This chapter discusses decision making under uncertainty. It describes the basic steps in decision making as listing alternative actions, uncertain events, determining payoffs, and adopting decision criteria. It introduces payoff tables and decision trees as methods to display this information. Expected monetary value and expected opportunity loss are presented as decision criteria that aim to maximize expected payoff or minimize expected loss. The value of perfect information is defined as the expected gain from knowing the outcome with certainty compared to the best action under uncertainty. Finally, it discusses how to account for risk by considering the variability of payoffs through measures like variance and standard deviation.
This document discusses time-series forecasting and index numbers. It begins by outlining the chapter goals, which are to develop basic forecasting models, identify time-series components, use smoothing and trend-based forecasting models, forecast seasonal data, and compute index numbers. The document then explains key concepts like time-series plots and components, moving averages, exponential smoothing, trend-based forecasting using linear, quadratic and exponential models, and model selection criteria. Examples are provided throughout to illustrate time-series smoothing and forecasting techniques.
Statistical Applications in Quality and Productivity ManagementYesica Adicondro
This chapter discusses quality management tools such as Total Quality Management, Six Sigma, and control charts. It introduces Deming's 14 Points for quality management and the Shewhart-Deming cycle. Six Sigma uses the DMAIC model to reduce defects. Control charts monitor process variation and distinguish common from special causes. The p chart is for proportions while the X and R charts monitor process averages and ranges for numeric data.
This document outlines a statistical analysis project involving daily stock price data from 2008-2009. Students are instructed to choose an index from the ASX, analyze the daily change in closing price for 2008 using descriptive statistics and statistical inference, and explore the relationship between daily volume and turnover for the same index in 2009 using regression. The project is split into two parts worth 15% of the overall course assessment. Detailed instructions are provided on statistical tasks to complete, reporting requirements, and grading criteria.
Analyzing and forecasting time series data ppt @ bec domsBabasab Patil
This document discusses forecasting time-series data using various models. It covers identifying components in time series, computing index numbers, smoothing-based and trend-based forecasting models, measuring forecast accuracy, and addressing autocorrelation. The key steps are developing models, identifying trends and seasonal components, computing forecasts, and comparing forecasts to actual data to evaluate model fit.
Chap19 time series-analysis_and_forecastingVishal Kukreja
Trend + Seasonality + Cyclical + Irregular
Multiplicative Model
X t = Trend × Seasonality × Cyclical × Irregular
This chapter discusses time-series analysis and forecasting methods. It covers computing and interpreting index numbers, testing for randomness, and identifying trend, seasonality, cyclical and irregular components in a time series. It also describes smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average models. The chapter aims to help readers analyze time-series data and develop forecasts.
This document provides instructions for building several reports in IBM Cognos Insight using an HR dataset. The first report analyzes employee performance, course plans, and satisfaction levels. The second report studies voluntary attrition among higher-performing employees. The third report performs a return on investment analysis by examining employment costs, training costs, and their effect on organization revenue and profits. Key tasks include importing the dataset, selecting and filtering fields, applying calculations, creating hierarchies, and analyzing trends through charts and maps.
This document provides an overview of simulation modeling. It defines a system as any set of interrelated components acting together to achieve a common objective. A model represents the structure of a real system through simplification, abstraction, and assumptions. Simulation is the process of running a computer model of a real system to study or experiment with it. There are different types of simulations depending on whether changes are continuous or discrete over time and whether aspects are deterministic or stochastic. Monte Carlo simulation uses random sampling to approximate expectations while discrete event simulation models systems as sequences of discrete events over time. Examples provided include using Monte Carlo to estimate pi and modeling a single machine system in discrete event simulation software.
The Development of Financial Information System and Business Intelligence Usi...IJERA Editor
One of the most emerging technologies is finance, becoming more amenable to data-driven modeling as large sets of financial data become available everywhere. So we are applying the data mining techniques in financial information system with Business Intelligence. A Business Intelligence System (BIS) can be described as an interactive, computer-based system designed to help decision-makers to solve unstructured problems. Using a combination of models, analytical techniques, and information retrieval, such systems help develop and evaluate appropriate alternatives.
This document discusses time-series forecasting and index numbers. It begins by outlining the chapter goals, which are to develop basic forecasting models, identify time-series components, use smoothing and trend-based forecasting models, forecast seasonal data, and compute index numbers. The document then explains key concepts like time-series plots and components, moving averages, exponential smoothing, trend-based forecasting using linear, quadratic and exponential models, and model selection criteria. Examples are provided throughout to illustrate time-series smoothing and forecasting techniques.
