This document contains 56 multiple choice questions about forecasting methods from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as types of forecasts, time-series forecasting models, measures of forecast accuracy, and exponential smoothing. Correct answers are provided for each question along with the difficulty level and topic area.
This document contains 56 multiple choice questions about forecasting methods from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as types of forecasts, time-series forecasting models, measures of forecast accuracy, and exponential smoothing. Correct answers are provided for each question along with the difficulty level and topic area.
This document contains 54 multiple choice questions about probability concepts from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as fundamental probability concepts, mutually exclusive and collectively exhaustive events, statistically independent events, probability distributions including binomial and normal distributions, and Bayes' theorem. For each question, the answer and difficulty level is provided along with the topic area.
Quantitative Analysis For Management 11th Edition Render Test BankRichmondere
Full download : http://alibabadownload.com/product/quantitative-analysis-for-management-11th-edition-render-test-bank/ Quantitative Analysis For Management 11th Edition Render Test Bank
This document provides teaching suggestions for regression models:
1) It suggests emphasizing the difference between independent and dependent variables in a regression model using examples.
2) It notes that correlation does not necessarily imply causation and gives an example of variables that are correlated but changing one does not affect the other.
3) It recommends having students manually draw regression lines through data points to appreciate the least squares criterion.
4) It advises selecting random data values to generate a regression line in Excel to demonstrate determining the coefficient of determination and F-test.
5) It suggests discussing the full and shortcut regression formulas to provide a better understanding of the concepts.
Transportation and assignment models are network flow problems that can be solved using linear programming. Transportation models are used to determine optimal shipping routes from sources to destinations to minimize costs. Assignment models are used to efficiently match people or tasks based on costs or times. Both models involve distributing goods or assigning tasks from multiple sources to multiple destinations, with the goal of minimizing total costs or time.
This document contains 54 multiple choice questions assessing knowledge of decision analysis concepts from Quantitative Analysis for Management, 11e by Render. The questions cover topics such as expected monetary value, decision making under risk and uncertainty, decision criteria like maximax and maximin, decision trees, utility theory, and calculating expected value with and without perfect information. Correct answers are provided for each question.
The Sample Average Approximation Method for Stochastic Programs with Integer ...SSA KPI
The document describes a sample average approximation method for solving stochastic programs with integer recourse. It approximates the expected recourse cost function using a sample average based on a sample of scenarios. It shows that as the sample size increases, the solution to the sample average approximation problem converges exponentially fast to the optimal solution of the true stochastic program. It also describes statistical and deterministic techniques for validating candidate solutions. Preliminary computational results applying this method are also mentioned.
This document contains information about simulation modeling from the textbook Quantitative Analysis for Management. It includes definitions, advantages and disadvantages of simulation, different types of simulation like Monte Carlo simulation, and examples of using simulation to model situations with probabilistic variables like customer demand, arrivals, service times and machine breakdowns. Random numbers are used with probability distributions to simulate outcomes over multiple runs and analyze the results.
This document contains 56 multiple choice questions about forecasting methods from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as types of forecasts, time-series forecasting models, measures of forecast accuracy, and exponential smoothing. Correct answers are provided for each question along with the difficulty level and topic area.
This document contains 54 multiple choice questions about probability concepts from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as fundamental probability concepts, mutually exclusive and collectively exhaustive events, statistically independent events, probability distributions including binomial and normal distributions, and Bayes' theorem. For each question, the answer and difficulty level is provided along with the topic area.
Quantitative Analysis For Management 11th Edition Render Test BankRichmondere
Full download : http://alibabadownload.com/product/quantitative-analysis-for-management-11th-edition-render-test-bank/ Quantitative Analysis For Management 11th Edition Render Test Bank
This document provides teaching suggestions for regression models:
1) It suggests emphasizing the difference between independent and dependent variables in a regression model using examples.
2) It notes that correlation does not necessarily imply causation and gives an example of variables that are correlated but changing one does not affect the other.
3) It recommends having students manually draw regression lines through data points to appreciate the least squares criterion.
4) It advises selecting random data values to generate a regression line in Excel to demonstrate determining the coefficient of determination and F-test.
5) It suggests discussing the full and shortcut regression formulas to provide a better understanding of the concepts.
Transportation and assignment models are network flow problems that can be solved using linear programming. Transportation models are used to determine optimal shipping routes from sources to destinations to minimize costs. Assignment models are used to efficiently match people or tasks based on costs or times. Both models involve distributing goods or assigning tasks from multiple sources to multiple destinations, with the goal of minimizing total costs or time.
This document contains 54 multiple choice questions assessing knowledge of decision analysis concepts from Quantitative Analysis for Management, 11e by Render. The questions cover topics such as expected monetary value, decision making under risk and uncertainty, decision criteria like maximax and maximin, decision trees, utility theory, and calculating expected value with and without perfect information. Correct answers are provided for each question.
