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Business Forecasting with Business forecastX 6th Edition Test Bank

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    1 Chapter 1 MULTIPLE CHOICE TEST BANK Note: The correct answer is denoted by **. 1. Which of the following does not require sophisticated quantitative forecasts? A) Accounting revenue forecasts for tax purposes. B) Money managers use of interest rate forecasts for asset allocation decisions. C) Managers of power plants using weather forecasts in forecasting power demand. D) State highway planners require peak load forecasts for planning purposes. E) All the above require quantitative forecasts. ** 2. Under what circumstances may it make sense not to prepare a business forecast? A) No data is readily available. B) The future will be no different from the past. ** C) The forecast horizon is 40 years. D) There is no consensus among informed individuals. E) The industry to forecast is undergoing dramatic change. 3. What is most likely to be the major difference between forecasting sales of a private business versus forecasting the demand of a public good supplied by a governmental agency? A) Amount of data available. B) Underlying economic relationships. C) Lack of market - determined price data for public goods. ** D) Last of historical data. E) Lack of quantitative ability by government forecasters. 4. Which of the following points about supply chain management is incorrect ? A) Forecasts are required at each step in the supply chain. B) Forecasts of sales are required for partners in the supply chain. C) Collaborative forecasting systems across the supply chain are needed. D) If you get the forecast right, you have the potential to get everything else right in the supply chain. E) None of the above. ** 5. Which of the following is not typically part of the traditional forecasting textbook? A) Classical statistics applied to business forecasting. B) Use of computationally intensive forecasting software . ** C) Attention to simplifying assumptions about the data. D) Discussion of probability distributions.

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    2 E) Attention to statistical inference. 6. Which subjective forecasting method depends upon the anonymous opinion of a panel of individuals to generate sales forecasts? A) Sales Force Composites. B) Customer Surveys. C) Jury of Executive Opinion. D) Delphi Method. ** E) None of the above. 7. Which subjective sales forecasting method may have the most information about the spending plans of customers for a specific firm? A) Sales Force Composites. ** B) Index of consumer sentiment. C) Jury of Executive Opinion. D) Delphi Method. E) None of the above. 8. Which subjective sales forecasting technique may have problems with individuals who have a dominant personality? A) Sales Force Composites. B) Customer Surveys. C) Jury of Executive Opinion. ** D) Delphi Method. E) None of the above. 9. Which of the following methods is not useful for forecasting sales of a new product? A) Time series techniques requiring lots of historical data. ** B) Delphi Method. C) Consumer Surveys. D) Test market results. E) All the above are correct. 10. Which of the following is not considered a subjective forecasting method? A) Sales force composites. B) Naive methods. ** C) Delphi methods. D) Juries of executive opinion. E) Consumer surveys. 11. Which of the following is not an argument for the use of subjective forecasting models?

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    3 A) They are easy for management to understand. B) They are quite useful for long - range forecasts. C) They provide valuable information that may not be present in quantitative models. D) They are useful when data for using quantitative models is extremely limited. E) None of the above. ** 12. Forecasts based solely on the most recent observation(s) of the variable of interest A) are called “naive” forecasts. B) are the simplest of all quantitative forecasting methods. C) leads to loss of one data point in the forecast series relative to the original series. D) are consistent with the “random walk” hypothesis in finance, which states that the optimal forecast of today's stock rate of return is yesterday's actual rate of return. E) All the above. ** 13. You are given a time series of sales data with 10 observations. You construct forecasts according to last period’s actual level of sales plus the most recent observed change in sales. How many data points will be lost in the forecast process relative to the original data series? A) One. B) Two. ** C) Three. D) Zero. E) None of the above. 14. Suppose you are attempting to forecast a variable that is independent over time such as stock rates of return. A potential candidate - forecasting model is A) The Jury of Executive Opinion. B) Last period’s actual rate of return. ** C) The Delphi Method. D) Last period’s actual rate of return plus some proportion of the most recently observed rate of change in the series. E) None of the above. 15. Measures of forecast accuracy based upon a quadratic error cost function, notably root mean square error (RMSE), tend to treat A) levels of large and small forecast errors equally. B) large and small forecast errors equally on the margin. C) large and small forecast errors unequally on the margin. ** D) every forecast error with the same penalty. E) None of the above. 16. Which of the following is incorrect ? Evaluation of forecast accuracy