Statistical Applications in Quality and Productivity ManagementYesica Adicondro
This chapter discusses quality management tools such as Total Quality Management, Six Sigma, and control charts. It introduces Deming's 14 Points for quality management and the Shewhart-Deming cycle. Six Sigma uses the DMAIC model to reduce defects. Control charts monitor process variation and distinguish common from special causes. The p chart is for proportions while the X and R charts monitor process averages and ranges for numeric data.
This document outlines a statistical analysis project involving daily stock price data from 2008-2009. Students are instructed to choose an index from the ASX, analyze the daily change in closing price for 2008 using descriptive statistics and statistical inference, and explore the relationship between daily volume and turnover for the same index in 2009 using regression. The project is split into two parts worth 15% of the overall course assessment. Detailed instructions are provided on statistical tasks to complete, reporting requirements, and grading criteria.
Analyzing and forecasting time series data ppt @ bec domsBabasab Patil
This document discusses forecasting time-series data using various models. It covers identifying components in time series, computing index numbers, smoothing-based and trend-based forecasting models, measuring forecast accuracy, and addressing autocorrelation. The key steps are developing models, identifying trends and seasonal components, computing forecasts, and comparing forecasts to actual data to evaluate model fit.
Chap19 time series-analysis_and_forecastingVishal Kukreja
Trend + Seasonality + Cyclical + Irregular
Multiplicative Model
X t = Trend × Seasonality × Cyclical × Irregular
This chapter discusses time-series analysis and forecasting methods. It covers computing and interpreting index numbers, testing for randomness, and identifying trend, seasonality, cyclical and irregular components in a time series. It also describes smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average models. The chapter aims to help readers analyze time-series data and develop forecasts.
This document provides instructions for building several reports in IBM Cognos Insight using an HR dataset. The first report analyzes employee performance, course plans, and satisfaction levels. The second report studies voluntary attrition among higher-performing employees. The third report performs a return on investment analysis by examining employment costs, training costs, and their effect on organization revenue and profits. Key tasks include importing the dataset, selecting and filtering fields, applying calculations, creating hierarchies, and analyzing trends through charts and maps.
This document provides an overview of simulation modeling. It defines a system as any set of interrelated components acting together to achieve a common objective. A model represents the structure of a real system through simplification, abstraction, and assumptions. Simulation is the process of running a computer model of a real system to study or experiment with it. There are different types of simulations depending on whether changes are continuous or discrete over time and whether aspects are deterministic or stochastic. Monte Carlo simulation uses random sampling to approximate expectations while discrete event simulation models systems as sequences of discrete events over time. Examples provided include using Monte Carlo to estimate pi and modeling a single machine system in discrete event simulation software.
The Development of Financial Information System and Business Intelligence Usi...IJERA Editor
One of the most emerging technologies is finance, becoming more amenable to data-driven modeling as large sets of financial data become available everywhere. So we are applying the data mining techniques in financial information system with Business Intelligence. A Business Intelligence System (BIS) can be described as an interactive, computer-based system designed to help decision-makers to solve unstructured problems. Using a combination of models, analytical techniques, and information retrieval, such systems help develop and evaluate appropriate alternatives.
This document provides an overview of key concepts in decision making covered in Chapter 16 of the textbook "Statistics for Managers Using Microsoft Excel". It begins by listing the chapter goals, which include describing decision making processes, constructing decision tables, applying expected value criteria, and accounting for risk attitudes. It then outlines the typical steps in decision making, such as listing alternatives and possible outcomes. Key decision making criteria are defined, like expected monetary value, expected opportunity loss, and value of perfect information. Examples are provided to demonstrate how to apply these concepts to make optimal decisions under uncertainty.
This document provides an overview of decision making techniques covered in Chapter 17. It begins by listing the learning objectives, which are to use payoff tables, decision trees, and criteria to evaluate alternative courses of action. It then outlines the steps in decision making, which include listing alternatives and uncertain events, determining payoffs, and adopting evaluation criteria. Several decision making criteria are introduced, including maximax, maximin, expected monetary value, expected opportunity loss, value of perfect information, and return-to-risk ratio. Payoff tables and decision trees are presented as methods for displaying decision problems. The chapter concludes by discussing how sample information can be used to revise old probabilities when making decisions.
This document outlines concepts related to statistical decision theory. It begins by listing the chapter goals, which are to describe decision making, expected monetary value criterion, and utility theory. It then describes the basic steps in decision making as listing actions and possible outcomes, determining payoffs, and adopting decision criteria. Two methods for organizing this information are presented: payoff tables and decision trees. The document goes on to discuss different decision criteria that can be used depending on whether or not probabilities are known, including maximin, minimax regret, and expected monetary value. Examples are provided to illustrate how to apply these concepts and criteria when evaluating decision problems.
