Forecasting Essay

1. Tupperw ar only uses both qualitative and quantitative foretelling techniques, culminating in a final exam bode that is the consensus of each(prenominal) in all participating managers. False (Global company profile Tupperware Corporation, moderate)2. The prophecy condemnation horizon and the aiming techniques used tend to vary all oer the look cycle of a crossing. sure (What is preindicationing? moderate)3. sales visualises are an input to financial planning, opus film cyphers impact human resource decisions. True (Types of forecasts, moderate)4. Forecasts of mortal products tend to be more accurate than forecasts of product families. False (Seven tone of voices in the prophecy system, moderate)5. Most call techniques as jibee that there is some key stability in the system. True (Seven go in the forecasting system, moderate)6. The sales force tangled forecasting method relies on salespersons estimates of expected sales. True (Forecasting approaches, well-h eeled)7. A succession- serial model uses a series of past data points to bind the forecast. True (Forecasting approaches, moderate)8. The quarterly make meeting of Lexus dealers is an example of a sales force composite forecast. True (Forecasting approaches, easy)9. Cycles and haphazard waverings are both comp integritynts of time series. True (Time-series forecasting, easy)10. A naive forecast for September sales of a product would be personify to the sales in August. True (Time-series forecasting, easy)11. One advantage of exponential smoothing is the limited amount of record keeping involved. True (Time-series forecasting, moderate)12. The bigger the number of outcomes in the sincere pitiful norm forecasting method, the greater the methods responsiveness to changes in postulate. False (Time-series forecasting, moderate)13. Forecast including bring buck is an exponential smoothing technique that put ons two smoothing constants one for the average level of the forecast and one for its trend. True (Time-series forecasting, easy)14. Mean Squared Error and Coefficient of Correlation are two measures of the overall error of a forecasting model. False (Time-series forecasting, easy)15. In trend projection, the trend component is the inc business of the relapse equation. True (Time-series forecasting, easy)16. In trend projection, a negative statistical regression slope is mathematically impossible. False (Time-series forecasting, moderate)17. Seasonal indexes adjust raw data for patterns that repeat at symmetrical time intervals. True (Time-series forecasting, moderate)18. If a quarterly seasonal index has been cipher at 1.55 for the October-December quarter, then raw data for that quarter must be multiplied by 1.55 so that the quarter can be fairly compared to otherwise quarters. False (Time-series forecasting Seasonal variation in data, moderate)19. The best way to forecast a business cycle is by finding a star variable. True (Time-series for ecasting, moderate)20. Linear-regression analysis is a straight-line mathematical model to describe the functional relationships between in restricted and dependent variables. True (Associative forecasting methods obsession and correlation coefficientanalysis, easy)21. The larger the standard error of the estimate, the more accurate the forecasting model. False (Associative forecasting methods Regression and correlation analysis, easy)22. A trend projection equation with a slope of 0.78 compresseds that there is a 0.78 unit rise in Y for every unit of time that passes. True (Time-series forecasting Trend projections, moderate)23. In a regression equation where Y is demand and X is advertising, a coefficient of determination (R2) of .70 means that 70% of the variance in advertising is explained by demand. False (Associative forecasting methods Regression and correlation analysis, moderate)24. Tracking limits should be within 8 MADs for low-volume stock items. True (Monitoring and controlling forecasts, moderate)25. If a forecast is consistently greater than (or slight than) developed determine, the forecast is said to be biased. True (Monitoring and controlling forecasts, moderate)26. Focus forecasting tries a variety of computer models and selects the best one for a particular application. True (Monitoring and controlling forecasts, moderate)27. Many service firms use point-of-sale computers to collect detailed records compulsory for accurate short-term forecasts. True (Forecasting in the service sector, moderate)MULTIPLE select28. Tupperwares use of forecastinga.involves only a few statistical toolsb.concentrates on the low-level dealer, and is not aggregated at the company levelc.relies on the fact that all of its products are in the maturity