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    4 A) is important since the production of forecasts is costly to the firm. B) requires the use of symmetric error cost functions. C) is important since it may reduce business losses from inaccurate forecasts. D) is done by averaging forecast errors. E) both b) and d) are incorrect. ** F) both a) and b) are incorrect. 17. Which of the following measures of forecast accuracy can be used to compare “goodness of fit” across different sized variables? A) Mean Absolute Error. B) Mean Absolute Percentage Error. ** C) Mean Squared Error. D) Root Mean Squared Error. E) None of the above. 18. Which of the following measures is a poor indicator of forecast accuracy, but useful in determining the direction of bias in a forecasting model? A) Mean Absolute Percentage Error. B) Mean Percentage Error. ** C) Mean Squared Error. D) Root Mean Squared Error. E) None of the above. 19. Which measure of forecast accuracy is analogous to standard deviation? A) Mean Absolute Error. B) Mean Absolute Percentage Error. C) Mean Squared Error. D) Root Mean Squared Error. **. 20. Which of the following measures of forecast performance are used to compare models for a given data series? A) Mean Error. B) Mean Absolute Error. C) Mean Squared Error. D) Root Mean Squared Error. E) All of the above. ** 21. What values of Theil’s U statistic are indicative of an improvement in forecast accuracy relative to the no - change naive model? A) U < 0.

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    5 B) U = 0. C) U < 1. ** D) U > 1. E) None of the above. 22. RMSE applied to the analysis of model forecast errors, treats A) levels of large and small forecast errors equally. B) large and small forecast errors equally on the margin. C) large and small forecast errors unequally on the margin. ** D) every forecast error with the same penalty. 23. Because of different units of various data series, which accuracy statistic can be used across different series? A) MSE. B) RMSE. C) MAPE . ** D) MAE E) None of the above. 24. Some helpful hints on judging forecast accuracy include: A) Be wary when the forecast outcome is not independent of the forecaster. B) Do not judge model adequacy based on large one - time errors. C) Do not placed unwarranted faith in computer - based forecasts. D) Keep in mind what exactly you are trying to forecast. E) All of the above. ** 25. Which of the following is not an appropriate use of forecast errors to access the accuracy of a particular forecasting model? A) Examine a time series plot of the errors and look for a random pattern. B) Examine the average absolute value of the errors. C) Examine the average squared value of the errors. D) Examine the average level of the errors. ** E) None of the above. 26. Which of the following forecasting methods requires use of large and extensive data sets? A) Naive methods. B) Exponential smoothing methods. C) Multiple regression. ** D) Delphi methods. E) None of the above.

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    6 27. When using quarterly data to forecast domestic car sales, how can the simple naive forecasting model be amended to model seasonal behavior of new car sales, i.e., patterns of sales that arise at the same time every year? A) Forecast next period's sales based on this period's sales. B) Forecast next period's sales based on last period's sales. C) Forecast next period’s sales based on the average sales over the current and last three quarters. D) Forecast next period's sales based on sales four quarters ago. ** E) None of the above. ESSAY/PROBLEM EXAM QUESTIONS 1. The State of Oregon has a law requiring that all state revenue, raised in excess of what had been forecast, be refunded to taxpayers. Besides making state residents happy when revenue forecasts understate true revenues, what impact would such a law have on the quality of forecasts? Is revenue forecasting in Oregon a political exercise? How would such a provision affect your ability to generate unbiased forecasts? In your opinion, is this a good law? ANSWER: The intent of the law was to force State planners to accurately forecast state budgets and that any revenue that exceeded forecasts could not simply be spent by politicians. The problem is that forecasters will have skewed incentives, which is an unforeseen consequence of this statute. For instance, state revenue forecasters may find that it makes politicians happy when revenues are under forecast since taxpayers get a kicker check in the mail from the State of Oregon. On the other hand, forecasters could be forced to subjectively increase revenue estimates so as not to pay out the forecast - error surplus. Is this a good law? Not from the forecasters prospective, who should be concerned with forecast accuracy and not the political consequences of a forecast error of a given type. 2. Comment on the following quote by a recent graduate from Quant - Tech: "Nobody in their right mind uses subjective forecasting techniques today. They are always biased and contain somebody else's opinion, which is certainty nothing that can be trusted." ANSWER: Besides not being true, this comment ignores the simple fact that subjective forecasts may contain valuable information that may not be available from quantitative forecasting methods. For example, situations in which little or no data exist may require subjective forecasts. In addition, some forecasters are quite skilled in making subjective forecasts of the business cycle. Finally, in Chapter Eight the composite forecast process is described in which subjective methods play an important role. 3. Contrast and compare forecasting for a private for - profit firm and for a public not - for - profit firm. Are the two necessarily different from the perspective of the forecaster?