The document describes multiple regression analysis and its applications in business decision making. It explains that multiple regression allows examination of the linear relationship between one dependent variable and two or more independent variables. The chapter goals are to help readers apply and interpret multiple regression, perform residual analysis, and test significance of variables. An example of using price and advertising spending to predict pie sales is provided to illustrate multiple regression concepts.
This document outlines the key concepts and methods for decision making under uncertainty, risk, and certainty. It discusses tools like decision trees and tables that can be used to evaluate alternatives and choose the best option. Specific techniques covered include expected monetary value, maximax/maximin, and expected value of perfect information. Examples are provided to demonstrate how to use decision trees to model sequential or complex decisions and calculate the expected monetary value at each decision node. The document is intended to teach students the fundamentals and applications of quantitative decision making.
This document provides an overview of decision analysis and decision making under certainty and uncertainty. It describes decision environments like certainty, where outcomes are known, and uncertainty, where outcomes are unknown. It also defines decision criteria for nonprobabilistic decisions, where probabilities are unknown, and probabilistic decisions, which consider probabilities. Examples are given of decision criteria like expected value, maximax, maximin and minimax regret. Payoff tables, opportunity loss tables, and decision trees are used to demonstrate the application of these decision criteria.
This chapter discusses important discrete probability distributions used in business statistics. It introduces discrete random variables and their probability distributions. It defines the binomial distribution and explains how to calculate probabilities using the binomial formula. Examples are provided to demonstrate calculating the mean, variance, and covariance of discrete random variables, as well as the expected value and risk of investment portfolios. Counting techniques like combinations are also discussed for calculating binomial probabilities.
This chapter discusses important discrete probability distributions used in statistics. It begins with an introduction to discrete random variables and probability distributions. It then covers the key concepts of mean, variance, standard deviation, and covariance for discrete distributions. The chapter focuses on explaining the binomial, hypergeometric, and Poisson distributions and how to calculate probabilities using them. It concludes with examples of how to apply these distributions to areas like finance.
The document summarizes key points about multiple regression analysis from the chapter. It discusses applying multiple regression to business problems, interpreting regression output, performing residual analysis, and testing significance. Graphs and equations are provided to illustrate multiple regression concepts like predicting outcomes, determining variation explained, and checking assumptions.
Some Important Discrete Probability DistributionsYesica Adicondro
The chapter discusses important discrete probability distributions used in statistics for managers. It covers the binomial, hypergeometric, and Poisson distributions. The binomial distribution describes the number of successes in a fixed number of trials when the probability of success is constant. It has applications in areas like manufacturing and marketing. The key characteristics of the binomial distribution are its mean, variance, and standard deviation. Examples are provided to demonstrate how to calculate probabilities and characteristics of the binomial distribution. Tables can also be used to find binomial probabilities.
Assignment 2 lasa 1 the costs of productioncoursesexams1
Joseph Farms, Inc. is a small agricultural firm that has provided limited cost data to determine the profit maximizing output level. You are asked to complete two tables analyzing the cost and revenue data for output levels from 1 to 10 units. Table 1 requires calculating total, average, and marginal costs and revenues. Table 2 calculates total revenue, profit, and loss for each output level. You must also explain the marginal cost equals marginal revenue rule, identify the profit maximizing output, and explain why a purely competitive firm is a price taker.
This document discusses the use of simulation modeling techniques like Monte Carlo simulation to analyze probabilistic problems and decision-making. It provides examples of using random numbers in Excel to simulate demand for laptops over multiple weeks and analyze the impact of ordering policies on revenues and shortages over time. Simulation can be used in areas like production, inventory, logistics, and services to model complex real-world systems with uncertain variables.
This chapter discusses time-series forecasting and index numbers. It aims to develop basic forecasting models using smoothing methods like moving averages and exponential smoothing. It also covers trend-based forecasting using linear and nonlinear regression models. Time-series data contains trend, seasonal, cyclical, and irregular components that must be accounted for. Forecasting future values involves identifying patterns in historical data and extending those patterns into the future.
This document outlines the key goals and concepts covered in Chapter 6 of the textbook "Statistics for Managers Using Microsoft Excel". The chapter introduces continuous probability distributions, including the normal, uniform, and exponential distributions. It describes the characteristics of the normal distribution and how to translate problems into standardized normal distribution problems. The chapter also covers sampling distributions, the central limit theorem, and how to find probabilities using the normal distribution table.