The Sample Average Approximation Method for Stochastic Programs with Integer ...SSA KPI
The document describes a sample average approximation method for solving stochastic programs with integer recourse. It approximates the expected recourse cost function using a sample average based on a sample of scenarios. It shows that as the sample size increases, the solution to the sample average approximation problem converges exponentially fast to the optimal solution of the true stochastic program. It also describes statistical and deterministic techniques for validating candidate solutions. Preliminary computational results applying this method are also mentioned.
This document contains information about simulation modeling from the textbook Quantitative Analysis for Management. It includes definitions, advantages and disadvantages of simulation, different types of simulation like Monte Carlo simulation, and examples of using simulation to model situations with probabilistic variables like customer demand, arrivals, service times and machine breakdowns. Random numbers are used with probability distributions to simulate outcomes over multiple runs and analyze the results.
This document contains 71 multiple choice questions about regression analysis from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as simple and multiple linear regression, assumptions of regression models, measuring model fit, and testing models for significance. Correct answers are provided along with a difficulty rating and topic for each question.
This document contains 44 multiple choice questions assessing knowledge of statistical process control and quality management concepts from a quantitative analysis textbook chapter on statistical quality control. The questions cover topics such as the history and definitions of quality management; statistical process control methods including control charts for variables and attributes; calculating control limits for x-bar, p, and R charts; and applying quality control principles to various processes and case studies.
The document discusses quantitative analysis and business analytics. It defines quantitative analysis as the scientific approach to managerial decision making. The quantitative analysis approach involves 7 steps: defining the problem, developing a model, acquiring input data, developing a solution, testing the solution, analyzing the results, and implementing the results. It also discusses the three categories of business analytics: descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics involves studying historical data, predictive analytics involves forecasting future outcomes, and prescriptive analytics uses optimization methods.
This document contains 36 multiple choice questions about queuing theory and waiting line models. It covers topics like the characteristics of queuing systems, different types of queuing models (M/M/1, M/D/1, etc.), assumptions of queuing models, and using queuing theory to analyze real world systems. Several questions also provide word problems to test the application of queuing concepts to calculate metrics like average queue length and server utilization. The questions assess understanding of key queuing theory terminology, assumptions, models, and calculations.
The document contains a chapter about project management tools PERT and CPM from a quantitative analysis textbook. It includes 58 multiple choice questions about key concepts related to PERT and CPM, such as their origins and uses, time estimates, critical paths, variances, and probability calculations. The questions test understanding of network diagrams, activity dependencies, slack times, and using PERT and CPM to schedule and manage complex projects.
This document contains a chapter summary for a quantitative analysis textbook. It includes 54 multiple choice questions covering topics related to linear programming models, including graphical and computer solution methods. Key topics assessed include formulating linear programming problems, the requirements and assumptions of linear programs, graphical solutions, special cases like infeasibility and redundancy, and sensitivity analysis.
This document provides examples of statistical concepts and calculations related to business statistics exercises covering weeks 36-50. It includes 3 examples:
1) Calculating probabilities using the normal distribution for scenarios involving a company's vending machine electricity consumption.
2) Hypothesis testing examples comparing a sample mean to a hypothesized population mean using z-tests and t-tests.
3) An example calculating the chi-squared test statistic to test if a sample variance matches a hypothesized population variance.
This document contains 52 multiple choice questions about inventory control models from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as single-period inventory models, economic order quantity, reorder point, safety stock, ABC analysis, just-in-time inventory, and enterprise resource planning. The correct answers to the questions are also provided.
This document discusses point and interval estimation. It defines an estimator as a function used to infer an unknown population parameter based on sample data. Point estimation provides a single value, while interval estimation provides a range of values with a certain confidence level, such as 95%. Common point estimators include the sample mean and proportion. Interval estimators account for variability in samples and provide more information than point estimators. The document provides examples of how to construct confidence intervals using point estimates, confidence levels, and standard errors or deviations.
This document provides an overview of confidence intervals. It discusses calculating confidence intervals for a population mean using the normal and student's t distributions. The key steps are presented: determining the point estimate, finding the appropriate z-score for the confidence level, calculating the margin of error using the z-score and standard error, and stating the confidence interval. An example is also provided to demonstrate calculating a 90% confidence interval for a population mean when the standard deviation is known.
The document provides an overview of quantitative analysis and the quantitative analysis approach. It discusses key concepts like the steps in the quantitative analysis approach, which include defining the problem, developing a model, acquiring input data, developing a solution, testing the solution, analyzing results, and implementing results. It also discusses mathematical models, decision variables, parameters, algorithms, and sensitivity analysis. Potential problems in quantitative analysis are outlined, such as conflicting viewpoints, poor assumptions, and inaccurate data.
