Quantitative Analysis For Management 12th Edition Pdf Free Download
 Quantitative Analysis For Management 12th Edition Pdf Free Download Free
 Quantitative Analysis For Management 12th Edition Pdf free download. software
 Quantitative Analysis For Management 12th Edition Pdf Free Download For Mac
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For courses in Management Science or Decision Modeling
A solid foundation in quantitative methods and management science
This popular text gives students a genuine foundation in business analytics, quantitative methods, and management science—and how to apply the concepts and techniques in the real world—through a strong emphasis on model building, computer applications, and examples. The authors’ approach presents mathematical models, with all of the necessary assumptions, in clear, plain English, and then applies the ensuing solution procedures to example problems along with stepbystep, howto instructions. In instances in which the mathematical computations are intricate, the details are presented in a manner that ensures flexibility, allowing instructors to omit these sections without interrupting the flow of the material. The use of computer software enables the instructor to focus on the managerial problem and spend less time on the details of the algorithms. Computer output is provided for many examples throughout the text.
Teaching and Learning Experience
Quantitative Analysis For Management Pdf Quantitative Analysis For Management Quantitative Analysis For Management Solutions Quantitative Analysis For Management, 11th Ed Quantitative Analysis For Management 12th Edition Pdf Solution Manual For Quantitative Analysis For Management 11th Quantitative Analysis For Management 11th Edition Answer. Quantitative Analysis for Management  PDF Free Download. Quantitative Analysis for Management TWELFTH EDITION GLOBAL EDITION Charles Harwood. Quantitative analysis for management 12th edition Download Book Quantitative Analysis For Management 12th Edition in PDF format. You can Read Online Quantitative Analysis For Management 12th Edition here in PDF, EPUB, Mobi or Docx formats. Quantitative Analysis for Management 12th Edition Test Bank Barry Render. For management 12th edition solutions free download sample quantitative analysis for management 12th edition solutions pdf quantitative analysis for management test bank free download quantitative analysis for management 12th edition answer key quantitative analysis. Solution Manual For Quantitative Analysis for Management 12th Edition Barry Render. Click to Download Test Bank for Quantitative Analysis for Management 12th Edition Barry Render? Table Of Contents. Chapter 1 Introduction to Quantitative Analysis Chapter 2 Probability Concepts and Applications Chapter 3 Decision Analysis Chapter 4 Regression Models.
Quantitative Analysis For Management 12th Edition Pdf Free Download Free
This text provides a solid foundation in quantitative methods and management science. Here’s how:
 Students see clearly how concepts and techniques are used in real organizations.
 Outstanding intext features provide reinforcement and ensure understanding.
 The text’s use of software allows instructors to focus on the managerial problem, while spending less time on the mathematical details of the algorithms.
Table of Contents
Chapter 1 Introduction to Quantitative Analysis
Chapter 2 Probability Concepts and Applications
Chapter 3 Decision Analysis
Chapter 4 Regression Models
Chapter 5 Forecasting
Chapter 6 Inventory Control Models
Chapter 7 Linear Programming Models: Graphical and Computer Methods
Chapter 8 Linear Programming Applications
Chapter 9 Transportation, Assignment, and Network Models
Chapter 10 Integer Programming, Goal Programming, and Nonlinear Programming
Chapter 11 Project Management
Chapter 12 Waiting Lines and Queuing Theory Models
Chapter 13 Simulation Modeling
Chapter 14 Markov Analysis
Chapter 15 Statistical Quality Control
Online Modules 1 Analytic Hierarchy Process
Online Modules 2 Dynamic Programming
Online Modules 3 Decision Theory and the Normal Distribution
Online Modules 4 Game Theory
Online Modules 5 Mathematical Tools: Determinants and Matrices
Online Modules 6 CalculusBased Optimization
Online Modules 7 Linear Programming: The Simplex Method
Online Modules 8 Transportation, Assignment, and Network Algorithms
These online resources are available at no cost.