phase of the life cycled.is a major source of its competitive edge over its rivalse.takes inputs from sales, grocery stack awaying, and finance, barely not from productiond (Global company profile, moderate)2 9. Which of the pursual statements regarding Tupperwares forecasting is false?a.Tupperwares fifty profit centers generate the basic set of projections.b.Tupperware uses at least third quantitative forecasting techniques.c.Tupperware uses only quantitative forecasting techniques.d.Sales per active dealer is one of collar key forecasting variables (factors).e. gore of executive opinion is the ultimate forecasting tool used at Tupperware.c (Global company profile, moderate)30. Forecastsa.become more accurate with longer time horizonsb.are rarely sodding(a)c.are more accurate for individual items than for bases of itemsd.all of the abovee.none of the aboveb (What is forecasting? moderate)31. One use of short-range forecasts is to determinea.production planningb.inventory budgetsc.research and development plansd.facility locatione.job assignmentse (What is forecasting? moderate)32. Forecasts are usually classified by time horizon into collar categoriesa.short-range, strong suit-ra nge, and long-rangeb.finance/accounting, marketing, and operationsc.strategic, tactical, and operationald.exponential smoothing, regression, and time seriese.departmental, organizational, and industriala (What is forecasting? easy)33. A forecast with a time horizon of about 3 months to 3 years is typically called aa.long-range forecastb.medium-range forecastc.short-range forecastd.weather forecaste.strategic forecastb (What is forecasting? moderate)34. Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize aa.short-range time horizonb.medium-range time horizonc.long-range time horizond.naive method, because there is no data historye.all of the abovec (What is forecasting? moderate)35. The common chord major types of forecasts used by business organizations area.strategic, tactical, and operationalb.economic, technological, and demandc.exponential smoothing, Delphi, and regressiond.causal, time-series, and seasonale.d epartmental, organizational, and territorialb (Types of forecasts, moderate)36. Which of the followers is not a step in the forecasting process?a.Determine the use of the forecast.b.Eliminate any assumptions.c.Determine the time horizon.d.Select forecasting model.e.Validate and implement the results.b (The strategic importance of forecasting, moderate)37. The two general approaches to forecasting area.qualitative and quantitativeb.mathematical and statisticalc.judgmental and qualitatived. diachronic and associativee.judgmental and associativea (Forecasting approaches, easy)38. Which of the spare-time activity uses deuce-ace types of participants decision makers, staff personnel, and respondents?a.executive opinionsb.sales force compositesc.the Delphi methodd.consumer surveyse.time series analysisc (Forecasting approaches, moderate)39. The forecasting model that pools the opinions of a group of experts or managers is known as thea.sales force composition modelb.multiple regression c.jury of executive opinion modeld.consumer market survey modele.management coefficients modelc (Forecasting approaches, moderate)40. Which of the following is not a type of qualitative forecasting?a.executive opinionsb.sales force compositesc.consumer surveysd.the Delphi methode. base averagee (Forecasting approaches, moderate)41. Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand?a.associative modelsb.exponential smoothingc.weighted wretched averaged. wide moving averagee.time seriesa (Forecasting approaches, moderate)42. Which of the following statements about time series forecasting is true?a.It is based on the assumption that future demand will be the same as past demand.b.It makes extensive use of the data collected in the qualitative approach.c.The analysis of past demand helps predict future demand.d.Because it accounts for trends, cycles, and seasonal patterns, it is more fibr ous than causal forecasting.e.All of the above are true.c (Time-series forecasting, moderate)43. Time series data may exhibit which of the following behaviors?a.trendb.random variationsc.seasonalityd.cyclese.They may exhibit all of the above.e (Time-series forecasting, moderate)44. Gradual, long-run movement in time series data is calleda.seasonal variationb.cyclesc.trendsd.exponential variatione.random variationc (Time-series forecasting, moderate)45. Which of the following is not present in a time series?a.seasonalityb.operational variationsc.trendd.cyclese.random variationsb (Time-series forecasting, moderate)46. The fundamental difference between cycles and seasonality is thea.duration of the