This document discusses interval estimation for proportions. It defines point estimates and interval estimates. A point estimate is a single value of a statistic used to estimate a population parameter, like the sample proportion p estimating the population proportion P. An interval estimate provides a range of values between which the population parameter is expected to lie with a certain confidence level, like a 95% confidence interval for a proportion. Two examples are provided to demonstrate how to calculate a confidence interval for a sample proportion and interpret whether it supports or contradicts a claimed population proportion.
This document contains 52 multiple choice questions about network modeling techniques including minimal spanning tree, maximal flow, and shortest route problems. The questions test understanding of applying these techniques to determine the minimum distance to connect all nodes, maximum flow through a network, and shortest path between nodes. The techniques and their applications to problems like transportation, infrastructure, and resource allocation are also assessed.
Quite often in experimental work, many situations arise where some observations are lost or become
unavailable due to some accidents or cost constraints. When there are missing observations, some
desirable design properties like orthogonality,rotatability and optimality can be adversely affected. Some
attention has been given, in literature, to investigating the prediction capability of response surface
designs; however, little or no effort has been devoted to investigating same for such designs when some
observations are missing. This work therefore investigates the impact of a single missing observation of the
various design points: factorial, axial and center points, on the estimation and predictive capability of
Central Composite Designs (CCDs). It was observed that for each of the designs considered, precision of
model parameter estimates and the design prediction properties were adversely affected by the missing
observations and that the largest loss in precision of parameters corresponds to a missing factorial point.
This document provides 10 teaching suggestions for instructors to help students better understand key concepts in decision analysis. Suggestions include having students describe personal decisions they made and which steps of the decision-making process they used; role playing to define problems and alternatives; discussing types of decisions under certainty, risk, and uncertainty; and using decision trees and Bayesian analysis to solve problems. The goal is for students to recognize how decision theory applies to important real-life decisions. Alternative examples provided apply concepts like expected monetary value to problems involving purchasing industrial robots.
- Point estimation involves using sample data to calculate a single number (point estimate) that estimates an unknown population parameter.
- A point estimator is a statistic used to calculate the point estimate. For example, when estimating an unknown population mean μ, the sample mean x̅ is a point estimator for μ.
- An unbiased estimator has an expected value equal to the true population parameter value. A biased estimator has an expected value that is not equal to the true parameter value.
- Common methods for finding estimators include maximum likelihood estimation and the method of moments. Maximum likelihood estimation identifies the value of the parameter that maximizes the likelihood function based on the sample data. The method of moments equates sample moments
This document provides an overview of key concepts from chapters 5 and 6 of an introductory statistics textbook. It discusses continuous probability distributions and their properties, including the uniform and exponential distributions. It then focuses on the normal distribution and standard normal distribution, explaining how to calculate z-scores and use the empirical rule. Examples are provided for calculating probabilities using the normal distribution. The summary aims to introduce students to important concepts involving continuous random variables and the normal distribution.
Common evaluation measures in NLP and IRRushdi Shams
This document discusses various evaluation measures used in information retrieval and natural language processing. It describes precision, recall, and the F1 score as fundamental measures for unranked retrieval sets. It also covers averaged precision and recall, accuracy, novelty and coverage ratios. For ranked retrieval sets, it discusses recall-precision graphs, interpolated recall-precision, precision at k, R-precision, ROC curves, and normalized discounted cumulative gain (NDCG). The document also discusses agreement measures like Kappa statistics and parses evaluation measures like Parseval and attachment scores.
This document provides an overview of PAC (Probably Approximately Correct) learning theory. It discusses how PAC learning relates the probability of successful learning to the number of training examples, complexity of the hypothesis space, and accuracy of approximating the target function. Key concepts explained include training error vs true error, overfitting, the VC dimension as a measure of hypothesis space complexity, and how PAC learning bounds can be derived for finite and infinite hypothesis spaces based on factors like the training size and VC dimension.
The document discusses quantitative analysis and the quantitative analysis approach. It provides examples of true/false questions related to key concepts in quantitative analysis, such as the steps in the quantitative analysis approach being defining the problem, developing a model, acquiring input data, developing a solution, testing the solution, analyzing results, and implementing results. Other concepts covered include decision variables, parameters, algorithms, sensitivity analysis, and problems that may arise in quantitative analysis.
This document contains 71 multiple choice questions about regression analysis from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as simple and multiple linear regression, assumptions of regression models, measuring model fit, and testing models for significance. Correct answers are provided along with a difficulty rating and topic for each question.
This document contains 71 multiple choice questions about regression analysis from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as simple and multiple linear regression, assumptions of regression models, measuring model fit, and testing models for significance. Correct answers are provided along with a difficulty rating and topic for each question.