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Quantitative Analysis For Management 12th Edition Pdf free download. software
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Quantitative Analysis For Management 12th Edition Pdf Free Download For Mac
Quantitative Analysis for Management 12th Edition Test Bank Barry Render, Ralph M. Stair, Michael E. Hanna, Trevor S. Hale Completed download: https://testbankarea.com/download/quantitativeanalysismanagement12theditiontestbankrenderstairhannahale/ Solutions Manual Quantitative Analysis for Management 12th Edition Render Stair Hanna Hale Completed download Comprehensive package: Solutions Manual, Answer key, Instructor Data, Excel Instructor for all chapters are included: https://testbankarea.com/download/quantitativeanalysismanagement12theditionsolutionsmanualrenderstairhannahale/ Chapter 5 Forecasting 1) A mediumterm forecast typically covers a two to fouryear time horizon. Answer: FALSE Diff: 2 Topic: INTRODUCTION
2) Regression is always a superior forecasting method to exponential smoothing, so regression should be used whenever the appropriate software is available. Answer: FALSE Diff: 1 Topic: INTRODUCTION
3) The three categories of forecasting models are time series, quantitative, and qualitative. Answer: FALSE Diff: 2 Topic: TYPES OF FORECASTING MODELS
4) TIME SERIES models attempt to predict the future by using historical data. Answer: TRUE Diff: 2 Topic: TYPES OF FORECASTING MODELS
5) TIME SERIES models rely on judgment in an attempt to incorporate qualitative or subjective factors into the forecasting model. Answer: FALSE Diff: 1 Topic: TYPES OF FORECASTING MODELS
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6) A moving average forecasting method is a causal forecasting method. Answer: FALSE Diff: 2 Topic: TYPES OF FORECASTING MODELS
7) An exponential forecasting method is a TIME SERIES forecasting method. Answer: TRUE Diff: 2 Topic: TYPES OF FORECASTING MODELS
8) A trendprojection forecasting method is a causal forecasting method. Answer: FALSE Diff: 2 Topic: TYPES OF FORECASTING MODELS
9) Qualitative models produce forecasts that are a little better than simple guesses or coin tosses. Answer: FALSE Diff: 1 Topic: TYPES OF FORECASTING MODELS
10) The most common quantitative causal model is regression analysis. Answer: TRUE Diff: 2 Topic: TYPES OF FORECASTING MODELS
11) Qualitative models attempt to incorporate judgmental or subjective factors into the forecasting model. Answer: TRUE Diff: 1 Topic: TYPES OF FORECASTING MODELS
12) A scatter diagram is useful to determine if a relationship exists between two variables. Answer: TRUE Diff: 1 Topic: SCATTER DIAGRAMS AND TIME SERIES
13) The Delphi method solicits input from customers or potential customers regarding their future purchasing plans. Answer: FALSE Diff: 2 Topic: TYPES OF FORECASTING MODELS
14) The naïve forecast for the next period is the actual value observed in the current period. Answer: TRUE Diff: 2 Topic: MEASURES OF FORECAST ACCURACY
15) Mean absolute deviation (MAD) is simply the sum of forecast errors. Answer: FALSE Diff: 2 Topic: MEASURES OF FORECAST ACCURACY
16) TIME SERIES models enable the forecaster to include specific representations of various qualitative
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and quantitative factors. Answer: FALSE Diff: 2 Topic: COMPONENTS OF A TIME SERIES
17) Four components of time series are trend, moving average, exponential smoothing, and seasonality. Answer: FALSE Diff: 2 Topic: COMPONENTS OF A TIME SERIES
18) The fewer the periods over which one takes a moving average, the more accurately the resulting forecast mirrors the actual data of the most recent time periods. Answer: TRUE Diff: 2 Topic: COMPONENTS OF A TIME SERIES
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19) In a weighted moving average, the weights assigned must sum to 1. Answer: FALSE Diff: 2 Topic: COMPONENTS OF A TIME SERIES
20) A scatter diagram for a time series may be plotted on a twodimensional graph with the horizontal axis representing the variable to be forecast (such as sales). Answer: FALSE Diff: 2 Topic: COMPONENTS OF A TIME SERIES
21) Scatter diagrams can be useful in spotting trends or cycles in data over time. Answer: TRUE Diff: 1 Topic: COMPONENTS OF A TIME SERIES
22) Exponential smoothing cannot be used for data with a trend. Answer: FALSE Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY
23) In a second order exponential smoothing, a low β gives less weight to more recent trends. Answer: TRUE Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY
24) An advantage of exponential smoothing over a simple moving average is that exponential smoothing requires one to retain less data. Answer: TRUE Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Reflective Thinking
25) When the smoothing constant α = 0, the exponential smoothing model is equivalent to the naïve forecasting model. Answer: FALSE Diff: 3 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
26) Multiple regression models use dummy variables to adjust for seasonal variations in an additive TIME SERIES model. Answer: TRUE Diff: 2 Topic: FORECASTING MODELS—TREND, SEASONAL, AND RANDOM VARIATIONS
27) Multiple regression can be used to develop a multiplicative decomposition model. Answer: FALSE Diff: 2 Topic: FORECASTING MODELS—TREND, SEASONAL, AND RANDOM VARIATIONS
28) A seasonal index must be between 1 and +1.
