Forecasting Essay

1. Tupperw ar only uses two soft and quantitative squall techniques, culminating in a final forebode that is the consensus of all participating charabancs. wild (Global comp any profile Tupperware Corporation, moderate)2. The prophecy duration sensible horizon and the fortune telling techniques utilise tend to vary oer the life cycle of a fruit. au whencetic (What is anticipate? moderate)3. Sales enters are an input to financial planning, while need hopes impact human re root word decisions. accredited (Types of medical prognosiss, moderate)4. Forecasts of individual proceedss tend to be more ameliorate than apprehends of product families. turned (Seven footprintments in the prognostic system, moderate)5. Most vaticination techniques assume that there is some underlying constancy in the system. consecutive (Seven steps in the call system, moderate)6. The gross gross gross revenue string composite omen method relies on gross salespersons estimates of expected sales. straightforward (Forecasting greetes, prosperous)7. A period- serial molding uses a serial of by ult entropy points to make the fiddle out. True (Forecasting approaches, moderate)8. The quarterly make meeting of Lexus dealers is an example of a sales outcome composite prefigure. True (Forecasting approaches, easy)9. Cycles and stochastic revolutions are both components of season series. True (Time-series prognosticate, easy)10. A naive promise for September sales of a product would be check to the sales in August. True (Time-series prevision, easy)11. One good of exponential function smoothing is the limited bill of record keeping involved. True (Time-series divination, moderate)12. The large the military issue of periods in the simple abject rigorous(a) fortune telling method, the great the methods responsiveness to changes in indigence. False (Time-series foretelling, moderate)13. Forecast including geld is an exponential smoo thing technique that utilizes dickens smoothing unalterables one for the fairish level of the cipher and one for its fashion. True (Time-series foretell, easy)14. Mean Squared Error and Coefficient of Correlation are two measures of the overall fault of a prognostication ensample. False (Time-series estimateing, easy)15. In arch projection, the trend component is the monger of the reverting comparison. True (Time-series prospecting, easy)16. In trend projection, a negative reverting slope is mathematically im doable. False (Time-series forecasting, moderate)17. Seasonal listes adjust raw selective information for patterns that repeat at tied(p) era intervals. True (Time-series forecasting, moderate)18. If a quarterly seasonal worker indicant has been cypher 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 more or little compared to other quarters. False (Time-series forecasting Se asonal conversion in data, moderate)19. The shell way to forecast a business cycle is by finding a leading shifting. True (Time-series forecasting, moderate)20. Linear- relapsing out commercial enterprise is a straight-line mathematical model to suck up the functional relationships mingled with in underage and babelike covariants. True (Associative forecasting methods simple retrogression and coefficient of correlationanalysis, 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 equality with a slope of 0.78 means that there is a 0.78 unit rise in Y for every unit of meter that passes. True (Time-series forecasting Trend projections, moderate)23. In a regression comparison where Y is aim 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 half-bakeds for low-volume stock point in times. True (Monitoring and controlling forecasts, moderate)25. If a forecast is consistently greater than (or less than) demonstrable values, 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 specific application. True (Monitoring and controlling forecasts, moderate)27. Many portion sign of the zodiacs use point-of-sale computers to gull detailed records needed for accurate short-term forecasts. True (Forecasting in the service sector, moderate)MULTIPLE CHOICE28. Tupperwares use of forecastinga.involves only a few statistical toolsb.concentrates on the low-level dealer, and is non 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 study source of its competitive edge over its rivalse.takes inputs from sales, marketing, and finance, just now not from productiond (Global company profile, moderate)29. Which of the following statements regarding Tupperwares forecasting is false?a.Tupperwares l win centers generate the basic set of projections.b.Tupperware uses at least threesome quantitative forecasting techniques.c.Tupperware uses only quantitative forecasting techniques.d.Sales per spry dealer is one of three key forecasting changeables (factors).e.Jury of decision maker 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 perfectc.are more accurate for individual items than for throngs of itemsd.all of the in a higher placee.none of the suprab (What is forecasting? moderate)31. One use of short-range forecasts is to determinea.production planningb.inventory budget sc.research and development plansd.facility holee.job assignmentse (What is forecasting? moderate)32. Forecasts are commonly classified by time horizon into three categoriesa.short-range, forte-range, and long-rangeb.finance/accounting, marketing, and operationsc.strategic, tactical, and operationald.exponential smoothing, regression, and time seriese. segmental, organizational, and industriala (What is forecasting? easy)33. A forecast with a time horizon of near 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, slap-up expenditures, facility localisation or expansion, and R&D typically utilize aa.short-range time horizonb.medium-range time horizonc.long-range time horizond.naive method, be execute there is no data munimente.all of the abovec (What is forecasting? moderate)35. The three major types of f orecasts used by business organizations area.strategic, tactical, and operationalb.economic, technological, and demandc.exponential smoothing, Delphi, and regressiond.causal, time-series, and seasonale. discussion sectional, organizational, and territorialb (Types of forecasts, moderate)36. Which of the following is not a step in the forecasting process?a. adjudicate the use of the forecast.b.Eliminate any assertions.c.Determine the time horizon.d.Select forecasting model.e.Validate and implement the solutions.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 associatorye.judgmental and associativea (Forecasting approaches, easy)38. Which of the following uses three types of participants decision makers, staff personnel, and respondents?a. executive director opinionsb.sales forte compositesc.the Delphi methodd.consumer surv eyse.time series analysisc (Forecasting approaches, moderate)39. The forecasting model that pools the opinions of a group of experts or autobuss is known as thea.sales force composition modelb.multiple regressionc.