This document contains 44 multiple choice questions assessing knowledge of statistical process control and quality management concepts from a quantitative analysis textbook chapter on statistical quality control. The questions cover topics such as the history and definitions of quality management; statistical process control methods including control charts for variables and attributes; calculating control limits for x-bar, p, and R charts; and applying quality control principles to various processes and case studies.
The document discusses quantitative analysis and business analytics. It defines quantitative analysis as the scientific approach to managerial decision making. The quantitative analysis approach involves 7 steps: defining the problem, developing a model, acquiring input data, developing a solution, testing the solution, analyzing the results, and implementing the results. It also discusses the three categories of business analytics: descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics involves studying historical data, predictive analytics involves forecasting future outcomes, and prescriptive analytics uses optimization methods.
This document contains 36 multiple choice questions about queuing theory and waiting line models. It covers topics like the characteristics of queuing systems, different types of queuing models (M/M/1, M/D/1, etc.), assumptions of queuing models, and using queuing theory to analyze real world systems. Several questions also provide word problems to test the application of queuing concepts to calculate metrics like average queue length and server utilization. The questions assess understanding of key queuing theory terminology, assumptions, models, and calculations.
The document contains a chapter about project management tools PERT and CPM from a quantitative analysis textbook. It includes 58 multiple choice questions about key concepts related to PERT and CPM, such as their origins and uses, time estimates, critical paths, variances, and probability calculations. The questions test understanding of network diagrams, activity dependencies, slack times, and using PERT and CPM to schedule and manage complex projects.
This document contains a chapter summary for a quantitative analysis textbook. It includes 54 multiple choice questions covering topics related to linear programming models, including graphical and computer solution methods. Key topics assessed include formulating linear programming problems, the requirements and assumptions of linear programs, graphical solutions, special cases like infeasibility and redundancy, and sensitivity analysis.
This document provides examples of statistical concepts and calculations related to business statistics exercises covering weeks 36-50. It includes 3 examples:
1) Calculating probabilities using the normal distribution for scenarios involving a company's vending machine electricity consumption.
2) Hypothesis testing examples comparing a sample mean to a hypothesized population mean using z-tests and t-tests.
3) An example calculating the chi-squared test statistic to test if a sample variance matches a hypothesized population variance.
This document contains 52 multiple choice questions about inventory control models from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as single-period inventory models, economic order quantity, reorder point, safety stock, ABC analysis, just-in-time inventory, and enterprise resource planning. The correct answers to the questions are also provided.
This document discusses point and interval estimation. It defines an estimator as a function used to infer an unknown population parameter based on sample data. Point estimation provides a single value, while interval estimation provides a range of values with a certain confidence level, such as 95%. Common point estimators include the sample mean and proportion. Interval estimators account for variability in samples and provide more information than point estimators. The document provides examples of how to construct confidence intervals using point estimates, confidence levels, and standard errors or deviations.
This document provides an overview of confidence intervals. It discusses calculating confidence intervals for a population mean using the normal and student's t distributions. The key steps are presented: determining the point estimate, finding the appropriate z-score for the confidence level, calculating the margin of error using the z-score and standard error, and stating the confidence interval. An example is also provided to demonstrate calculating a 90% confidence interval for a population mean when the standard deviation is known.
The document provides an overview of quantitative analysis and the quantitative analysis approach. It discusses key concepts like the steps in the quantitative analysis approach, which include defining the problem, developing a model, acquiring input data, developing a solution, testing the solution, analyzing results, and implementing results. It also discusses mathematical models, decision variables, parameters, algorithms, and sensitivity analysis. Potential problems in quantitative analysis are outlined, such as conflicting viewpoints, poor assumptions, and inaccurate data.
This document discusses interval estimation for proportions. It defines point estimates and interval estimates. A point estimate is a single value of a statistic used to estimate a population parameter, like the sample proportion p estimating the population proportion P. An interval estimate provides a range of values between which the population parameter is expected to lie with a certain confidence level, like a 95% confidence interval for a proportion. Two examples are provided to demonstrate how to calculate a confidence interval for a sample proportion and interpret whether it supports or contradicts a claimed population proportion.
This document contains 52 multiple choice questions about network modeling techniques including minimal spanning tree, maximal flow, and shortest route problems. The questions test understanding of applying these techniques to determine the minimum distance to connect all nodes, maximum flow through a network, and shortest path between nodes. The techniques and their applications to problems like transportation, infrastructure, and resource allocation are also assessed.
