning the array around the pivot so that elements less than the pivot are to its left and greater elements are to its right. It then recursively applies this process to the subarrays until each contains a single element, at which point the array is fully sorted. The example demonstrates quicksort sorting an array from 0 to 7.
This document provides an overview of statistical concepts such as population, sample, variables, frequency tables, and graphical representations. It defines key terms like population, sample, qualitative and quantitative variables. It also explains how to create frequency tables and calculates absolute, relative, and cumulative frequencies. Finally, it gives examples of different types of graphical representations like bar graphs, pie charts, and pictograms.
This Presentation course will help you in understanding the Machine Learning model i.e. Generalized Linear Models for classification and regression with an intuitive approach of presenting the core concepts
This document provides information about frequency tables and statistical concepts. It defines key terms like population, sample, qualitative and quantitative variables. It also explains how to create frequency tables for qualitative and quantitative variables and calculate absolute, relative and cumulative frequencies. Examples of frequency tables are provided for different types of variables. The document is intended to teach students about basic statistical and frequency table concepts.
This document provides information about frequency tables and statistical concepts. It defines key terms like population, sample, qualitative and quantitative variables. It also explains how to create frequency tables for qualitative and quantitative variables and calculate absolute, relative and cumulative frequencies. Examples of frequency tables are provided for different types of variables. The document is intended to teach students about basic statistical and frequency table concepts.
1) Simple linear regression models the relationship between a dependent variable (Y) and a single independent variable (X) as a linear equation. It finds the line of best fit to the data and uses this to estimate or predict future values of Y based on X.
2) The document provides an example of using simple linear regression to model the relationship between weekly sales (Y) and advertising expenditures (X) for a retail merchant. It estimates the regression equation and uses this to predict sales for a given expenditure level.
3) Key outputs of the simple linear regression analysis are presented, including estimating the regression coefficients, testing their significance, calculating confidence intervals and analyzing the variance (ANOVA).
This document provides information on frequency tables and statistical concepts. It defines key terms like population, sample, qualitative and quantitative variables. It also explains how to create frequency tables for qualitative and quantitative variables by listing the values/intervals, frequencies, relative frequencies, and cumulative relative frequencies. Examples of frequency tables are provided for different types of data. The document is intended to teach students about organizing and representing statistical data in tables.
This document discusses concepts related to statistics and probability such as measures, position, quartiles, deciles, and percentiles. It provides examples of calculating the first quartile (Q1) from a set of scored data and defines the sample space and possible outcomes for random variables. It asks to find the third quartile (Q3), 11th decile (D11), and 90th percentile (P90) for a given data set.
The document provides information about statistics for entrepreneurs, including links to download materials on topics such as business statistics, statistical analysis, research methods, forecasting methods, and data smoothing techniques. It also contains examples and solutions to exercises on concepts like moving averages, seasonality, mean, median, mode, range, and standard deviation. The document is intended as a resource for participants in a postgraduate program in social entrepreneurship.
Raimundo Soto - Catholic University of Chile
ERF Training on Advanced Panel Data Techniques Applied to Economic Modelling
29 -31 October, 2018
Cairo, Egypt
1) This document discusses kinematics equations for motion in one and two dimensions. It presents the equations for position, velocity, and acceleration as vectors along the x and y axes.
2) Equations are developed for the velocity and position of an object experiencing constant acceleration due to gravity along the y-axis of a projectile motion.
3) The equations derived allow calculating the velocity, position, and acceleration of an object along each axis over time given the initial position and velocity.
The document contains several physics problems involving kinematics and vector addition. It provides data such as distances, velocities, and angular velocities. It asks the reader to calculate unknown velocities and angular velocities using the given information and equations for vector addition and kinematics. Multiple problems are presented with varying configurations of objects and vectors to be solved step-by-step.
Online Faculty Development Program-cum-certificate course on Research Analysis: Tools and Techniques Jointly organized by FGM Govt College Adampur, Hisar, GAD TLC, Khalsa College University of Delhi and #Heera Psychological Testing Research and Consultancy, Rewari.