repeating patternsb.magnitude of the variationc.ability to attribute the pattern to a caused.all of the abovee.none of the abovea (Time-series forecasting, moderate)47. In time series, which of the following cannot be predicted?a.large increases in demandb.technological trendsc.seasonal fl uctuationsd.random fluctuationse.large decreases in demandd (Time-series forecasting, moderate)48. What is the approximate forecast for May using a four-month moving average?49. Which time series model below assumes that demand in the abutting period will be equal to the approximately recent periods demand?a.naive approachb.moving average approachc.weighted moving average approachd.exponential smoothing approache.none of the abovea (Time-series forecasting, easy)50. Which of the following is not a characteristic of simple moving averages?a.It smoothes random variations in the data.b.It has minimal data storage requirements.c.It weights each historical order equally.d.It lags changes in the data.e.It smoothes real variations in the data.b (Time-series forecasting, moderate)51. A six-month moving average forecast is better than a three-month moving average forecast if demanda.is rather stableb.has been changing due to recent promotional effortsc.follows a downward trendd.follows a seasonal pattern that repeats itself twice a yeare.follows an upward trenda (Time-series forecasting, moderate)52. Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense ofa.manager understandingb.accuracyc.stabilityd.responsiveness to changese.All of the above are diminished when the number of periods increases.d (Time-series forecasting, moderate)53. Which of the following statements comparing the weighted moving average technique and exponential smoothing is true?a.Exponential smoothing is more easily used in combination with the Delphi method.b.More emphasis can be placed on recent values using the weighted moving average.c.Exponential smoothing is considerably more difficult to implement on a computer.d.Exponential smoothing typically requires less record keeping of past data.e.Exponential smoothing allows one to develop forecasts for multiple periods, whereas weighted moving averages does not.d (Time-series forecasting, moder ate)54. Which time series model uses past forecasts and past demand data to generate a new forecast?a.naiveb.moving averagec.weighted moving averaged.exponential smoothinge.regression analysisd (Time-series forecasting, moderate)55. Which is not a characteristic of exponential smoothing?a.smoothes random variations in the datab.easily altered weighting schemec.weights each historical value equallyd.has minimal data storage requirementse.none of the above they are all characteristics of exponential smoothingc (Time-series forecasting, moderate)56. Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?a.0b.1 dual-lane by the number of periodsc.0.5d.1.0e.cannot be situatedd (Time-series forecasting, moderate)57. urinaten an actual demand of 103, a previous forecast value of 99, and an alpha of .4, the exponential smoothing forecast for the next period would bea.94.6b.97.4c.100.6d.101.6e.103.0c (Time-series forecasting, moderate)58. A forecast based on the previous forecast plus a percentage of the forecast error is a(n)a.qualitative forecastb.naive forecastc.moving average forecastd.weighted moving average forecaste.exponentially smoothed forecaste (Time-series forecasting, moderate)59. Given an actual demand of 61, a previous forecast of 58, and an of .3, what would the forecast for the next period be using simple exponential smoothing?a.45.5b.57.1c.58.9d.61.0e.65.5c (Time-series forecasting, moderate)60. Which of the following values of alpha would cause exponential smoothing to respond the most slowly to forecast errors?a.0.10b.0.20c.0.40d.0.80e.cannot be determineda (Time-series forecasting, moderate)61. A forecasting method has produced the following over the past five months. What is the mean absolute deviation?62. The primary purpose of the mean absolute deviation (MAD) in forecasting is toa.estimate the trend lineb.eliminate forecast errorsc.measure forecast accuracyd.seasonally adjust the forecaste.all of the abovec (Time-series forecasting, moderate)63. Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?a.2b.3c.4d.8e.16c (Time-series forecasting, moderate)64. The last four months of sales were 8, 10, 15, and 9 units. The last four forecasts were 5, 6, 11, and 12 units. The Mean Absolute Deviation (MAD) isa.2b.