To simulate is to try to duplicate the features, appearance and characteristics of a real system.
The idea behind simulation is to imitate a real-world situation mathematically, to study its properties and operating characteristics, to draw conclusions and make action decisions based on the results of the simulation.
The real-life system is not touched until the advantages and disadvantages of what may be a major policy decision are first measured on the system's model.
This chapter discusses statistical decision theory and making decisions under uncertainty. It covers constructing payoff tables and decision trees to list possible actions and outcomes. It introduces several decision criteria for selecting the best action, including maximin, minimax regret, and expected monetary value. Expected monetary value calculates the weighted average payoff using the probabilities of different outcomes. Bayes' theorem allows revising probabilities based on new sample information. The key goals are describing decision making processes and criteria for evaluating options under uncertainty.
Complete Table-1 (Joseph Farms, Inc., Cost and Revenue Data), either.docxluellaj
Complete Table-1 (Joseph Farms, Inc., Cost and Revenue Data), either as a Microsoft Excel spreadsheet, or as a Microsoft Word table. Assume that the price is $165 and the fixed costs are $125, at an output level of 1. Also assume that the data represents a firm in pure competition. Show your calculations in summary form.
What is the MC=MR Rule? To what market structures does this Rule apply? Explain your answers.
Using Microsoft Excel, graph the data in Columns 9 and 10.
What is the profit maximizing (or loss minimizing) output for this firm? Is there an economic profit? Explain your answers.
Explain why a firm in pure competition is considered to be a “price taker.”
Using the data in Table-1 (Joseph Farms, Inc., Cost and Revenue Data), complete Table-2 (Joseph Farms, Inc., Revenue/Profit/Loss Data), either as a Microsoft Excel spreadsheet, or as a Microsoft Word table. Show your calculations in summary form.
Using the data in Table-2 (Joseph Farms, Inc., Revenue/Profit/Loss Data), what is the break even output level for this firm? If this firm is in pure competition, at what output level would they operate? Show your calculations in summary form
Table-1: Joseph Farms, Inc., Cost and Revenue Data
Column 1
Column 2
Column 3
Column 4
Column 5
Column 6
Column 7
Column 8
Column 9
Column 10
Column 11
Output
Price per unit
Total Fixed Cost
Total Variable Cost
Total Cost
Average Fixed Cost
Average Variable Cost
Average Total Cost
Marginal
Marginal Revenue
Total Revenue
Level
Cost
0
$ -
NA
1
$ 113.00
2
$ 213.00
3
$ 300.00
4
$ 375.00
5
$ 463.00
6
$ 563.00
7
$ 675.00
8
$ 813.00
Complete Table-1 (Joseph Farms, Inc., Cost and Revenue Data), either as a Microsoft Excel spreadsheet, or as a Microsoft Word table. Assume that the price is $165 and the fixed costs are $125, at an output level of 1. Also assume that the data represents a firm in pure competition. Show your calculations in summary form.
What is the MC=MR Rule? To what market structures does this Rule apply? Ex.
-
BAFN 305 - Multiple Regression Questions
The attached provides descriptive statistics, correlations and a multiple regression run for four variables.
The four variables are about corporate earnings, debt, sales and ownership. Sample size is 400
companies. You are provided means, standard deviations, correlations and two regression models, a full
and reduced model.
The variables are: Earnings per share (EPS) - the 'Y'variable-\,
Company Debt, measured in millions of dollars,
Annual Sales, measured in millions of US dollars, and
Public or private ownership, a binary of (1,0) with 1 assigned to public.
Answer the following:
L. What percent of the companies are private. How many is that
2. The best predictor of EPS is
3. The weakest predictor of EPS is
4. ldentify the independent variable(s) understudy
5. A multiple regression model for EPS = f(Debt Sales,Ownership] was run and is noted on the
attached. Answer the following:
a. Coefficient of determination is
b.TestthehypothesisthatHop=0'AIphaat.05(AcceptorReject}-
a. State the critical value
b. State the computed value
c. State the p-value
d. State the decision
e. State the conclusion.
c. State the regression equation to forecast EPS.
d. Test the hypothesis to determine the importance of each variable.
a. State the critical value for the test - Alpha at .01 TT
b. State the computed value and the p-value for each
c. State the decisions.
d. State the conclusions.