Full presentation: https://youtu.be/VUglQZ8eoSk
This document discusses a study conducted on customer satisfaction towards Pantaloons store in Bhubaneswar, India. It provides background on Pantaloons and details on the research methodology, which involved a survey of 100 respondents using a questionnaire. The results of the survey are presented in tables showing responses by age group to questions on shopping preferences, store visit frequency, product ratings, customer service ratings and overall satisfaction levels. In general, most customers were satisfied with prices, product quality and customer service at Pantaloons. Younger customers tended to visit more frequently and rated the shopping experience more positively compared to older age groups.
This document discusses various measures of dispersion used to describe how spread out or clustered data values are around a central measure like the mean or median. It defines absolute and relative measures of dispersion and explains key measures like range, interquartile range, quartile deviation, mean deviation, and their coefficients. Examples are provided to demonstrate calculating each measure for both ungrouped and grouped data. The advantages and disadvantages of range, quartile deviation, and mean deviation are also outlined.
Statistical Analysis using Central TendenciesCelia Santhosh
This document discusses various statistical measures of central tendency, including the mean, median, and mode. It provides definitions and formulas for calculating the arithmetic mean using direct, shortcut, and step deviation methods for individual, discrete, and continuous data series. It also discusses how to calculate the median and weighted mean. The document compares the merits and demerits of the arithmetic mean and provides examples to illustrate the different calculation techniques for central tendencies.
This document contains data on the number of home appliances owned by 60 Grade 7 students. It includes tables showing the frequency distribution and calculations of measures of central tendency (mean, median, mode) and variation (range, variance, standard deviation). The modal class is 1-10 appliances, with a frequency of 28. The median is in the class range of 21-30 appliances. The mean number of appliances is 15.66. The range of appliances owned is from 1 to 70, with a standard deviation of 14.08 from the mean.
- The document presents data on the population distribution in the United States by age group, with the total population being 265 million people. It includes the percentage and number of people in each age group.
- Some key figures extracted from the data include that people under 59 years old account for 221.275 million people, and those over 55 years make up 20.8% of the population. The smallest age group is 85 years and older, with 3.71 million people.
- The second part of the document examines survey data from 100 families on their weekly food spending, categorizing the results into spending ranges and calculating statistics like the mean, median and mode.
From temporal to static networks, and backPetter Holme
Infectious diseases are a major burden to global health. Understanding their mechanisms and being able to predict and intervene epidemic outbreaks is an important challenge for researchers and decision makers alike. It should not be too hard either―if we include human contact patterns, the mechanisms of contagion and the typical features of the disease, we could model most infectious-disease related phenomena. Of these three components, the network epidemiology of the last decade has shown that our limited understanding of human contact patterns is probably the most important focus are for advancing infectious disease epidemiology. We will discuss what is known about human contact patterns and how to include this knowledge in epidemic modeling. First, we discuss recent work on what the epidemiologically most important temporal structures of human contacts are. We use about 80 empirical temporal network datasets, several arguably important for disease spreading, and scan the entire parameter space of disease-spreading models. By comparing to null-models, we identify important, simple temporal patterns that affect disease spreading stronger than the bursty interevent time distributions. Furthermore, we investigate how to eliminate the temporal information to make an as relevant static network as possible. After all, static network epidemiology has more methods and results than temporal network epidemiology and it for some purposes it is necessary. We find that an “exponential threshold” representation almost always the best performance, but time-sliced network (with a carefully chosen window, usually considerably different than the sampling time of the data) works almost as good. In contrast, networks of concurrent contacts do not seem to carry so important information.