Quite often in experimental work, many situations arise where some observations are lost or become
unavailable due to some accidents or cost constraints. When there are missing observations, some
desirable design properties like orthogonality,rotatability and optimality can be adversely affected. Some
attention has been given, in literature, to investigating the prediction capability of response surface
designs; however, little or no effort has been devoted to investigating same for such designs when some
observations are missing. This work therefore investigates the impact of a single missing observation of the
various design points: factorial, axial and center points, on the estimation and predictive capability of
Central Composite Designs (CCDs). It was observed that for each of the designs considered, precision of
model parameter estimates and the design prediction properties were adversely affected by the missing
observations and that the largest loss in precision of parameters corresponds to a missing factorial point.
This document provides 10 teaching suggestions for instructors to help students better understand key concepts in decision analysis. Suggestions include having students describe personal decisions they made and which steps of the decision-making process they used; role playing to define problems and alternatives; discussing types of decisions under certainty, risk, and uncertainty; and using decision trees and Bayesian analysis to solve problems. The goal is for students to recognize how decision theory applies to important real-life decisions. Alternative examples provided apply concepts like expected monetary value to problems involving purchasing industrial robots.
- Point estimation involves using sample data to calculate a single number (point estimate) that estimates an unknown population parameter.
- A point estimator is a statistic used to calculate the point estimate. For example, when estimating an unknown population mean μ, the sample mean x̅ is a point estimator for μ.
- An unbiased estimator has an expected value equal to the true population parameter value. A biased estimator has an expected value that is not equal to the true parameter value.
- Common methods for finding estimators include maximum likelihood estimation and the method of moments. Maximum likelihood estimation identifies the value of the parameter that maximizes the likelihood function based on the sample data. The method of moments equates sample moments
This document provides an overview of key concepts from chapters 5 and 6 of an introductory statistics textbook. It discusses continuous probability distributions and their properties, including the uniform and exponential distributions. It then focuses on the normal distribution and standard normal distribution, explaining how to calculate z-scores and use the empirical rule. Examples are provided for calculating probabilities using the normal distribution. The summary aims to introduce students to important concepts involving continuous random variables and the normal distribution.
Common evaluation measures in NLP and IRRushdi Shams
This document discusses various evaluation measures used in information retrieval and natural language processing. It describes precision, recall, and the F1 score as fundamental measures for unranked retrieval sets. It also covers averaged precision and recall, accuracy, novelty and coverage ratios. For ranked retrieval sets, it discusses recall-precision graphs, interpolated recall-precision, precision at k, R-precision, ROC curves, and normalized discounted cumulative gain (NDCG). The document also discusses agreement measures like Kappa statistics and parses evaluation measures like Parseval and attachment scores.
This document provides an overview of PAC (Probably Approximately Correct) learning theory. It discusses how PAC learning relates the probability of successful learning to the number of training examples, complexity of the hypothesis space, and accuracy of approximating the target function. Key concepts explained include training error vs true error, overfitting, the VC dimension as a measure of hypothesis space complexity, and how PAC learning bounds can be derived for finite and infinite hypothesis spaces based on factors like the training size and VC dimension.
The document discusses quantitative analysis and the quantitative analysis approach. It provides examples of true/false questions related to key concepts in quantitative analysis, such as the steps in the quantitative analysis approach being defining the problem, developing a model, acquiring input data, developing a solution, testing the solution, analyzing results, and implementing results. Other concepts covered include decision variables, parameters, algorithms, sensitivity analysis, and problems that may arise in quantitative analysis.
This document contains 71 multiple choice questions about regression analysis from the textbook "Quantitative Analysis for Management, 11e". The questions cover topics such as simple and multiple linear regression, assumptions of regression models, measuring model fit, and testing models for significance. Correct answers are provided along with a difficulty rating and topic for each question.
This document discusses various qualitative and quantitative forecasting methods including simple and weighted moving averages, exponential smoothing, and simple linear regression. It provides examples of how to calculate forecasts using each of these methods and evaluates forecast accuracy using metrics like MAD and tracking signal.
This document contains information about simulation modeling from the textbook Quantitative Analysis for Management. It includes definitions, advantages and disadvantages of simulation, different types of simulation like Monte Carlo simulation, and examples of using simulation to model situations with probabilistic variables like customer demand, arrivals, service times and machine breakdowns. Random numbers are used with probability distributions to simulate outcomes over multiple runs and analyze the results.
Quantitative Analysis For Management 13th Edition Render Test BankJescieer
This document contains 47 multiple choice questions about quantitative analysis and business analytics. It covers topics such as the quantitative analysis approach, business analytics categories, modeling, and developing quantitative analysis models. Several questions define key terms used in quantitative analysis like decision variables, parameters, algorithms, and sensitivity analysis. Other questions ask about the history and applications of quantitative analysis techniques.