6. lf you completed the above, you evaluated the 'Full Model'
a. Would you create a reduced model based on the analysis above.
b. lf so, which variables would be kept for the reduced model.
c. lf you ran the reduced model, why did you remove the variable, multicollinearity or lack
of a relationship with 'Y'.
d. Would the model provide good prediction of EPS._ Why
7
Full Model - Earnings = f(Debt,Sales,Ownership)
Summary
R-Square .75
Standard error .75
Cases 400
Reduced Model - Earnings = f(Debt, Sales)
Summary
R-Square .71
Standard error .80
Cases 400
Earnings (per sh) Debt Sales Public/Private(1,0
Means 3.00 20 (million) 30 (million) .65
Standard Deviations 1.50 8 (million) 10 (million) .30
Correlations Earnings Debt Sales Public/Private(t,0)
Earninss 1.00
Debt -.58 1.00
Sales 0.40 0.40 1.00
Public/Private (1,0) 0.30 =.25 o.41 1.00
ANOVA Sum of squares Df Mean Square F p-value
Regression 7,500 3 2.s00 396.20 .000
Residual 2.s00 396 6.31
Total 10,000 399
Variable Coefficient Standard Error t/z statistic p-value
lntercept 1.25
Debt -.10 xxxxxxxx -2.70 .006
Sales 0.15 xxxxxxxx L.45 .090
Public/Private 0.08 xxxxxxxx L.40 .096
ANOVA Sum of squares Df Mean Square F p-value
Reeression 7,too 2 3s50 486.30 .000
Residual 2,9OO 397 7.30
Total 10,000 399
Variable Coefficients Standard error t/z statistic
lntercept 1.85
Debt -.L4 xxxxxxxx -2.90 .003
Sales .20 xxxxxxxx 2.00 ,o23
Comparative Analysis
Xxxxxxxx Yyyyyyyyy
ITM 619
xx/xx/xxxx
Dr. Webb
waelalturki
Highlight
waelalturki ...
This document outlines concepts related to decision making under uncertainty and risk. It discusses six steps to decision making, including defining the problem, listing alternatives and outcomes, and identifying payoffs. It then covers various decision making models for uncertainty, like maximax, maximin, and expected monetary value. Sensitivity analysis is introduced as a way to examine how decisions may change with different input data. The document uses examples and tables to illustrate key concepts in decision analysis.
Konsep Balanced Score Card. Penilaian kinerja dilihat dari 4 perspektif yaitu perspektif keuangan, konsumen, learn and growth dan proses bisnis internal.
This document provides an overview of key concepts in decision making covered in Chapter 16 of the textbook "Statistics for Managers Using Microsoft Excel". It begins by listing the chapter goals, which include describing decision making processes, constructing decision tables, applying expected value criteria, and accounting for risk attitudes. It then outlines the typical steps in decision making, such as listing alternatives and possible outcomes. Key decision making criteria are defined, like expected monetary value, expected opportunity loss, and value of perfect information. Examples are provided to demonstrate how to apply these concepts to make optimal decisions under uncertainty.
This document provides an overview of decision making techniques covered in Chapter 17. It begins by listing the learning objectives, which are to use payoff tables, decision trees, and criteria to evaluate alternative courses of action. It then outlines the steps in decision making, which include listing alternatives and uncertain events, determining payoffs, and adopting evaluation criteria. Several decision making criteria are introduced, including maximax, maximin, expected monetary value, expected opportunity loss, value of perfect information, and return-to-risk ratio. Payoff tables and decision trees are presented as methods for displaying decision problems. The chapter concludes by discussing how sample information can be used to revise old probabilities when making decisions.
This document outlines concepts related to statistical decision theory. It begins by listing the chapter goals, which are to describe decision making, expected monetary value criterion, and utility theory. It then describes the basic steps in decision making as listing actions and possible outcomes, determining payoffs, and adopting decision criteria. Two methods for organizing this information are presented: payoff tables and decision trees. The document goes on to discuss different decision criteria that can be used depending on whether or not probabilities are known, including maximin, minimax regret, and expected monetary value. Examples are provided to illustrate how to apply these concepts and criteria when evaluating decision problems.
The document describes multiple regression analysis and its applications in business decision making. It explains that multiple regression allows examination of the linear relationship between one dependent variable and two or more independent variables. The chapter goals are to help readers apply and interpret multiple regression, perform residual analysis, and test significance of variables. An example of using price and advertising spending to predict pie sales is provided to illustrate multiple regression concepts.
This document outlines the key concepts and methods for decision making under uncertainty, risk, and certainty. It discusses tools like decision trees and tables that can be used to evaluate alternatives and choose the best option. Specific techniques covered include expected monetary value, maximax/maximin, and expected value of perfect information. Examples are provided to demonstrate how to use decision trees to model sequential or complex decisions and calculate the expected monetary value at each decision node. The document is intended to teach students the fundamentals and applications of quantitative decision making.