The document summarizes the simplex method for solving linear programming problems involving maximization. It involves 12 steps: 1) Formulating the LPP, 2) Introducing slack, surplus and artificial variables, 3) Formulating the initial basic solution, 4) Constructing the initial simplex table, 5) Checking for positive elements in the Cj-Zj row, 6) Identifying the incoming basic variable, 7) Choosing the incoming basic variable if multiple positives exist, 8) Identifying the outgoing basic variable, 9) Constructing the next simplex table using row operations, 10) Completing the new simplex table, 11) Repeating steps 5-10, and 12) Terminating when the
This document provides an overview of statistical concepts such as population, sample, variables, frequency tables, and graphical representations. It defines key terms like population, sample, qualitative and quantitative variables. It also explains how to create frequency tables and calculates absolute, relative, and cumulative frequencies. Finally, it gives examples of different types of graphical representations like bar graphs, pie charts, and pictograms.
This Presentation course will help you in understanding the Machine Learning model i.e. Generalized Linear Models for classification and regression with an intuitive approach of presenting the core concepts
This document provides information about frequency tables and statistical concepts. It defines key terms like population, sample, qualitative and quantitative variables. It also explains how to create frequency tables for qualitative and quantitative variables and calculate absolute, relative and cumulative frequencies. Examples of frequency tables are provided for different types of variables. The document is intended to teach students about basic statistical and frequency table concepts.
This document provides information about frequency tables and statistical concepts. It defines key terms like population, sample, qualitative and quantitative variables. It also explains how to create frequency tables for qualitative and quantitative variables and calculate absolute, relative and cumulative frequencies. Examples of frequency tables are provided for different types of variables. The document is intended to teach students about basic statistical and frequency table concepts.
1) Simple linear regression models the relationship between a dependent variable (Y) and a single independent variable (X) as a linear equation. It finds the line of best fit to the data and uses this to estimate or predict future values of Y based on X.
2) The document provides an example of using simple linear regression to model the relationship between weekly sales (Y) and advertising expenditures (X) for a retail merchant. It estimates the regression equation and uses this to predict sales for a given expenditure level.
3) Key outputs of the simple linear regression analysis are presented, including estimating the regression coefficients, testing their significance, calculating confidence intervals and analyzing the variance (ANOVA).
This document provides information on frequency tables and statistical concepts. It defines key terms like population, sample, qualitative and quantitative variables. It also explains how to create frequency tables for qualitative and quantitative variables by listing the values/intervals, frequencies, relative frequencies, and cumulative relative frequencies. Examples of frequency tables are provided for different types of data. The document is intended to teach students about organizing and representing statistical data in tables.
This document discusses concepts related to statistics and probability such as measures, position, quartiles, deciles, and percentiles. It provides examples of calculating the first quartile (Q1) from a set of scored data and defines the sample space and possible outcomes for random variables. It asks to find the third quartile (Q3), 11th decile (D11), and 90th percentile (P90) for a given data set.
The document provides information about statistics for entrepreneurs, including links to download materials on topics such as business statistics, statistical analysis, research methods, forecasting methods, and data smoothing techniques. It also contains examples and solutions to exercises on concepts like moving averages, seasonality, mean, median, mode, range, and standard deviation. The document is intended as a resource for participants in a postgraduate program in social entrepreneurship.
Raimundo Soto - Catholic University of Chile
ERF Training on Advanced Panel Data Techniques Applied to Economic Modelling
29 -31 October, 2018
Cairo, Egypt
1) This document discusses kinematics equations for motion in one and two dimensions. It presents the equations for position, velocity, and acceleration as vectors along the x and y axes.
2) Equations are developed for the velocity and position of an object experiencing constant acceleration due to gravity along the y-axis of a projectile motion.
3) The equations derived allow calculating the velocity, position, and acceleration of an object along each axis over time given the initial position and velocity.
The document contains several physics problems involving kinematics and vector addition. It provides data such as distances, velocities, and angular velocities. It asks the reader to calculate unknown velocities and angular velocities using the given information and equations for vector addition and kinematics. Multiple problems are presented with varying configurations of objects and vectors to be solved step-by-step.
Online Faculty Development Program-cum-certificate course on Research Analysis: Tools and Techniques Jointly organized by FGM Govt College Adampur, Hisar, GAD TLC, Khalsa College University of Delhi and #Heera Psychological Testing Research and Consultancy, Rewari.