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Answer: FALSE Diff: 2 Topic: ADJUSTING FOR SEASONAL VARIATIONS
29) A seasonal index of 1 means that the season is average. Answer: TRUE Diff: 2 Topic: ADJUSTING FOR SEASONAL VARIATIONS
30) The process of isolating linear trend and seasonal factors to develop a more accurate forecast is called regression. Answer: FALSE Diff: 2 Topic: ADJUSTING FOR SEASONAL VARIATIONS
31) When the smoothing constant α = 1, the exponential smoothing model is equivalent to the naïve forecasting model. Answer: TRUE Diff: 3 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
32) Multiple regression may be used to forecast both trend and seasonal components present in a time series. Answer: TRUE Diff: 2 Topic: FORECASTING MODELS—TREND, SEASONAL, AND RANDOM VARIATIONS
33) Adaptive smoothing is analogous to exponential smoothing where the coefficients α and β are periodically updated to improve the forecast. Answer: TRUE Diff: 2 Topic: MONITORING AND CONTROLLING FORECASTS
34) Bias is the average error of a forecast model. Answer: TRUE Diff: 2 Topic: MEASURES OF FORECAST ACCURACY
35) Which of the following is not classified as a qualitative forecasting model? A) exponential smoothing B) Delphi method C) jury of executive opinion D) sales force composite E) consumer market survey Answer: A Diff: 1 Topic: TYPES OF FORECASTING MODELS
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36) A judgmental forecasting technique that uses decision makers, staff personnel, and respondent to determine a forecast is called A) exponential smoothing. B) the Delphi method. C) jury of executive opinion. D) sales force composite. E) consumer market survey. Answer: B Diff: 2 Topic: TYPES OF FORECASTING MODELS
37) Which of the following is considered a causal method of forecasting? A) exponential smoothing B) moving average C) Holt's method D) Delphi method E) None of the above Answer: E Diff: 2 Topic: TYPES OF FORECASTING MODELS
38) A graphical plot with sales on the Y axis and time on the X axis is a A) scatter diagram. B) trend projection. C) radar chart. D) line graph. E) bar chart. Answer: A Diff: 2 Topic: FORECASTING MODELS—TREND AND RANDOM VARIATIONS
39) Which of the following statements about scatter diagrams is true? A) Time is always plotted on the yaxis. B) It can depict the relationship among three variables simultaneously. C) It is helpful when forecasting with qualitative data. D) The variable to be forecasted is placed on the yaxis. E) It is not a good tool for understanding TIME SERIES data. Answer: D Diff: 2 Topic: COMPONENTS OF A TIME SERIES
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40) Which of the following is a technique used to determine forecasting accuracy? A) exponential smoothing B) moving average C) regression D) Delphi method E) mean absolute percent error Answer: E Diff: 1 Topic: MEASURES OF FORECAST ACCURACY
41) A mediumterm forecast is considered to cover what length of time? A) 24 weeks B) 1 month to 1 year C) 24 years D) 510 years E) 20 years Answer: B Diff: 2 Topic: INTRODUCTION
42) When is the exponential smoothing model equivalent to the naïve forecasting model? A) α = 0 B) α = 0.5 C) α = 1 D) during the first period in which it is used E) never Answer: C Diff: 3 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
43) Enrollment in a particular class for the last four semesters has been 120, 126, 110, and 130. Suppose a onesemester moving average was used to forecast enrollment (this is sometimes referred to as a naïve forecast). Thus, the forecast for the second semester would be 120, for the third semester it would be 126, and for the last semester it would be 110. What would the MSE be for this situation? A) 196.00 B) 230.67 C) 100.00 D) 42.00 E) None of the above Answer: B Diff: 2 Topic: MEASURES OF FORECAST ACCURACY AACSB: Analytic Skills
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44) Which of the following methods tells whether the forecast tends to be too high or too low? A) MAD B) MSE C) MAPE D) decomposition E) bias Answer: E Diff: 2 Topic: MEASURES OF FORECAST ACCURACY
45) Assume that you have tried three different forecasting models. For the first, the MAD = 2.5, for the second, the MSE = 10.5, and for the third, the MAPE = 2.7. We can then say: A) the third method is the best. B) the second method is the best. C) methods one and three are preferable to method two. D) method two is least preferred. E) None of the above Answer: E Diff: 2 Topic: MEASURES OF FORECAST ACCURACY
46) Which of the following methods gives an indication of the percentage of forecast error? A) MAD B) MSE C) MAPE D) decomposition E) bias Answer: C Diff: 1 Topic: MEASURES OF FORECAST ACCURACY
47) Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent). Forecast sales for the next day using a twoday moving average. A) 14 B) 13 C) 15 D) 28 E) 12.5 Answer: A Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
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48) As one increases the number of periods used in the calculation of a moving average, A) greater emphasis is placed on more recent data. B) less emphasis is placed on more recent data. C) the emphasis placed on more recent data remains the same. D) it requires a computer to automate the calculations. E) one is usually looking for a longterm prediction. Answer: B Diff: 2 Topic: COMPONENTS OF A TIME SERIES AACSB: Reflective Thinking
49) Enrollment in a particular class for the last four semesters has been 122, 128, 100, and 155 (listed from oldest to most recent). The best forecast of enrollment next semester, based on a threesemester moving average, would be A) 116.7. B) 126.3. C) 168.3. D) 135.0. E) 127.7. Answer: E Diff: 1 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