-10c.3.5d.9e.10.5c (Time-series forecasting, moderate)65. A time series trend equation is 25.3 + 2.1 X. What is your forecast for period 7?a.23.2b.25.3c.27.4d.40.0e.cannot be determinedd (Time-series forecasting, moderate)66. For a given product demand, the time series trend equation is 53 4 X. The negative sign on the slope of the equationa.is a mathematical impossibilityb.is an indication that the forecast is biased, with forecast values lower than actual valuesc.is an indication that product demand is decliningd.implies that the coefficient of determination will also be negativee.implies that the RSFE will be negativec (Time-ser ies forecasting, moderate)67. In trend-adjusted exponential smoothing, the forecast including trend (FIT) consists ofa.an exponentially smoothed forecast and an estimated trend valueb.an exponentially smoothed forecast and a smoothed trend factorc.the old forecast adjusted by a trend factord.the old forecast and a smoothed trend factore.a moving average and a trend factorb (Time-series forecasting, moderate)68. Which of the following is true regarding the two smoothing constants of the Forecast Including Trend (FIT) model?a.One constant is positive, while the other is negative.b.They are called MAD and RSFE.c.Alpha is always smaller than beta.d.One constant smoothes the regression intercept, whereas the other smoothes the regression slope.e.Their values are determined independently.e (Time-series forecasting, moderate)69. Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January periodic index is 1.25. What is the seasonally-adjusted sales forecast for January?a.640 unitsb.798.75 unitsc.800 unitsd.1000 unitse.cannot be calculated with the training givena (Time-series forecasting, moderate)70. A seasonal index for a monthly series is about to be calculated on the substructure of three years accumulation of data. The three previous July values were 110, 150, and 130. The average over all months is 190. The approximate seasonal index for July isa.0.487b.0.684c.1.462d.2.053e. cannot be calculated with the discipline givenb (Time-series forecasting, moderate)71. A fundamental distinction between trend projection and linear regression is thata.trend projection uses least squares while linear regression does notb.only linear regression can have a negative slopec.in trend projection the independent variable is time in linear regression the independent variable need not be time, but can be any variable with explanatory powerd.linear regression tends to die hard better on data that lack trendse.trend projection uses two smoothing constants, not just onec (Associative forecasting methods Regression and correlation analysis, moderate)72. The percent of variation in the dependent variable that is explained by the regression equation is measured by thea.mean absolute deviationb.slopec.coefficient of determinationd.correlation coefficiente.interceptc (Associative forecasting methods Regression and correlation analysis, moderate)73. The degree or strength of a linear relationship is shown by thea.alphab.meanc.mean absolute deviationd.correlation coefficiente.RSFEd (Associative forecasting methods Regression and correlation analysis, moderate)74. If two variables were perfectly correlated, the correlation coefficient r would equala.0b.less than 1c.exactly 1d.-1 or +1e.greater than 1d (Associative forecasting methods Regression and correlation analysis, moderate)75. The last four periodic values of sales were 80, 100, 105, and 90 units. The last four forecasts were 60, 80, 95, and 75 units. These forecasts illustratea.qualitative methodsb.adaptive smoothingc.sloped.biase.trend projectiond (Monitoring and controlling forecasts, easy)76. The principaling signal is thea.standard error of the estimateb.running sum of forecast errors (RSFE)c.mean absolute deviation (MAD)d.ratio RSFE/MADe.mean absolute percentage error (MAPE)d (Monitoring and controlling forecasts, moderate)77. Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is characteristic ofa.exponential smoothing including trendb.adaptive smoothingc.trend projectiond.focus forecastinge.multiple regression analysisb (Monitoring and controlling forecasts, moderate)78. Many services maintain records of sales notinga.the solar daytime of the calendar weekb.unusual eventsc.weatherd.holidayse.all of the abovee (Forecasting in the service sector, moderate)79. Taco Bells unique employee scheduling practices are partly the r esult of usinga.point-of-sale computers to track food sales in 15 minute intervalsb.focus forecastingc.a six-week moving average forecasting techniqued.multiple regressione.a and c are both placee (Forecasting in the service sector, moderate)96. A skeptical manager asks what short-range forecasts can be used for. Give her three possible uses/purposes. Any three of planning purchasing, job scheduling, work force levels, job assignments, production levels. (What is forecasting? moderate)97. A skeptical manager asks what