Full presentation: https://youtu.be/VUglQZ8eoSk
This document discusses a study conducted on customer satisfaction towards Pantaloons store in Bhubaneswar, India. It provides background on Pantaloons and details on the research methodology, which involved a survey of 100 respondents using a questionnaire. The results of the survey are presented in tables showing responses by age group to questions on shopping preferences, store visit frequency, product ratings, customer service ratings and overall satisfaction levels. In general, most customers were satisfied with prices, product quality and customer service at Pantaloons. Younger customers tended to visit more frequently and rated the shopping experience more positively compared to older age groups.
This document discusses various measures of dispersion used to describe how spread out or clustered data values are around a central measure like the mean or median. It defines absolute and relative measures of dispersion and explains key measures like range, interquartile range, quartile deviation, mean deviation, and their coefficients. Examples are provided to demonstrate calculating each measure for both ungrouped and grouped data. The advantages and disadvantages of range, quartile deviation, and mean deviation are also outlined.
Statistical Analysis using Central TendenciesCelia Santhosh
This document discusses various statistical measures of central tendency, including the mean, median, and mode. It provides definitions and formulas for calculating the arithmetic mean using direct, shortcut, and step deviation methods for individual, discrete, and continuous data series. It also discusses how to calculate the median and weighted mean. The document compares the merits and demerits of the arithmetic mean and provides examples to illustrate the different calculation techniques for central tendencies.
This document contains data on the number of home appliances owned by 60 Grade 7 students. It includes tables showing the frequency distribution and calculations of measures of central tendency (mean, median, mode) and variation (range, variance, standard deviation). The modal class is 1-10 appliances, with a frequency of 28. The median is in the class range of 21-30 appliances. The mean number of appliances is 15.66. The range of appliances owned is from 1 to 70, with a standard deviation of 14.08 from the mean.
- The document presents data on the population distribution in the United States by age group, with the total population being 265 million people. It includes the percentage and number of people in each age group.
- Some key figures extracted from the data include that people under 59 years old account for 221.275 million people, and those over 55 years make up 20.8% of the population. The smallest age group is 85 years and older, with 3.71 million people.
- The second part of the document examines survey data from 100 families on their weekly food spending, categorizing the results into spending ranges and calculating statistics like the mean, median and mode.
From temporal to static networks, and backPetter Holme
Infectious diseases are a major burden to global health. Understanding their mechanisms and being able to predict and intervene epidemic outbreaks is an important challenge for researchers and decision makers alike. It should not be too hard either―if we include human contact patterns, the mechanisms of contagion and the typical features of the disease, we could model most infectious-disease related phenomena. Of these three components, the network epidemiology of the last decade has shown that our limited understanding of human contact patterns is probably the most important focus are for advancing infectious disease epidemiology. We will discuss what is known about human contact patterns and how to include this knowledge in epidemic modeling. First, we discuss recent work on what the epidemiologically most important temporal structures of human contacts are. We use about 80 empirical temporal network datasets, several arguably important for disease spreading, and scan the entire parameter space of disease-spreading models. By comparing to null-models, we identify important, simple temporal patterns that affect disease spreading stronger than the bursty interevent time distributions. Furthermore, we investigate how to eliminate the temporal information to make an as relevant static network as possible. After all, static network epidemiology has more methods and results than temporal network epidemiology and it for some purposes it is necessary. We find that an “exponential threshold” representation almost always the best performance, but time-sliced network (with a carefully chosen window, usually considerably different than the sampling time of the data) works almost as good. In contrast, networks of concurrent contacts do not seem to carry so important information.
Similar to Decision theory introductory problem (20)
The document summarizes the simplex method for solving linear programming problems involving maximization. It involves 12 steps: 1) Formulating the LPP, 2) Introducing slack, surplus and artificial variables, 3) Formulating the initial basic solution, 4) Constructing the initial simplex table, 5) Checking for positive elements in the Cj-Zj row, 6) Identifying the incoming basic variable, 7) Choosing the incoming basic variable if multiple positives exist, 8) Identifying the outgoing basic variable, 9) Constructing the next simplex table using row operations, 10) Completing the new simplex table, 11) Repeating steps 5-10, and 12) Terminating when the
A publisher has contracted an author to produce a textbook. The production process involves the author submitting a manuscript and files, editing, sample page and cover design, artwork, formatting, and printing. The critical path through the network is the author submitting the manuscript, editing, formatting, artwork approval, plate production, and binding, taking 17 weeks total to complete the project.