The document summarizes various forecasting techniques used in industrial engineering and operations management. It discusses time series analysis, quantitative forecasting methods like moving average, weighted moving average, exponential smoothing and regression analysis. It also discusses qualitative forecasting techniques like market surveys and Delphi method. It provides examples of how to calculate forecasts using different quantitative techniques. Finally, it lists objective type questions related to forecasting from previous GATE and IES exams.
ForecastingBUS255 GoalsBy the end of this chapter, y.docxbudbarber38650
Forecasting
BUS255
Goals
By the end of this chapter, you should know:
Importance of Forecasting
Various Forecasting Techniques
Choosing a Forecasting Method
2
Forecasting
Forecasts are done to predict future events for planning
Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan
Forecasts are made on many different variables
Forecasts are important to managing both processes and managing supply chains
3
Key Decisions in Forecasting
Deciding what to forecast
Level of aggregation
Units of measurement
Choosing a forecasting system
Choosing a forecasting technique
4
5
Forecasting Techniques
Qualitative (Judgment) Methods
Sales force Estimates
Time-series Methods
Naïve Method
Causal Methods
Executive Opinion
Market Research
Delphi Method
Moving Averages
Exponential Smoothing
Regression Analysis
Qualitative (Judgment) methods
Salesforce estimates
Executive opinion
Market Research
The Delphi Method
Salesforce estimates: Forecasts derived from estimates provided by salesforce.
Executive opinion: Method in which opinions, experience, and technical knowledge of one or more managers are summarized to arrive at a single forecast.
Market research: A scientific study and analysis of data gathered from consumer surveys intended to learn consumer interest in a product or service.
Delphi method: A process of gaining consensus from a group of experts while maintaining their anonymity.
6
Case Study
Reference: Krajewski, Ritzman, Malhotra. (2010). Operations Management: Processes and Supply Chains, Ninth Edition. Pearson Prentice Hall. P. 42-43.
7
Case study questions
What information system is used by UNILEVER to manage forecasts?
What does UNILEVER do when statistical information is not useful for forecasting?
What types of qualitative methods are used by UNILEVER?
What were some suggestions provided to improve forecasting?
8
Causal methods – Linear Regression
A dependent variable is related to one or more independent variables by a linear equation
The independent variables are assumed to “cause” the results observed in the past
Simple linear regression model assumes a straight line relationship
9
Causal methods – Linear Regression
Y = a + bX
where
Y = dependent variable
X = independent variable
a = Y-intercept of the line
b = slope of the line
10
Causal methods – Linear Regression
Fit of the regression model
Coefficient of determination
Standard error of the estimate
Please go to in-class exercise sheet
Coefficient of determination: Also called r-squared. Measures the amount of variation in the dependent variable about its mean that is explained by the regression line. Range between 0 and 1. In general, larger values are better.
Standard error of the estimate: Measures how closely the data on the dependent variable cluster around the regression line. Smaller values are better.
11
Time Series
A time seri.
The document provides tips and techniques for data interpretation and approximation including reading questions carefully, analyzing data, paying attention to units, and learning to approximate and skim data. Examples demonstrate approximating values, identifying missing values in equations, and calculating averages, ratios, and using graphs including bar graphs, stacked graphs, tables, line graphs, and pie charts to organize and present data. Key concepts are defined for average, ratio, and different types of graphs. Sample questions are provided for practice interpreting various types of graphs.
1) The document describes a unit on repeated measures designs, including a review of standard repeated measures analyses using linear models and multi-level modeling, as well as an alternative approach.
2) Key features of repeated measures designs are discussed, such as having more than one observation per participant. Advantages and challenges like order effects are also reviewed.
3) Methods for analyzing repeated measures data using linear models by first transforming the data into wide format using differences and averages are described and compared to a multi-level modeling approach.