50) Which of the following methods produces a particularly stiff penalty in periods with large forecast errors? A) MAD B) MSE C) MAPE D) decomposition E) bias Answer: B Diff: 2 Topic: MEASURES OF FORECAST ACCURACY AACSB: Reflective Thinking
51) The process of isolating linear trend and seasonal factors to develop more accurate forecasts is called A) regression. B) decomposition. C) smoothing. D) monitoring. E) None of the above Answer: B Diff: 2 Topic: FORECASTING MODELS—TREND, SEASONAL, AND RANDOM VARIATIONS
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52) Sales for boxes of Girl Scout cookies over a 4month period were forecasted as follows: 100, 120, 115, and 123. The actual results over the 4month period were as follows: 110, 114, 119, 115. What was the MAD of the 4month forecast? A) 0 B) 5 C) 7 D) 108 E) None of the above Answer: C Diff: 2 Topic: MEASURES OF FORECAST ACCURACY AACSB: Analytic Skills
53) Sales for boxes of Girl Scout cookies over a 4month period were forecasted as follows: 100, 120, 115, and 123. The actual results over the 4month period were as follows: 110, 114, 119, 115. What was the MSE of the 4month forecast? A) 0 B) 5 C) 7 D) 108 E) None of the above Answer: E Diff: 2 Topic: MEASURES OF FORECAST ACCURACY AACSB: Analytic Skills
54) Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent). Forecast sales for the next day using a threeday weighted moving average where the weights are 3, 1, and 1 (the highest weight is for the most recent number). A) 12.8 B) 13.0 C) 70.0 D) 14.0 E) None of the above Answer: D Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
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55) Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent). Forecast sales for the next day using a twoday weighted moving average where the weights are 3 and 1. A) 14.5 B) 13.5 C) 14 D) 12.25 E) 12.75 Answer: A Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
56) Which of the following is not considered to be one of the components of a time series? A) trend B) seasonality C) variance D) cycles E) random variations Answer: C Diff: 2 Topic: COMPONENTS OF A TIME SERIES
57) Which of the following statements about the decomposition method is/are false? A) The process of 'deseasonalizing' involves multiplying by a seasonal index. B) Dummy variables are used in a regression model as part of an additive approach to seasonality. C) Computing seasonal indices is the first step of the decomposition method. D) Data is 'deseasonalized' after the trend line is found. E) Decomposition can involve additive or multiplicative methods with respect to seasonality. Answer: D Diff: 3 Topic: FORECASTING MODELS—TREND, SEASONAL, AND RANDOM VARIATIONS
58) Enrollment in a particular class for the last four semesters has been 120, 126, 110, and 130 (listed from oldest to most recent). Develop a forecast of enrollment next semester using exponential smoothing with an alpha = 0.2. Assume that an initial forecast for the first semester was 120 (so the forecast and the actual were the same). A) 118.96 B) 121.17 C) 130 D) 120 E) None of the above Answer: B Diff: 3 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
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59) Demand for soccer balls at a new sporting goods store is forecasted using the following regression equation: Y = 98 + 2.2X where X is the number of months that the store has been in existence. Let April be represented by X = 4. April is assumed to have a seasonality index of 1.15. What is the forecast for soccer ball demand for the month of April (rounded to the nearest integer)? A) 123 B) 107 C) 100 D) 115 E) None of the above Answer: B Diff: 2 Topic: FORECASTING MODELS—TREND AND RANDOM VARIATIONS AACSB: Analytic Skills
60) A TIME SERIES forecasting model in which the forecast for the next period is the actual value for the current period is the A) Delphi model. B) Holt's model. C) naïve model. D) exponential smoothing model. E) weighted moving average. Answer: C Diff: 2 Topic: MEASURES OF FORECAST ACCURACY AACSB: Analytic Skills
61) In picking the smoothing constant for an exponential smoothing model, we should look for a value that A) produces a nicelooking curve. B) equals the utility level that matches with our degree of risk aversion. C) produces values which compare well with actual values based on a standard measure of error. D) causes the least computational effort. E) None of the above Answer: C Diff: 1 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY
62) Which of the following is not considered one of the steps to developing the decomposition method? A) compute seasonal indices using CMAs B) deseasonalize the data by dividing each number by its seasonal index C) find the equation of the trend line using the deseasonlized data D) forecast for future periods using the trend line E) add the seasonal index to the trend forecast Answer: E Diff: 3 Topic: FORECASTING MODELS—TREND, SEASONAL, AND RANDOM VARIATIONS
63) A method to measure how well predictions fit actual data is
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A) decomposition B) smoothing C) tracking signal D) regression E) moving average Answer: C Diff: 2 Topic: MONITORING AND CONTROLLING FORECASTS
64) If the Q1 demand for a particular year is 200 and the seasonal index is 0.85, what is the deseasonalized demand value for Q1? A) 170 B) 185 C) 215 D) 235.29 E) 250 Answer: D Diff: 2 Topic: FORECASTING METHODS—TREND, SEASONAL, AND RANDOM VARIATIONS
65) In the exponential smoothing with trend adjustment forecasting method, β is the A) slope of the trend line. B) new forecast. C) Yaxis intercept. D) independent variable. E) trend smoothing constant. Answer: E Diff: 2 Topic: FORECASTING MODELS—TREND AND RANDOM VARIATIONS
66) Using the additive decomposition model, what would be the period 2, Q3 forecast using the following equation:
= 20 + 3.2X1 + 1.5X2 + 0.8X3 + 0.6X4?