This flowchart outlines an optimization process to find an optimal solution. It starts with finding an initial basic solution, then checks if that solution is optimal. If it is optimal, that solution is the final answer. If not, the process seeks a better solution to try and find the optimal one.
The document outlines a strategic management model that includes four main stages: strategic intent, formulation, implementation, and evaluation. It involves analyzing internal and external environments to determine a vision, mission, goals and objectives. Strategies are then formulated, implemented through resource allocation and structure, and evaluated for effectiveness with feedback into reformulation.
The document presents a linear programming problem to determine the optimal production mix for two products (P1 and P2) that maximizes profit. The products have different processing times and resource requirements on milling and drilling machines, which have limited weekly hours. The problem is formulated as a linear program to maximize total profit subject to the machine hour constraints. Slack variables are introduced and the problem is solved using the simplex method to find the optimal production levels of 50 units of P1 and 20 units of P2, yielding maximum profit of Rs. 20,500.
This document presents a linear programming problem involving assigning quality inspectors to minimize total inspection costs. There are two types of inspectors (Grade I and Grade II) with different inspection rates and accuracy. The objective is to minimize total costs based on wages, inspection pieces, and error costs with constraints on minimum inspection pieces and available inspectors.
This document formulates a linear programming problem to determine the optimal production quantities of Products P1 and P2 given machine time and contribution margin constraints. Product P1 takes 4 hours on machine M1 and 2 hours on M2, while Product P2 takes 2 hours on M1 and 4 hours on M2. The objective is to maximize total contribution by choosing the quantities x1 and x2 subject to the 60 hours available on M1, 48 hours on M2, and non-negativity constraints.
The document describes a production problem involving two products (P1 and P2) that are manufactured using two machines (M1 and M2). P1 requires 4 hours on M1 and 2 hours on M2, while P2 requires 2 hours on M1 and 4 hours on M2. The goal is to determine the optimal quantities of P1 and P2 to maximize total contribution, given 60 hours available on M1 and 48 hours on M2. This problem is modeled as a linear programming problem and graphically solved by plotting the constraint lines and finding their intersection point.
The document contains information about game theory including pure strategies, mixed strategies, and solving games. It provides examples of games represented as payoff matrices and discusses applying the principles of dominance, algebraic methods for 2x2 games, graphical methods for 2xn and mx2 games, and linear programming for mxn games. It also includes an example analyzing a 6x6 game modeling the Allied invasion of Normandy in WWII.
Four teams will participate in a game involving selecting strategies of A or B. The aim is to score the maximum dividends. Scoring is based on the number of As and Bs selected. The document then explains the Prisoner's Dilemma game theory concept where two prisoners can either cooperate or betray each other, and discusses why rational individuals may not cooperate even if it is in their best interest to do so.
The document discusses how to formulate the dual of a primal linear programming problem. It provides 10 steps for converting a primal maximization problem into a dual minimization problem. As an example, it formulates the dual of the primal problem: Maximize z = -5x1 + 2x2 subject to x1 - x2 ≥ 2 and 2x1 + 3x2 ≤ 5, with non-negativity constraints. The dual is formulated as: Minimize z = -2y1 + 5y2 subject to -y1 + 2y2 ≥ -5 and y1 + 3y2 ≥ 2, with non-negativity constraints on the dual variables y1 and y2.
A finance manager is considering drilling a well on their property. Based on past data, there is a 70% chance of finding water at 20 meters depth, and a 20% chance of finding water between 20-25 meters if no water is found at 20 meters. The costs to drill are Rs.500 per meter plus Rs.15,000 to buy water externally if the well is not drilled. The optimal decision tree strategy is to first drill to 20 meters, and if no water, then drill further to 25 meters, resulting in an expected cost of Rs. 11,350.