long-range forecasts can be used for. Give her three possible uses/purposes. Any three of planning new products, capital expenditures, facility location or expansion, research and development. (What is forecasting? moderate)98. Describe the three forecasting time horizons and their use. Forecasting time horizons are short rangegenerally less than three months, used for purchasing, job scheduling, work force levels, production levels medium rangeusually from three mont hs up to three years, used for sales planning, production planning and budgeting, cash budgeting, analyzing operating plans long rangeusually three years or more, used for new product development, capital expenditures, facility planning, and R&D. (What is forecasting? moderate)99. List and briefly describe the three major types of forecasts. The three types are economic, technological, and demand economic refers to macroeconomic, growth and financial variables technological refers to forecasting amount of technological advance, or futurism demand refers toproduct demand. (Types of forecasts, moderate)100. List the seven steps involved in forecasting.1. Determine the use of the forecast.2. Select the items that are to be forecast.3. Determine the time horizon of the forecast.4. Select the forecasting model(s).5. Gather the data needed to make the forecast.6. Make the forecast.7. Validate the forecasting mode and implement the results.(Seven steps in the forecasting process, moderate) 101. What are the realities of forecasting that companies face? First, forecasts are seldom perfect. Second, most forecasting techniques assume that there is some underlying stability in the system. Finally, both product family and aggregated forecasts are more accurate than individual product forecasts. (Seven steps in the forecasting system, moderate)102. What are the differences between quantitative and qualitative forecasting methods? Quantitative methods use mathematical models to analyze historical data. Qualitative methods represent such factors as the decision makers intuition, emotions, personal experiences, and value systems in determining the forecast. (Forecasting approaches, moderate)103. List four quantitative forecasting methods.The list includes naive, moving averages, exponential smoothing, trend projection, and linear regression. (Forecasting approaches, moderate)104. What is a time-series forecasting model?A time series forecasting model is any mathematical model that uses historical values of the standard of interest to predict future values of that quantity. (Forecasting approaches, easy)105. What is the difference between an associative model and a time-series model? A time series model uses only historical values of the quantity ofinterest to predict future values of that quantity. The associative model, on the other hand, attempts to identify underlying causes or factors that control the variation of the quantity of interest, predict future values of these factors, and use these predictions in a model to predict future values of the specific quantity of interest. (Forecasting approaches, moderate)106. Name and discuss three qualitative forecasting methods. Qualitative forecasting methods include jury of executive opinion, where high-level managers arrive at a group estimate of demand sales force composite, where salespersons estimates are aggregated Delphi method, where respondents provide inputs to a group of decision makers the grou p of decision makers, often experts, then make the actual forecast consumer market survey, where consumers are queried about their future purchase plans. (Forecasting approaches, moderate)107. List the four components of a time series. Which one of these is rarely forecast? wherefore is this so? Trend, seasonality, cycles, and random variation. Since random variations follow no discernible pattern, they cannot be predicted, and thus are not forecast. (Time-series forecasting, moderate)108. Compare seasonal effects and cyclical effects.A cycle is longer (typically several years) than a season (typically days, weeks, months, or quarters). A cycle has variable duration, while a season has fixed duration and regular repetition. (Time-series forecasting, moderate)109. Distinguish between a moving average model and an exponential smoothing model. Exponential smoothing is a weighted moving average model wherein previous values are weighted in a specific mannerin particular, all previous v alues are weighted with a set of weights that deny exponentially. (Time-series forecasting, moderate)110. Describe three popular measures of forecast accuracy.Measures of forecast accuracy include (a) MAD (mean absolute deviation). This is a sum of the absolute values of individual