A) 23.2 B) 25 C) 27 D) 27.2 E) 27.9 Answer: D Diff: 2 Topic: FORECASTING MODELS—TREND, SEASONAL, AND RANDOM VARIATIONS
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67) The computer monitoring of tracking signals and selfadjustment is referred to as A) exponential smoothing. B) adaptive smoothing. C) trend projections. D) trend smoothing. E) running sum of forecast errors (RFSE). Answer: B Diff: 2 Topic: MONITORING AND CONTROLLING FORECASTS
68) Which of the following is not a characteristic of trend projections? A) The variable being predicted is the Y variable. B) Time is the X variable. C) It is useful for predicting the value of one variable based on time trend. D) A negative intercept term always implies that the dependent variable is decreasing over time. E) They are often developed using linear regression. Answer: D Diff: 2 Topic: FORECASTING MODELS—TREND AND RANDOM VARIATIONS
69) A tracking signal was calculated for a particular set of demand forecasts. This tracking signal was positive. This would indicate that A) demand is greater than the forecast. B) demand is less than the forecast. C) demand is equal to the forecast. D) the MAD is negative. E) None of the above Answer: A Diff: 2 Topic: MONITORING AND CONTROLLING FORECASTS
70) A seasonal index of ________ indicates that the season is average. A) 10 B) 100 C) 0.5 D) 0 E) 1 Answer: E Diff: 2 Topic: ADJUSTING FOR SEASONAL VARIATIONS
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71) The errors in a particular forecast are as follows: 4, 3, 2, 5, 1. What is the tracking signal of the forecast? A) 0.4286 B) 2.3333 C) 5 D) 1.4 E) 2.5 Answer: B Diff: 3 Topic: MONITORING AND CONTROLLING FORECASTS AACSB: Analytic Skills
72) Demand for a particular type of battery fluctuates from one week to the next. A study of the last six weeks provides the following demands (in dozens): 4, 5, 3, 2, 8, 10 (last week). (a) Forecast demand for the next week using a twoweek moving average. (b) Forecast demand for the next week using a threeweek moving average. Answer: (a) (8 + 10)/2 = 9 (b) (2 + 8 + 10)/3 = 6.67 Diff: 1 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
73) Daily high temperatures in the city of Houston for the last week have been: 93, 94, 93, 95, 92, 86, 98 (yesterday). (a) Forecast the high temperature today using a threeday moving average. (b) Forecast the high temperature today using a twoday moving average. (c) Calculate the mean absolute deviation based on a twoday moving average, covering all days in which you can have a forecast and an actual temperature. Answer: (a) (92 + 86 + 98)/3 = 92 (b) (86 + 98)/2 = 92 (c) MAD = ( + + + + ) / 5 = 20.5 / 5 = 4.1 Diff: 2 Topic: VARIOUS AACSB: Analytic Skills
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74) For the data below: Month January February March April May June
Automobile Battery Sales 20 21 15 14 13 16
Automobile Battery Sales 17 18 20 20 21 23
Month July August September October November December
(a) Develop a scatter diagram. (b) Develop a threemonth moving average. (c) Compute MAD.
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Answer: (a) scatter diagram
(b) Month January February March April May June July August September October November December January
Automobile Battery Sales 20 21 15 14 13 16 17 18 20 20 21 23 
3Month Moving Avg. 18.67 16.67 14 14.33 15.33 17 18.33 19.33 20.33 21.33
(c) MAD = 2.85 Diff: 3 Topic: VARIOUS AACSB: Analytic Skills
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Absolute Deviation 4.67 3.67 2 2.67 3.67 3 1.67 1.67 2.67 
75) For the data below: Month January February March April May June
Automobile Tire Sales 80 84 60 56 52 64
Automobile Tire Sales 68 100 80 80 84 92
Month July August September October November December
(a) Develop a scatter diagram. (b) Compute a threemonth moving average. (c) Compute the MSE.
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Answer: (a) scatter diagram
(b) Month January February March April May June July August September October November December January
Automobile Tire Sales 80 84 60 56 52 64 68 100 80 80 84 92 
3Month Tire Average 74.7 66.7 56.0 57.3 61.3 77.3 82.7 86.7 81.3 85.33
(c) MSE = 264.26 Diff: 3 Topic: VARIOUS AACSB: Analytic Skills
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Squared Error 349.69 216.09 64 114.49 1497.69 7.29 7.29 7.29 114.49
76) For the data below: Year 1990 1991 1992 1993 1994 1995 1996
Automobile Sales 116 105 29 59 108 94 27
Year 1997 1998 1999 2000 2001 2002 2003
Automobile Sales 119 34 34 48 53 65 111
(a) Develop a scatter diagram. (b) Develop a sixyear moving average forecast. (c) Find MAPE.