The grocer must decide how many cases of milk to stock for tomorrow's demand. Each case sold yields a profit of Rs.3, but unsold cases at the end of the day lose Rs.5. Historical demand data shows the number of cases demanded and the probability of each quantity. The optimal decision can be determined by calculating the expected monetary value (EMV) of stocking different quantities of milk based on the probabilities and outcomes. The expected profit for the grocer if they stock the quantity with the highest EMV is Rs.47.7.
A fast food chain wants to build four new stores and received bids from six construction companies. The document shows the bid amounts in a table and describes using the Hungarian method to determine the optimal assignment of companies to stores that minimizes the total cost. The method involves reducing the table through successive steps to reveal a unique solution with no remaining zeros. The result assigns each store to a single construction company to minimize the total cost for building all four stores.
Operations research (OR) is a tool used to increase the effectiveness of managerial decisions. It can help with profit maximization, production management like determining optimal product mix and scheduling, financial management, marketing management, and personnel management. Some common OR models include linear programming, transportation, assignment, and sequencing problems. OR uses mathematical techniques like linear programming, decision theory, game theory, queuing theory, simulation, network analysis, and inventory models.
This presentation, "The Morale Killers: 9 Ways Managers Unintentionally Demotivate Employees (and How to Fix It)," is a deep dive into the critical factors that can negatively impact employee morale and engagement. Based on extensive research and real-world experiences, this presentation reveals the nine most common mistakes managers make, often without even realizing it.
The presentation begins by highlighting the alarming statistic that 70% of employees report feeling disengaged at work, underscoring the urgency of addressing this issue. It then delves into each of the nine "morale killers," providing clear explanations and illustrative examples.
1. Ignoring Achievements: The presentation emphasizes the importance of recognizing and rewarding employees' efforts, tailored to their individual preferences.
2. Bad Hiring/Promotions & Broken Promises: It reveals the detrimental effects of poor hiring and promotion decisions, along with the erosion of trust that results from broken promises.
3. Treating Everyone Equally & Tolerating Poor Performance: This section stresses the need for fair treatment while acknowledging that employees have different needs. It also emphasizes the importance of addressing poor performance promptly.
4. Stifling Growth & Lack of Interest: The presentation highlights the importance of providing opportunities for learning and growth, as well as showing genuine care for employees' well-being.
5. Unclear Communication & Micromanaging: It exposes the frustration and resentment caused by vague expectations and excessive control, advocating for clear communication and employee empowerment.
The presentation then shifts its focus to the power of recognition and empowerment, highlighting how a culture of appreciation can fuel engagement and motivation. It provides actionable takeaways for managers, emphasizing the need to stop demotivating behaviors and start actively fostering a positive workplace culture.
The presentation concludes with a strong call to action, encouraging viewers to explore the accompanying blog post, "9 Proven Ways to Crush Employee Morale (and How to Avoid Them)," for a more in-depth analysis and practical solutions.
A comprehensive-study-of-biparjoy-cyclone-disaster-management-in-gujarat-a-ca...Samirsinh Parmar
Disaster management;
Cyclone Disaster Management;;
Biparjoy Cyclone Case Study;
Meteorological Observations;
Best practices in Disaster Management;
Synchronization of Agencies;
GSDMA in Cyclone disaster Management;
History of Cyclone in Arabian ocean;
Intensity of Cyclone in Gujarat;
Cyclone preparedness;
Miscellaneous observations - Biparjoy cyclone;
Role of social Media in Disaster Management;
Unique features of Biparjoy cyclone;
Role of IMD in Biparjoy Prediction;
Lessons Learned; Disaster Preparedness; published paper;
Case study; for disaster management agencies; for guideline to manage cyclone disaster; cyclone management; cyclone risks; rescue and rehabilitation for cyclone; timely evacuation during cyclone; port closure; tourism closure etc.