errors divided by thenumber of periods of data. (b) MSE (mean squared error). This is the average of the squared differences between the forecast and observed values. (c) MAPE (mean absolute percent error) is independent of the magnitude of the variable being forecast. (Forecasting approaches bar forecast error, moderate)111. Give an exampleother than a restaurant or other food-service firmof an organization that experiences an hourly seasonal pattern. (That is, each hour of the day has a pattern that tends to repeat day after day.) Explain. Answer will vary. However, two non-food examples would be banks and movie theaters. (Time-series forecasting, moderate) 112. Explain the role of regression models ( time series and otherwise) in forecasting. That is, how is trend projection able to forecast? How is regression used for causal forecasting? For trend projection, the independent variable is time. The trend projection equation has a slope that is the change in demand per period. To forecast the demand for period t, perform the calculation a + bt. For causal forecasting, the independent variables are predictors of the forecast value or dependent variable. The slope of the regression equation is the change in the Y variable per unit change in the X variable. (Time-series forecasting, difficult)113. List three advantages of the moving average forecasting model. List three disadvantages of the moving average forecasting model. Two advantages of the model are that it uses simple calculations, it smoothes out sudden fluctuations, and it is easy for users to understand. The disadvantages are that the averages always stay within past ranges, that they require extensive record keeping of pas t data, and that they do not pick up on trends very well. (Time-series forecasting, moderate)114. What does it mean to decompose a time series?To decompose a time series means to break past data down into components of trends, seasonality, cycles, and random blips, and to project them forward. (Time-series forecasting, easy)115. Distinguish a dependent variable from an independent variable. Theindependent variable causes some behavior in the dependent variable the dependent variable shows the effect of changes in the independent variable. (Associative forecasting methods Regression and correlation, moderate)116. Explain, in your own words, the meaning of the coefficient of determination. The coefficient of determination measures the amount (percent) of total variation in the data that is explained by the model. (Associative forecasting methods Regression and correlation, moderate)117. What is a tracking signal? How is it calculated? Explain the connection between adaptive smoothing and tracking signals. A tracking signal is a measure of how well the forecast actually predicts. Its calculation is the ratio of RSFE to MAD. The larger the absolute tracking signal, the worse the forecast is performing. Adaptive smoothing sets limits to the tracking signal, and makes changes to its forecasting models when the tracking signal goes beyond those limits. (Monitoring and controlling forecasts, moderate)118. What is focus forecasting?It is a forecasting method that tries a variety of computer models, and selects the one that is best for a particular application. (Monitoring and controlling forecasts, easy)124. A management analyst is using exponential smoothing to predict merchandise returns at an upscale branch of a department set up chain. Given an actual number of returns of 154 items in the most recent period completed, a forecast of 172 items for that period, and a smoothing constant of 0.3, what is the forecast for the next period? How would the forecast be change d if the smoothing constant were 0.6? Explain the difference in terms of alpha and responsiveness. 166.6 161.2 The larger the smoothing constant in an exponentially smoothed forecast, the more responsive the forecast. (Time-series forecasting, easy)126. The following trend projection is used to predict quarterly demand Y = 250 2.5t, where t = 1 in the first quarter of 2004. Seasonal (quarterly) relatives are line 1 = 1.5 Quarter 2 = 0.8 Quarter 3 = 1.1 and Quarter 4 = 0.6. What is the seasonally adjusted forecast for the four quarters of 2006?PeriodProjectionAdjusted9 227.5341.2510 225180.0011222.5224.7512220132.00(Time-series forecasting, moderate)127. Jims department at a local department store has tracked the sales of a product over the last ten weeks. Forecast demand using exponential smoothing with an alpha of 0.4, and an initial forecast of 28.0. Calculate MAD and the tracking signal. What do you recommend?130. A small family-owned restaurant uses a seven-day moving average model to determine manpower requirements. These