This document provides an overview of decision analysis and decision making under certainty and uncertainty. It describes decision environments like certainty, where outcomes are known, and uncertainty, where outcomes are unknown. It also defines decision criteria for nonprobabilistic decisions, where probabilities are unknown, and probabilistic decisions, which consider probabilities. Examples are given of decision criteria like expected value, maximax, maximin and minimax regret. Payoff tables, opportunity loss tables, and decision trees are used to demonstrate the application of these decision criteria.
This chapter discusses important discrete probability distributions used in business statistics. It introduces discrete random variables and their probability distributions. It defines the binomial distribution and explains how to calculate probabilities using the binomial formula. Examples are provided to demonstrate calculating the mean, variance, and covariance of discrete random variables, as well as the expected value and risk of investment portfolios. Counting techniques like combinations are also discussed for calculating binomial probabilities.
This chapter discusses important discrete probability distributions used in statistics. It begins with an introduction to discrete random variables and probability distributions. It then covers the key concepts of mean, variance, standard deviation, and covariance for discrete distributions. The chapter focuses on explaining the binomial, hypergeometric, and Poisson distributions and how to calculate probabilities using them. It concludes with examples of how to apply these distributions to areas like finance.
The document summarizes key points about multiple regression analysis from the chapter. It discusses applying multiple regression to business problems, interpreting regression output, performing residual analysis, and testing significance. Graphs and equations are provided to illustrate multiple regression concepts like predicting outcomes, determining variation explained, and checking assumptions.
Some Important Discrete Probability DistributionsYesica Adicondro
The chapter discusses important discrete probability distributions used in statistics for managers. It covers the binomial, hypergeometric, and Poisson distributions. The binomial distribution describes the number of successes in a fixed number of trials when the probability of success is constant. It has applications in areas like manufacturing and marketing. The key characteristics of the binomial distribution are its mean, variance, and standard deviation. Examples are provided to demonstrate how to calculate probabilities and characteristics of the binomial distribution. Tables can also be used to find binomial probabilities.
Assignment 2 lasa 1 the costs of productioncoursesexams1
Joseph Farms, Inc. is a small agricultural firm that has provided limited cost data to determine the profit maximizing output level. You are asked to complete two tables analyzing the cost and revenue data for output levels from 1 to 10 units. Table 1 requires calculating total, average, and marginal costs and revenues. Table 2 calculates total revenue, profit, and loss for each output level. You must also explain the marginal cost equals marginal revenue rule, identify the profit maximizing output, and explain why a purely competitive firm is a price taker.
This document discusses the use of simulation modeling techniques like Monte Carlo simulation to analyze probabilistic problems and decision-making. It provides examples of using random numbers in Excel to simulate demand for laptops over multiple weeks and analyze the impact of ordering policies on revenues and shortages over time. Simulation can be used in areas like production, inventory, logistics, and services to model complex real-world systems with uncertain variables.
This chapter discusses time-series forecasting and index numbers. It aims to develop basic forecasting models using smoothing methods like moving averages and exponential smoothing. It also covers trend-based forecasting using linear and nonlinear regression models. Time-series data contains trend, seasonal, cyclical, and irregular components that must be accounted for. Forecasting future values involves identifying patterns in historical data and extending those patterns into the future.
This document outlines the key goals and concepts covered in Chapter 6 of the textbook "Statistics for Managers Using Microsoft Excel". The chapter introduces continuous probability distributions, including the normal, uniform, and exponential distributions. It describes the characteristics of the normal distribution and how to translate problems into standardized normal distribution problems. The chapter also covers sampling distributions, the central limit theorem, and how to find probabilities using the normal distribution table.
To simulate is to try to duplicate the features, appearance and characteristics of a real system.
The idea behind simulation is to imitate a real-world situation mathematically, to study its properties and operating characteristics, to draw conclusions and make action decisions based on the results of the simulation.
The real-life system is not touched until the advantages and disadvantages of what may be a major policy decision are first measured on the system's model.
This chapter discusses statistical decision theory and making decisions under uncertainty. It covers constructing payoff tables and decision trees to list possible actions and outcomes. It introduces several decision criteria for selecting the best action, including maximin, minimax regret, and expected monetary value. Expected monetary value calculates the weighted average payoff using the probabilities of different outcomes. Bayes' theorem allows revising probabilities based on new sample information. The key goals are describing decision making processes and criteria for evaluating options under uncertainty.