QRB 501 Final Exam Answers
QRB 501 Final Exam
1) Write the following as an algebraic expression using x as the variable:
Triple a number subtracted from the number
A. 3(x - x)
B. x 3 – x
C. 3x - x
D. x - 3x
2) Write the following as an algebraic expression using x as the variable: A
number decreased by 25 and multiplied by 4
A. x – 25 · 4
B. -25x · 4
C. 4x - 25
D. 4(x – 25)
3. Write the following as an algebraic expression using x as the variable: The
sum of a number and -8
A. -8 + x
B. -8 - x
C. x (-8)
D. -8x
4) Write the following as an algebraic expression using x as the variable:
Twelve less than six times a number
A. 12 – 6x
B. –6x
C. –12(6x)
D. 6x – 12
5) Solve: -3 – (-2 + 4) - 5
A. 15
B. 10
C. -6
D. -10
6) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
D. -.039
7) Solve: 3(32) – 8(9 – 2) ÷ 2
A. -14.5
B. 55
C. 66.5
D. -1
8) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
QRB 501 Final Exam Answers
QRB 501 Final Exam
1) Write the following as an algebraic expression using x as the variable:
Triple a number subtracted from the number
A. 3(x - x)
B. x 3 – x
C. 3x - x
D. x - 3x
2) Write the following as an algebraic expression using x as the variable: A
number decreased by 25 and multiplied by 4
A. x – 25 · 4
B. -25x · 4
C. 4x - 25
D. 4(x – 25)
3. Write the following as an algebraic expression using x as the variable: The
sum of a number and -8
A. -8 + x
B. -8 - x
C. x (-8)
D. -8x
4) Write the following as an algebraic expression using x as the variable:
Twelve less than six times a number
A. 12 – 6x
B. –6x
C. –12(6x)
D. 6x – 12
5) Solve: -3 – (-2 + 4) - 5
A. 15
B. 10
C. -6
D. -10
6) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
D. -.039
7) Solve: 3(32) – 8(9 – 2) ÷ 2
A. -14.5
B. 55
C. 66.5
D. -1
8) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
QRB 501 Final Exam Answers
QRB 501 Final Exam
1) Write the following as an algebraic expression using x as the variable:
Triple a number subtracted from the number
A. 3(x - x)
B. x 3 – x
C. 3x - x
D. x - 3x
2) Write the following as an algebraic expression using x as the variable: A
number decreased by 25 and multiplied by 4
A. x – 25 · 4
B. -25x · 4
C. 4x - 25
D. 4(x – 25)
3. Write the following as an algebraic expression using x as the variable: The
sum of a number and -8
A. -8 + x
B. -8 - x
C. x (-8)
D. -8x
4) Write the following as an algebraic expression using x as the variable:
Twelve less than six times a number
A. 12 – 6x
B. –6x
C. –12(6x)
D. 6x – 12
5) Solve: -3 – (-2 + 4) - 5
A. 15
B. 10
C. -6
D. -10
6) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
D. -.039
7) Solve: 3(32) – 8(9 – 2) ÷ 2
A. -14.5
B. 55
C. 66.5
D. -1
8) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
QRB 501 Final Exam Answers
QRB 501 Final Exam
1) Write the following as an algebraic expression using x as the variable:
Triple a number subtracted from the number
A. 3(x - x)
B. x 3 – x
C. 3x - x
D. x - 3x
2) Write the following as an algebraic expression using x as the variable: A
number decreased by 25 and multiplied by 4
A. x – 25 · 4
B. -25x · 4
C. 4x - 25
D. 4(x – 25)
3. Write the following as an algebraic expression using x as the variable: The
sum of a number and -8
A. -8 + x
B. -8 - x
C. x (-8)
D. -8x
4) Write the following as an algebraic expression using x as the variable:
Twelve less than six times a number
A. 12 – 6x
B. –6x
C. –12(6x)
D. 6x – 12
5) Solve: -3 – (-2 + 4) - 5
A. 15
B. 10
C. -6
D. -10
6) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
D. -.039
7) Solve: 3(32) – 8(9 – 2) ÷ 2
A. -14.5
B. 55
C. 66.5
D. -1
8) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
QRB 501 Final Exam Answers
QRB 501 Final Exam
1) Write the following as an algebraic expression using x as the variable:
Triple a number subtracted from the number
A. 3(x - x)
B. x 3 – x
C. 3x - x
D. x - 3x
2) Write the following as an algebraic expression using x as the variable: A
number decreased by 25 and multiplied by 4
A. x – 25 · 4
B. -25x · 4
C. 4x - 25
D. 4(x – 25)
3. Write the following as an algebraic expression using x as the variable: The
sum of a number and -8
A. -8 + x
B. -8 - x
C. x (-8)
D. -8x
4) Write the following as an algebraic expression using x as the variable:
Twelve less than six times a number
A. 12 – 6x
B. –6x
C. –12(6x)
D. 6x – 12
5) Solve: -3 – (-2 + 4) - 5
A. 15
B. 10
C. -6
D. -10
6) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
D. -.039
7) Solve: 3(32) – 8(9 – 2) ÷ 2
A. -14.5
B. 55
C. 66.5
D. -1
8) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
QRB 501 Final Exam Answers
QRB 501 Final Exam
1) Write the following as an algebraic expression using x as the variable:
Triple a number subtracted from the number
A. 3(x - x)
B. x 3 – x
C. 3x - x
D. x - 3x
2) Write the following as an algebraic expression using x as the variable: A
number decreased by 25 and multiplied by 4
A. x – 25 · 4
B. -25x · 4
C. 4x - 25
D. 4(x – 25)
3. Write the following as an algebraic expression using x as the variable: The
sum of a number and -8
A. -8 + x
B. -8 - x
C. x (-8)
D. -8x
4) Write the following as an algebraic expression using x as the variable:
Twelve less than six times a number
A. 12 – 6x
B. –6x
C. –12(6x)
D. 6x – 12
5) Solve: -3 – (-2 + 4) - 5
A. 15
B. 10
C. -6
D. -10
6) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
D. -.039
7) Solve: 3(32) – 8(9 – 2) ÷ 2
A. -14.5
B. 55
C. 66.5
D. -1
8) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
QRB 501 Final Exam Answers
QRB 501 Final Exam
1) Write the following as an algebraic expression using x as the variable:
Triple a number subtracted from the number
A. 3(x - x)
B. x 3 – x
C. 3x - x
D. x - 3x
2) Write the following as an algebraic expression using x as the variable: A
number decreased by 25 and multiplied by 4
A. x – 25 · 4
B. -25x · 4
C. 4x - 25
D. 4(x – 25)
3. Write the following as an algebraic expression using x as the variable: The
sum of a number and -8
A. -8 + x
B. -8 - x
C. x (-8)
D. -8x
4) Write the following as an algebraic expression using x as the variable:
Twelve less than six times a number
A. 12 – 6x
B. –6x
C. –12(6x)
D. 6x – 12
5) Solve: -3 – (-2 + 4) - 5
A. 15
B. 10
C. -6
D. -10
6) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
D. -.039
7) Solve: 3(32) – 8(9 – 2) ÷ 2
A. -14.5
B. 55
C. 66.5
D. -1
8) Solve: (–5)2 · (9 – 17)2 ÷ (–10)2
A. 16
B. 64
C. -6.4
This document contains a chapter summary for a quantitative analysis textbook. It includes 54 multiple choice questions covering topics related to linear programming models, including graphical and computer solution methods. Key topics assessed include formulating linear programming problems, the requirements and assumptions of linear programs, graphical solutions, special cases like infeasibility and redundancy, and sensitivity analysis.
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Dynamic programming is a technique for solving complex problems by breaking them down into simpler sub-problems. It involves storing solutions to sub-problems for later use, avoiding recomputing them. Examples where it can be applied include matrix chain multiplication and calculating Fibonacci numbers. For matrix chains, dynamic programming finds the optimal order for multiplying matrices with minimum computations. For Fibonacci numbers, it calculates values in linear time by storing previous solutions rather than exponentially recomputing them through recursion.
Page 2 of 5 MG 620 Term Project and Grading RubricsSPRING 2.docxkarlhennesey
Page 2 of 5
MG 620 Term Project and Grading Rubrics SPRING 2019
Grading Rubrics:
>Detailed >
Stage I………………………………………………..…………………………… (30%)
A. Select a topic of interest:
Identify the variable under study
a. Topic:
1. Once you have identified your topic, define your variable of interest which will be your topic for undertaking the research. For example, if your topic is the Transportation industry, then you will focus on the profit for that industry, try to identify some factors that will help explain the industry’s profit such as costs of shipment, cost of production, market shares or revenues. Next, try to formulate your title by turning the relationship between profit and one of the explanatory factor in the form of a question that will motive to collect data to solve the problem.
Title: Do revenues and costs influence the profit of the transportation industry for the most recent twenty years?
2. If you are interested in examining the problems facing First Year College Students, then your topic is 1st Year Students.
Title: Does the number of hours of study influence student Grade Point Average?
The relationship between the dependent and independent variables can be known in advance before the data are collected. This pre-relationship is referred to as the theoretical framework.
Reference at least three scholarly articles to understand how each independent variable influences the dependent variable.
1. Article number 1. For example, in the URL search for student performance. A list of articles will show up. Make a brief analysis (3-5 sentences) describing what the author reported about the relationship; positive, or negative. This will be part of your theoretical expectation of the variables before you collect the data.
2. Article 2. Do the same as above
3. Article 3. Do the same as above.
If your topic is first year students, then you will need to identify theoretically all those factors that may explain the variations of the dependent variable. Some explanations are:
Dependent Variable: GPA for the at least twenty-five students.
Independent Variables: In theory, it is argued that as the number of hours studied, student gain more knowledge and confidence in the course material. So, the relationship between GPA and the number of hours studied is expected to be positive (make reference((s) to your articles). Do the same type of expectation for the average distance (in miles) travelled.
B. Creating your data set before you start to write: Data collection.
This data table will be eventually placed as an Appendix
See below the data set in the table showing GPA and Hours Studied, distance travelled for 25 students.
I: GPA: Grade point average over 25 students
II. Variables that may influence GPA are Hours studied, and Distance travelled.
Table showing the dependent variable (GPA) and independent variables (Hours and distance for twenty-five students.
Student
Dependent (Y)
GPA
Independent(X1)
Hours
Independent (X2)
Distance
Independen ...
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