From Concept to reality : Implementing Lean Managements DMAIC Methodology for...Rokibul Hasan
The Ready-Made Garments (RMG) industry in Bangladesh is a cornerstone of the economy, but increasing costs and stagnant productivity pose significant challenges to profitability. This study explores the implementation of Lean Management in the Sampling Section of RMG factories to enhance productivity. Drawing from a comprehensive literature review, theoretical framework, and action research methodology, the study identifies key areas for improvement and proposes solutions.
Through the DMAIC approach (Define, Measure, Analyze, Improve, Control), the research identifies low productivity as the primary problem in the Sampling Section, with a PPH (Productivity per head) of only 4.0. Using Lean Management techniques such as 5S, Standardized work, PDCA/Kaizen, KANBAN, and Quick Changeover, the study addresses issues such as pre and post Quick Changeover (QCO) time, improper line balancing, and sudden plan changes.
The research employs regression analysis to test hypotheses, revealing a significant correlation between reducing QCO time and increasing productivity. With a regression equation of Y = -0.000501X + 6.72 and an R-squared value of 0.98, the study demonstrates a strong relationship between the independent variables (QCO downtime and improper line balancing downtime) and the dependent variable (productivity per head).
The findings suggest that by implementing Lean Management practices and addressing key productivity inhibitors, RMG factories can achieve substantial improvements in efficiency and profitability. The study provides valuable insights for practitioners, policymakers, and researchers seeking to enhance productivity in the RMG industry and similar manufacturing sectors.
Small Business Management An Entrepreneur’s Guidebook 8th edition by Byrd tes...ssuserf63bd7
Small Business Management An Entrepreneur’s Guidebook 8th edition by Byrd test bank.docx
https://qidiantiku.com/test-bank-for-small-business-management-an-entrepreneurs-guidebook-8th-edition-by-mary-jane-byrd.shtml
Many companies have perceived CRM that accompanied by numerous
uncoordinated initiatives as a technological solution for problems in
individual areas. However, CRM should be considered as a strategy when
a company decides to implement it due to its humanitarian, technological
and process-related effects (Mendoza et al., 2007, p. 913). CRM is
evolving today as it should be seen as a strategy for maintaining a longterm relationship with customers.
A CRM business strategy includes the internet with the marketing,
sales, operations, customer services, human resources, R&D, finance, and
information technology departments to achieve the company’s purpose and
maximize the profitability of customer interactions (Chen and Popovich,
2003, p. 673).
After Corona Virus Disease-2019/Covid-19 (Coronavirus) first
appeared in Wuhan, China towards the end of 2019, its effects began to
be felt clearly all over the world. If the Coronavirus crisis is not managed
properly in business-to-business (B2B) and business-to-consumer
(B2C) sectors, it can have serious negative consequences. In this crisis,
companies can typically face significant losses in their sales performance,
existing customers and customer satisfaction, interruptions in operations
and accordingly bankruptcy
m249-saw PMI To familiarize the soldier with the M249 Squad Automatic Weapon ...LinghuaKong2
M249 Saw marksman PMIThe Squad Automatic Weapon (SAW), or 5.56mm M249 is an individually portable, gas operated, magazine or disintegrating metallic link-belt fed, light machine gun with fixed headspace and quick change barrel feature. The M249 engages point targets out to 800 meters, firing the improved NATO standard 5.56mm cartridge.The SAW forms the basis of firepower for the fire team. The gunner has the option of using 30-round M16 magazines or linked ammunition from pre-loaded 200-round plastic magazines. The gunner's basic load is 600 rounds of linked ammunition.The SAW was developed through an initially Army-led research and development effort and eventually a Joint NDO program in the late 1970s/early 1980s to restore sustained and accurate automatic weapons fire to the fire team and squad. When actually fielded in the mid-1980s, the SAW was issued as a one-for-one replacement for the designated "automatic rifle" (M16A1) in the Fire Team. In this regard, the SAW filled the void created by the retirement of the Browning Automatic Rifle (BAR) during the 1950s because interim automatic weapons (e.g. M-14E2/M16A1) had failed as viable "base of fire" weapons.