Complete Table-1 (Joseph Farms, Inc., Cost and Revenue Data), either.docxluellaj
Complete Table-1 (Joseph Farms, Inc., Cost and Revenue Data), either as a Microsoft Excel spreadsheet, or as a Microsoft Word table. Assume that the price is $165 and the fixed costs are $125, at an output level of 1. Also assume that the data represents a firm in pure competition. Show your calculations in summary form.
What is the MC=MR Rule? To what market structures does this Rule apply? Explain your answers.
Using Microsoft Excel, graph the data in Columns 9 and 10.
What is the profit maximizing (or loss minimizing) output for this firm? Is there an economic profit? Explain your answers.
Explain why a firm in pure competition is considered to be a “price taker.”
Using the data in Table-1 (Joseph Farms, Inc., Cost and Revenue Data), complete Table-2 (Joseph Farms, Inc., Revenue/Profit/Loss Data), either as a Microsoft Excel spreadsheet, or as a Microsoft Word table. Show your calculations in summary form.
Using the data in Table-2 (Joseph Farms, Inc., Revenue/Profit/Loss Data), what is the break even output level for this firm? If this firm is in pure competition, at what output level would they operate? Show your calculations in summary form
Table-1: Joseph Farms, Inc., Cost and Revenue Data
Column 1
Column 2
Column 3
Column 4
Column 5
Column 6
Column 7
Column 8
Column 9
Column 10
Column 11
Output
Price per unit
Total Fixed Cost
Total Variable Cost
Total Cost
Average Fixed Cost
Average Variable Cost
Average Total Cost
Marginal
Marginal Revenue
Total Revenue
Level
Cost
0
$ -
NA
1
$ 113.00
2
$ 213.00
3
$ 300.00
4
$ 375.00
5
$ 463.00
6
$ 563.00
7
$ 675.00
8
$ 813.00
Complete Table-1 (Joseph Farms, Inc., Cost and Revenue Data), either as a Microsoft Excel spreadsheet, or as a Microsoft Word table. Assume that the price is $165 and the fixed costs are $125, at an output level of 1. Also assume that the data represents a firm in pure competition. Show your calculations in summary form.
What is the MC=MR Rule? To what market structures does this Rule apply? Ex.
-
BAFN 305 - Multiple Regression Questions
The attached provides descriptive statistics, correlations and a multiple regression run for four variables.
The four variables are about corporate earnings, debt, sales and ownership. Sample size is 400
companies. You are provided means, standard deviations, correlations and two regression models, a full
and reduced model.
The variables are: Earnings per share (EPS) - the 'Y'variable-\,
Company Debt, measured in millions of dollars,
Annual Sales, measured in millions of US dollars, and
Public or private ownership, a binary of (1,0) with 1 assigned to public.
Answer the following:
L. What percent of the companies are private. How many is that
2. The best predictor of EPS is
3. The weakest predictor of EPS is
4. ldentify the independent variable(s) understudy
5. A multiple regression model for EPS = f(Debt Sales,Ownership] was run and is noted on the
attached. Answer the following:
a. Coefficient of determination is
b.TestthehypothesisthatHop=0'AIphaat.05(AcceptorReject}-
a. State the critical value
b. State the computed value
c. State the p-value
d. State the decision
e. State the conclusion.
c. State the regression equation to forecast EPS.
d. Test the hypothesis to determine the importance of each variable.
a. State the critical value for the test - Alpha at .01 TT
b. State the computed value and the p-value for each
c. State the decisions.
d. State the conclusions.
6. lf you completed the above, you evaluated the 'Full Model'
a. Would you create a reduced model based on the analysis above.
b. lf so, which variables would be kept for the reduced model.
c. lf you ran the reduced model, why did you remove the variable, multicollinearity or lack
of a relationship with 'Y'.
d. Would the model provide good prediction of EPS._ Why
7
Full Model - Earnings = f(Debt,Sales,Ownership)
Summary
R-Square .75
Standard error .75
Cases 400
Reduced Model - Earnings = f(Debt, Sales)
Summary
R-Square .71
Standard error .80
Cases 400
Earnings (per sh) Debt Sales Public/Private(1,0
Means 3.00 20 (million) 30 (million) .65
Standard Deviations 1.50 8 (million) 10 (million) .30
Correlations Earnings Debt Sales Public/Private(t,0)
Earninss 1.00
Debt -.58 1.00
Sales 0.40 0.40 1.00
Public/Private (1,0) 0.30 =.25 o.41 1.00
ANOVA Sum of squares Df Mean Square F p-value
Regression 7,500 3 2.s00 396.20 .000
Residual 2.s00 396 6.31
Total 10,000 399
Variable Coefficient Standard Error t/z statistic p-value
lntercept 1.25
Debt -.10 xxxxxxxx -2.70 .006
Sales 0.15 xxxxxxxx L.45 .090
Public/Private 0.08 xxxxxxxx L.40 .096
ANOVA Sum of squares Df Mean Square F p-value
Reeression 7,too 2 3s50 486.30 .000
Residual 2,9OO 397 7.30
Total 10,000 399
Variable Coefficients Standard error t/z statistic
lntercept 1.85
Debt -.L4 xxxxxxxx -2.90 .003
Sales .20 xxxxxxxx 2.00 ,o23
Comparative Analysis
Xxxxxxxx Yyyyyyyyy
ITM 619
xx/xx/xxxx
Dr. Webb
waelalturki
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waelalturki ...