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Answer: (a) scatter diagram
(b) Year 1990 1991 1992 1993 1994 1995 1996 9 1998 1999 2000 2001 2002 2003
Number of Automobiles 116 105 29 59 108 94 27 119 34 34 48 53 65 111
Forecast
Error
Error Actual
X X X X X X 85.2 70.3 72.7 73.5 69.3 59.3 52.5 58.8
58.2 48.7 38.7 39.5 21.3 6.3 12.5 52.2
2.15 0.41 1.14 1.16 0.44 0.12 0.19 0.47
(c) MAPE = .76 ∗ 100% = 76% Diff: 3 Topic: VARIOUS AACSB: Analytic Skills
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77) Use simple exponential smoothing with α = 0.3 to forecast battery sales for February through May. Assume that the forecast for January was for 22 batteries. Month January February March April
Automobile Battery Sales 42 33 28 59
Answer: Forecasts for February through May are: 28, 29.5, 29.05, and 38.035. Diff: 2 Topic: VARIOUS AACSB: Analytic Skills
78) Average starting salaries for students using a placement service at a university have been steadily increasing. A study of the last four graduating classes indicates the following average salaries: $30,000, $32,000, $34,500, and $36,000 (last graduating class). Predict the starting salary for the next graduating class using a simple exponential smoothing model with α = 0.25. Assume that the initial forecast was $30,000 (so that the forecast and the actual were the same). Answer: Forecast for next period = $32,625 Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
79) Use simple exponential smoothing with α = 0.33 to forecast the tire sales for February through May. Assume that the forecast for January was for 22 sets of tires. Month January February March April
Automobile Battery Sales 28 21 39 34
Answer: Forecast for Feb. through May = 23.98, 22.9966, 28.2777, and 30.1661 Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY AACSB: Analytic Skills
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80) The following table represents the new members that have been acquired by a fitness center. Month Jan Feb March April
New members 45 60 57 65
Assuming α = 0.3, β = 0.4, an initial forecast of 40 for January, and an initial trend adjustment of 0 for January, use exponential smoothing with trend adjustment to come up with a forecast for May on new members. Answer: Ft Tt FITt Month New members Jan Feb March April May
45 60 57 65
40 41.5 47.47 52.2526 58.57011
0 0.6 2.748 3.56184 4.664107
May forecast = 58.57 Diff: 3 Topic: FORECASTING MODELS—TREND AND RANDOM VARIATIONS AACSB: Analytic Skills
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40 42.1 50.218 55.81444 63.23422
81) The following table represents the number of applicants at a popular private college in the last four years. Month 2007 2008 2009 2010
New members 10,067 10,940 11,116 10,999
Assuming α = 0.2, β = 0.3, an initial forecast of 10,000 for 2007, and an initial trend adjustment of 0 for 2007, use exponential smoothing with trend adjustment to come up with a forecast for 2011 on the number of applicants. Answer: Month
# of applicants
2007 2008 2009 2010 2011
10,067 10,940 11,116 10,999
Ft 10,000 10013.4 10201.94 10432.25 10634.12
Tt
FITt
0 4.02 59.3748 110.6562 138.0219
10000 10017.42 10261.31 10542.9 10772.15
2011 Forecast = 10,634 Diff: 3 Topic: FORECASTING MODELS—TREND AND RANDOM VARIATIONS AACSB: Analytic Skills
82) Given the following data, if MAD = 1.25, determine what the actual demand must have been in period 2 (A2). Time Period 1
Forecast (F) 3
1
2
Actual (A) 2 A2 = ?
4

3 4
6 4
5 6
1 2
Answer: A2 = 3 or A2 = 5 Diff: 2 Topic: MEASURES OF FORECAST ACCURACY AACSB: Analytic Skills
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83) Calculate (a) MAD, (b) MSE, and (c) MAPE for the following forecast versus actual sales figures. (Please round to four decimal places for MAPE.) Forecast 100 110 120 130
Actual 95 108 123 130
Answer: (a) MAD = 10/4 = 2.5 (b) MSE = 38/4 = 9.5 (c) MAPE = (0.0956/4)100 = 2.39% Diff: 2 Topic: MEASURES OF FORECAST ACCURACY AACSB: Analytic Skills
84) Use the sales data given below to determine: Year 1995 1996 1997 1998
Sales (units) 130 140 152 160
Year 1999 2000 2001 2002
Sales (units) 169 182 194 ?