forecasts need to be seasonalized because each day of the week has its own demand pattern. The seasonal relatives for each day of the week are Monday, 0.445 Tuesday, 0.791 Wednesday, 0.927 thorium, 1.033 Friday, 1.422 Saturday, 1.478 and Sunday 0.903. Average daily demand based on the most recent moving average is 194 patrons. What is the seasonalized forecast for each day of next week? The average value multiplied by each days seasonal index. Monday 194 x .445 = 86 Tuesday 194 x .791 = 153 Wednesday 194 x .927 = 180 Thursday 194 x 1.033 = 200 Friday 194 x 1.422 = 276 Saturday 194 x 1.478 = 287 and Sunday 194 x .903 = 175. (Associative forecasting methods Regression and correlation, moderate)131. A restaurant has tracked the number of meals served at lunch over the last four weeks. The data shows little in terms of trends, but does display substantial variation by day of the week. Use the following information to determine the seasonal (daily) index for this restaurant.132. A firm has modeled its experience with industrial accidents and found that the number of accidents per year (Y) is related to the number of employees (X) by the regression equation Y = 3.3 + 0.049*X. R-Square is 0.68. The regression is based on 20 annual observations. The firm intends to employ 480 workers next year. How many accidents do you project? How much boldness do you have in that forecast? Y = 3.3 + 0.049 * 480 = 3.3 + 23.52 = 26.52 accidents. This is not a time series, so next year = year 21 is of no relevance. presumption comes from the coefficient of determination the model explains 68% of the variation in number of accidents, which seems respectable. (Associative forecasting methods Regression and correlation, moderate)133. Demand for a certain product is forecast to be 8,000 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January? 8,000 x 1.25 = 10,000 (Time-series forecasting, easy)134. A seasonal index for a monthly series is about to be calculated on the basis of three years accumulation of data. The three previous July values were 110, 135, and 130. The average over all months is 160. The approximate seasonal index for July is(110 + 135 + 130)/3 = 125 125/160 = 0.781 (Time-series forecasting,moderate)135. Marie Bain is the production manager at a company that manufactures hot water supply heaters. Marie needs a demand forecast for the next few years to help decide whether to add new production capacity. The companys sales history (in thousands of units) is shown in the table below. Use exponential smoothing with trend adjustment, to forecast demand for period 6. The initial forecast for period 1 was 11 units the initial estimate of trend was 0. The smoothing constants are = .3 and = .3136. The quarterly sales for specific educational software over the past three years are given in the following table. Compute the four seasonal factors.137. An innovative restaurateur owns and operates a dozen Ultimate Low-Carb restaurants in northern Arkansas. His signature item is a cheese-encrusted flush medallion wrapped in lettuce. Sales (X, in one million million millions of dollars) is related to Profits (Y, in hundreds of thousands of dollars) by the regression equation Y = 8.21 + 0.76 X. What is your forecast of profit for a store with sales of $40 million? $50 million?Students must recognize that sales is the independent variable and boodle is dependent the problem is not a time series. A store with $40 million in sales 40 x 0.76 = 30.4 30.4 + 8.21 = 38.61, or $3,861,000 in profit $50 million in sales is estimated to profit 46.21 or $4,621,000. (Associative forecasting methods Regression and correlation, moderate)138. Arnold Tofu owns and operates a chain of 12 vegetable protein hamburger restaurants in northern Louisiana. Sales figures an d profits for the stores are in the table below. Sales are given in millions of dollars profits are in hundreds of thousands of dollars. Calculate a regression line for the data. What is your forecast of profit for a store with sales of $24 million? $30 million?Students must recognize that sales is the independent variable and profits is dependent. Store number is not a variable, and the problem is not a time series. The regression equation is Y = 5.936 + 1.421 X (Y = profit, X = sales). A store with $24 million in sales is estimated to profit 40.04 or $4,004,000 $30 million in sales should yield 48.566 or $4,856,600 in profit. (Associative forecasting methods Regression and correlation, moderate)139. The department manager using a combination of methods has forecast sales of toasters at a local department store. Calculate the MAD for themanagers forecast. Compare the managers forecast against a naive forecast. Which is better?