This document outlines concepts related to decision making under uncertainty and risk. It discusses six steps to decision making, including defining the problem, listing alternatives and outcomes, and identifying payoffs. It then covers various decision making models for uncertainty, like maximax, maximin, and expected monetary value. Sensitivity analysis is introduced as a way to examine how decisions may change with different input data. The document uses examples and tables to illustrate key concepts in decision analysis.
Konsep Balanced Score Card. Penilaian kinerja dilihat dari 4 perspektif yaitu perspektif keuangan, konsumen, learn and growth dan proses bisnis internal.
Dokumen tersebut membahas prospek industri taksi di Jakarta yang padat. Bakri mempertimbangkan untuk memasuki bisnis ini namun perlu menganalisis apakah dapat bersaing, bagaimana memulai usaha, kerja sama dengan investor dan mekanisme kerja sama dengan supir taksi.
Bakri berencana memasuki bisnis taksi di Jakarta. Analisis lingkungan eksternal menunjukkan prospek bisnis taksi masih baik meskipun persaingan ketat. Bakri perlu strategi yang tepat untuk bersaing di pasar yang padat.
Makalah Analisis PT Kereta API Indonesia . membahas analisis strategik dalam perusahaan kereta api, dimana dampak peraturan harga pesawat tidak ada penetapan batas bawah maka kereta api berdampak.
Makalah Analisis PT Kereta API Indonesia . membahas analisis strategik dalam perusahaan kereta api, dimana dampak peraturan harga pesawat tidak ada penetapan batas bawah maka kereta api berdampak.
Dmfi booklet indonesian. isi petisi nya yah jangan lupa klik www.dogmeatfreeindonesia.org
tidak sampai 1 menit isi petisi ini agar indonesia bebas dari daging anjing, anjing layak diperlakukan layak dan lebih baik.
tolong ya teman - teman
Dokumen tersebut membahas risiko kesehatan dan kesejahteraan hewan yang ditimbulkan oleh perdagangan daging anjing di Indonesia, termasuk penyebaran penyakit rabies, penderitaan hewan, dan kerentanan kelompok tertentu terhadap penyakit. Beberapa organisasi berkomitmen untuk meningkatkan kesadaran masyarakat dan mendorong pemerintah mengakhiri praktik ini.
BPR adalah merancang ulang radikal sistem bisnis untuk meningkatkan kinerja kritis seperti biaya, kualitas, layanan dan kecepatan. Faktor keberhasilan BPR meliputi visi, keterampilan, insentif, sumber daya, dan rencana aksi. Hasil yang diharapkan dari BPR adalah perbaikan proses hingga 100% dan pengurangan biaya secara drastis.
Makalah ini membahas tentang Business Process Reengineering (BPR), termasuk definisi, pihak yang terlibat, tahapan pelaksanaannya, dan faktor-faktor keberhasilannya. BPR merupakan perancangan ulang mendasar dan radikal sistem bisnis untuk meningkatkan kinerja perusahaan secara signifikan."
Balanced Scorecard (BSC) adalah sistem pengelolaan strategis yang menggabungkan ukuran-ukuran keuangan dan nonkeuangan untuk menyelaraskan strategi perusahaan. BSC memiliki empat perspektif yaitu keuangan, pelanggan, proses internal, dan pembelajaran & pertumbuhan. Langkah-langkah penyusunan BSC meliputi penetapan masalah, indikator kinerja utama, pengukuran KPI, dan pembuatan peta strategi.
Makalah ini membahas tentang Balanced Scorecard, yaitu sistem pengukuran kinerja yang mempertimbangkan empat perspektif yaitu keuangan, pelanggan, proses bisnis internal, dan pembelajaran dan pertumbuhan. Balanced Scorecard dikembangkan untuk mengurangi kelemahan pengukuran kinerja konvensional yang hanya berfokus pada aspek keuangan."
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014