(a) The least squares trend line. (b) The predicted value for 2002 sales. (c) The MAD. (d) The unadjusted forecasting MSE. Answer: (a) = 119.14 + 10.46X (b) 119.14 + 10.46(8) = 202.82 (c) MAD = 1.01 (d) MSE = 1.71 Diff: 3 Topic: VARIOUS AACSB: Analytic Skills
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85) For the data below: Year 1990 1991 1992 1993 1994 1995 1996
Automobile Sales 116 105 29 59 108 94 27
Year 1977 1998 1999 2000 2001 2002 2003
Automobile Sales 119 34 34 48 53 65 111
(a) Determine the least squares regression line. (b) Determine the predicted value for 2004. (c) Determine the MAD. (d) Determine the unadjusted forecasting MSE. Answer: (a) = 85.15  1.8X (b) 85.15  1.8 (15) = 58.15 (c) MAD = 30.09 (d) MSE = 1,121.66 Diff: 3 Topic: VARIOUS AACSB: Analytic Skills
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86) Given the following gasoline data: Quarter 1 2 3 4
Year 1 150 140 185 160
Year 2 156 148 201 174
(a) Compute the seasonal index for each quarter. (b) Suppose we expect year 3 to have annual demand of 800. What is the forecast value for each quarter in year 3? Answer: (a) Average twoQuarterly Average Quarter Year 1 Year 2 year demand demand seasonal index 1 150 156 153 164.25 .932 2 140 148 144 164.25 .877 3 185 201 193 164.25 1.175 4 160 174 167 164.25 1.017 (b) Quarter 1 2 3 4
Forecast 200 * .932 = 186.00 200 * .877 = 175.34 200 * 1.175 = 235.01 200 * 1.017 = 203.35
Diff: 3 Topic: ADJUSTING FOR SEASONAL VARIATIONS AACSB: Analytic Skills
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87) Given the following data and seasonal index:
(a) Compute the seasonal index using only year 1 data. (b) Determine the deseasonalized demand values using year 2 data and year 1's seasonal indices. (c) Determine the trend line on year 2's deseasonalized data. (d) Forecast the sales for the first 3 months of year 3, adjusting for seasonality. Answer: (a) and (b)
(c) y = 11.96 + .29X (d) Jan = [11.96 + .29 (13)] * .87 = 13.69 Feb = [11.96 + .29 (14)] * .67 = 12.18 Mar = [11.96 + .29 (15)] * .55 = 8.97 Diff: 3 Topic: ADJUSTING FOR SEASONAL VARIATIONS AACSB: Analytic Skills
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88) Wick's Ski Shop is looking to forecast ski sales on a quarterly basis based on the historical data listed in the table below:
Use the steps to develop a forecast using the decomposition method to answer the following questions: (a) Using the CMAs, calculate the seasonal indices for Q1, Q2, Q3, and Q4. (b) Find the equation for the trend line using deseasonalized data. (c) Find the year 5 quarterly forecasts. Answer: (a) Q1 — 2.1174, Q2 — 0.6129, Q3 — 0.3320, Q4 — 0.9324 (b) y = 227.73 + 4.32X (c) Q1 forecast — 637.66, Q2 forecast — 187.22, Q3 forecast — 102.85, Q4 forecast — 292.88 Diff: 3 Topic: FORECASTING MODELS—TREND, SEASONAL, AND RANDOM VARIATIONS
89) The following table represents the actual vs. forecasted amount of new customers acquired by a major credit card company: Month Jan Feb March April May
Actual 1024 1057 1049 1069 1065
Forecast 1010 1025 1141 1053 1059
(a) What is the tracking signal? (b) Based on the answer in part (a), comment on the accuracy of this forecast. Answer: Month Actual Forecast Error RSFE Jan 1024 1010 14 14 14 Feb 1057 1025 32 46 32 March 1049 1141 92 46 92 April 1069 1053 16 30 16 May 1065 1059 6 24 6 (a) RSFE/MAD = 24/32 = 0.75 MAD (b) The answer in part (a) indicates an accurate forecast, one where overall, the actual amount of new customers was slightly less than the forecast. Diff: 3 Topic: MONITORING AND CONTROLLING FORECASTS AACSB: Analytic Skills
90) What is the basic additive decomposition model (in regression terms)?