Early in the SAW's fielding, the Army identified the need for a Product Improvement Program (PIP) to enhance the weapon. This effort resulted in a "PIP kit" which modifies the barrel, handguard, stock, pistol grip, buffer, and sights.
The M249 machine gun is an ideal complementary weapon system for the infantry squad platoon. It is light enough to be carried and operated by one man, and can be fired from the hip in an assault, even when loaded with a 200-round ammunition box. The barrel change facility ensures that it can continue to fire for long periods. The US Army has conducted strenuous trials on the M249 MG, showing that this weapon has a reliability factor that is well above that of most other small arms weapon systems. Today, the US Army and Marine Corps utilize the license-produced M249 SAW.
Neal Elbaum Shares Top 5 Trends Shaping the Logistics Industry in 2024Neal Elbaum
In the ever-evolving world of logistics, staying ahead of the curve is crucial. Industry expert Neal Elbaum highlights the top five trends shaping the logistics industry in 2024, offering valuable insights into the future of supply chain management.
Maximize Your Efficiency with This Comprehensive Project Management Platform ...SOFTTECHHUB
In today's work environment, staying organized and productive can be a daunting challenge. With multiple tasks, projects, and tools to juggle, it's easy to feel overwhelmed and lose focus. Fortunately, liftOS offers a comprehensive solution to streamline your workflow and boost your productivity. This innovative platform brings together all your essential tools, files, and tasks into a single, centralized workspace, allowing you to work smarter and more efficiently.
3. Suppose an electrical goods merchant buys, for resale purposes in a
market, electric irons in the range of 0 to 4. His resources permit him
to buy nothing or 1 or 2 or 3 or 4 units. These are his alternative
courses of action or strategies. The demand for electric irons on any
day is something beyond his control and hence is a state of nature.
Let us presume that the dealer does not know how many units will
be bought from him by the customers. The demand could be
anything from 0 to 4. The dealer can buy each unit of electric iron @
Rs.40 and sell it at Rs.45 each, his margin being Rs.5 per unit.
Assume the stock on hand is valueless. Portray in a payoff table and
opportunity loss table the quantum of total margin (loss), that he
gets in relation to various alternative strategies and states of nature.
4. Payoff Matrix
Courses of Action
0 1 2 3 4
States
of
Nature
0 0–0=0
1 0–0=0
2 0–0=0
3 0–0=0
4 0–0=0
5. Payoff Matrix
Courses of Action
0 1 2 3 4
States
of
Nature
0 0–0=0 0–40=–40
1 0–0=0 45–40=5
2 0–0=0 45–40=5
3 0–0=0 45–40=5
4 0–0=0 45–40=5
33. DECISION CRITERIA UNDER CONDITION OF
UNCERTAINTY
• Maximin.
• Maximax.
• Minimax Regret.
• Hurwicz Criterion.
• Baye’s/Lapalce’s Criterion.
34. CRITERION OF PESSIMISM (MAXIMIN)
• Also called ‘Waldian Criterion.’
• Determine the lowest outcome for each alternative.
• Choose the alternative associated with the best of these.
35. CRITERION OF OPTIMISM (MAXIMAX)
• Suggested by Leonid Hurwicz.
• Determine the best outcome for each alternative.
• Select the alternative associated with the best of these.
36. MINIMAX REGRET CRITERION
• Attributed to Leonard Savage.
• For each state, identify the most attractive alternative.
• Place a zero in those cells.
• Compute opportunity loss for other alternatives.
• Identify the maximum opportunity loss for each alternative.
• Select the alternative associated with the lowest of these.
37. CRITERION OF REALISM (HURWICZ CRITERION)
• A compromise between maximax and maximin criteria.
• A coefficient of optimism α (0≤α≤1) is selected.
• When α is close to 1, the decision-maker is optimistic about the
future.
• When α is close to 0, the decision-maker is pessimistic about the
future.
39. LAPLACE CRITERION
• Assign equal probabilities to each state.
• Compute the expected value for each alternative.
• Select the alternative with the highest alternative.