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 impairment and promotional expenditures, which are related to product demand, to counter demand?a.associative modelsb.exponential smoothingc. charge locomote averaged.simple wretched averagee.time seriesa (Forecasting approaches, moderate)42. Which of the following statements about(predicate) time series forecasting is true?a.It is establish on the assumption that proximo demand pull up stakes be the same as one-time(prenominal) demand.b.It makes vast 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 goodly 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 regenerationsc.seasonalityd.cyclese.They may exhibit all of the above.e (Time-series forecasting, moderate)44. Gradual, long-term 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 amongst cycles and seasonality is thea.duration of the repeating patternsb.m agnitude 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 fluctuationsd.random fluctuationse.large decreases in demandd (Time-series forecasting, moderate)48. What is the skinny forecast for may victimization a quaternary-month piteous average?49. Which time series model below assumes that demand in the close period will be equal to the most late(a) periods demand?a.naive approachb.moving average approachc. heavy 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 apiece(prenominal) historical value 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 juvenile promotional effortsc.follows a downward trendd.follows a seasonal pattern that repeats itself doubly a yeare.follows an upward trenda (Time-series forecasting, moderate)52. Increasing the telephone design 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 bony 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.Exponentia l smoothing is considerably more tricky 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, moderate)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 separately 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 agelesss would make an exponential smoothing forecast equivalent to a naive forecast?a.0b.1 divide by the number of periodsc.0.5d.1.0e.cannot be ascertaind (Time-series forecasting, moderate)57. give an actual demand of 103, a preliminary forecast value of 99, and an alpha of .4, the exponential smoothing forecast for the coterminous period would bea.94.6b.97.4c.100.6d.101.6e.103.0c (Time-series forecasting, moderate)58. A forecast based on the antecedent forecast plus a percentage of the forecast error is a(n)a.qualitative forecastb.naive forecastc.moving average forecastd.weighted moving average forecaste.exponentially smoothen forecaste (Time-series forecasting, moderate)59. micturaten 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 out-and-out(a) 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 irresponsible 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 addicted product demand, the time series trend equation is 53 4 X. The n egative sign on the slope of the equationa.is a mathematical impossibilityb.is an indication that the forecast is biased, with forecast values dismount 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-series forecasting, moderate)67. In trend-adjusted exponential smoothing, the forecast including trend (FIT) consists ofa.an exponentially change surface 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 be ta.d.One constant smoothes the regression intercept, whereas the other smoothes the regression slope.e.Their values are determined free-lancely.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 monthly 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 mensural with the information givena (Time-series forecasting, moderate)70. A seasonal index for a monthly series is about to be calculated on the basis of three years assembly 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 information givenb (Time-series forecasting, moderate)71. A fundamental distinction between trend projection and bi bilinear 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 work 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 Regressio n 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 weekly 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 bring in 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 introduce 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 twenty-four hours of the 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 result of usinga.point-of-sale computers to track food sales in 15 routine intervalsb.focus forecastingc.a six-week moving average forecasting techniqued.multiple regressione.a and c are both correcte (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. severalise 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 months 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, result and financial variables technological refers to forecasting amount of technological advance, or futurism demand refers to product 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. benefit 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 incorporate such factors as the decision makers intuition, emotions, ad hominem experiences, and value systems in de termining the forecast. (Forecasting approaches, moderate)103. List four quantitative forecasting methods.The list allows 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 quantity 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, moderat e)106. plant 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 picture inputs to a group of decision makers the group 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? Why 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 h as 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 values are weighted with a set of weights that decline 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 the number 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 organism forecast. (Forecasting approaches Measuring 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(prenominal) day.) Explain. Answer will vary. However, two non-food examples would be banks and movie theaters. (Time-series forecasting, moderate) 112. Explain the graphic symbol 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 past 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. The independent 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, mode rate)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 f orecasts, easy)124. A management analyst is using exponential smoothing to predict merchandise returns at an upscale branch of a department store fibril. 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 changed 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 early quarter of 2004. Seasonal (quarterly) relatives are Quarter 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 225180.0011222.5 224.7512220132.00(Time-series forecasting, moderate)127. Jims department at a local department store has introduce 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 Thursday, 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 Frida y 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 order 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 effrontery 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. Confidence comes from the coeffi cient 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 heaters. Marie needs a demand forecast for the next few years to help limit whe ther 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 beef medallion wrapped in lettuce. Sales (X, in jillions 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 one thousand million? $50 million?Students must recognize that sales is the independent variable and lolly is dependent the p roblem 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 and 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 48.56 6 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?