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Answer:
= a + b1X1 + b2X2 + b3X3 + b4X4
Where X1 = time period; X2 = 1 if quarter 2, 0 otherwise; X3 = 1 if quarter 3, 0 otherwise; X4 = 1 if quarter 4, 0 otherwise. Diff: 2 Topic: TYPES OF FORECASTING MODELS
91) In general terms, describe what causal forecasting models are. Answer: Causal forecasting models incorporate variables or factors that might influence the quantity being forecasted. Diff: 2 Topic: TYPES OF FORECASTING MODELS
92) In general terms, describe what qualitative forecasting models are. Answer: Qualitative forecasting models attempt to incorporate judgmental or subjective factors into the model. Diff: 2 Topic: TYPES OF FORECASTING MODELS
93) Briefly describe the structure of a scatter diagram for a time series. Answer: A scatter diagram for a time series may be plotted on a twodimensional graph with the horizontal axis representing the time period, while the variable to be forecast (such as sales) is placed on the vertical axis. Diff: 2 Topic: COMPONENTS OF A TIME SERIES
94) Briefly describe the jury of executive opinion forecasting method. Answer: The jury of executive opinion forecasting model uses the opinions of a small group of highlevel managers, often in combination with statistical models, and results in a group estimate of demand. Diff: 2 Topic: TYPES OF FORECASTING MODELS
95) Briefly describe the consumer market survey forecasting method. Answer: It is a forecasting method that solicits input from customers or potential customers regarding their future purchasing plans. Diff: 2 Topic: TYPES OF FORECASTING MODELS
96) Describe the naïve forecasting method. Answer: The forecast for the next period is the actual value observed in the current period. Diff: 2 Topic: MEASURES OF FORECAST ACCURACY
97) Briefly describe why the scatter diagram is helpful. Answer: Scatter diagrams show the relationships between model variables. Diff: 1 Topic: COMPONENTS OF A TIME SERIES
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98) Explain, briefly, why most forecasting error measures use either the absolute or the square of the error. Answer: A deviation is equally important whether it is above or below the actual. This also prevents negative errors from canceling positive errors that would understate the true size of the errors. Diff: 2 Topic: MEASURES OF FORECAST ACCURACY
99) List four measures of historical forecasting errors. Answer: MAD, MSE, MAPE, and Bias Diff: 2 Topic: MEASURES OF FORECAST ACCURACY
100) In general terms, describe what TIME SERIES forecasting models are. Answer: forecasting models that make use of historical data Diff: 1 Topic: COMPONENTS OF A TIME SERIES
101) List four components of TIME SERIES data. Answer: trend, seasonality, cycles, and random variations Diff: 2 Topic: COMPONENTS OF A TIME SERIES
102) Explain, briefly, why the larger number of periods included in a moving average forecast, the less well the forecast identifies rapid changes in the variable of interest. Answer: The larger the number of periods included in the moving average forecast, the less the average is changed by the addition or deletion of a single number. Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY
103) State the mathematical expression for exponential smoothing. Answer: Ft+1 = Ft + α(Yt  Ft) Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY
104) Explain, briefly, why, in the exponential smoothing forecasting method, the larger the value of the smoothing constant, α, the better the forecast will be in allowing the user to see rapid changes in the variable of interest. Answer: The larger the value of α, the greater is the weight placed on the most recent values. Diff: 2 Topic: FORECASTING MODELS—RANDOM VARIATIONS ONLY
105) In exponential smoothing, discuss the difference between α and β. Answer: α is a weight applied to adjust for the difference between last period actual and forecasted value. β is a trend smoothing constant. Diff: 2 Topic: FORECASTING MODELS—TREND AND RANDOM VARIATIONS
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106) In general terms, describe the difference between a general linear regression model and a trend projection. Answer: A trend projection is a regression model where the independent variable is always time. Diff: 2 Topic: FORECASTING MODELS—TREND AND RANDOM VARIATIONS
107) In general terms, describe a centered moving average. Answer: An average of the values centered at a particular point in time. This is used to compute seasonal indices when trend is present. Diff: 2 Topic: ADJUSTING FOR SEASONAL VARIATIONS
108) The decomposition approach to forecasting (using trend and seasonal components) may be helpful when attempting to forecast a TIME SERIES. Could an analogous approach be used in multiple regression analysis? Explain briefly. Answer: An analogous approach would be possible using time as one independent variable and using a set of dummy variables to represent the seasons. Diff: 2 Topic: FORECASTING MODELS—TREND, SEASONAL, AND RANDOM VARIATIONS
109) List the steps to develop a forecast using the decomposition method. Answer: 1. Compute seasonal indices using CMAs. 2. Deseasonalize the data by dividing each number by its seasonal index. 3. Find the equation of a trend line using the deseasonalized data. 4. Forecast for future periods using the trend line. 5. Multiply the trend line forecast by the appropriate seasonal index. Diff: 2 Topic: FORECASTING MODELS—TREND, SEASONAL, AND RANDOM VARIATIONS
110) What is one advantage of using causal models over TIME SERIES or qualitative models? Answer: Use of the causal model requires that the forecaster gain an understanding of the relationships, not merely the frequency of variation; i.e., the forecaster gains a greater understanding of the problem than the other methods. Diff: 2 Topic: TYPES OF FORECASTING MODELS AACSB: Reflective Thinking
111) Discuss the use of a tracking signal. Answer: A tracking signal measures how well predictions fit actual data. By setting tracking limits, a manager is signaled to reevaluate the forecasting method. Diff: 2 Topic: MONITORING AND CONTROLLING FORECASTS
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