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    7 ANSWER: Not really. Quantitative methods are equally applicable for profit firms and not - for - profit firms. However, many public goods are not valued in the marketplace, but instead are valued in the political process. This implies that, in some cases, adequate price data for public forecasting may not be available. 4. How do forecast practitioners rank various forecasting models applied to any given problem? Which technique is used most often in practice and why? Explain in - sample and out - of - sample forecast model evaluation. ANSWER: In most cases, the forecaster will have available several different models to forecast a given variable. To select which model is the best, forecasters commonly employ a measure called root - mean - squared - error (RMSE), which is essentially the standard deviation of forecast errors. It is also very important to distinguish between fit and accuracy. Fit refers to in - sample model performance, whereas accuracy refers to out - of - sample model performance. In many cases models that perform well in sample perform very poorly out - of - sample. Since forecast accuracy is always first priority, emphasis should be placed on out - of - sample RMSE rather than model fit. This is usually accomplished by use of a holdout period in the sample. This is a period at the end of the sample in which forecasts from earlier periods can be made to access the accuracy of a given model. 5. You are the quality control manager in a plant that produces bunjee cords. Your responsibility is to oversee the production of the synthetic material in the cord. Specifically, your responsibility is to ensure that bunjee cords have the correct elastic qualities to avoid personal injury lawsuits. Your efforts are compounded in that you use two procedures for testing bunjee cord elasticity, procedure A and procedure B. Procedure A is generally subject to error, but few are very large. On the other hand, procedure B is very accurate but subject to large one - time errors. Specifically, forecast errors in evaluating the dynamic cord elasticity per pound of load are presented below for a random sample of four cords. Procedure A Forecast Errors Procedure B Forecast Errors .01 .008 - .01 - .009 - .02 - .008 .02 .03 Using mean - absolute deviation (MAD) and mean - squared error (MSE), evaluate the relative accuracy of each procedure. Which procedure will you use in quality control testing? ANSWER: To find mean - absolute deviation (MAD) we simply sum the absolute values of each forecast error and divide by sample size of four. Accordingly, MAD for Procedure A is .015, whereas the MAD for Procedure B is .01375. Accordingly, using mean - absolute deviation criterion, Procedure B has the lowest average absolute error and therefore is superior. On the other hand, the squared forecast errors are:

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    8 Procedure A Squared Errors Procedure B Squared Errors .0001 .000064 .0001 .000081 .0004 .000064 .0004 .0009 SUM = .001 SUM = .0011 MSE = .00025 MSE = .000275 Accordingly, under mean - squared error (MSE) criterion, Procedure A has the lowest average squared error and there is superior. The quality control manager thus faces a dilemma: Under MAD Procedure B is superior, under MSE Procedure A is superior. What should the manager do? Ultimately this depends on the relative costs of large versus small forecast errors underlying the accuracy measures. We suspect that large errors are more costly to the firm then small, and accordingly apply the MSE measure and conclude that Procedure A is superior.

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    1 Chapter 2 MULTIPLE CHOICE TEST BANK Note: The correct answer is denoted by **. 1. Why are forecasting textbooks full of applied statistics? A) Statistics is the study of uncertainty. B) Real - world business decisions involve risk and uncertainty. C) Forecasting attempts to reduce the uncertainty for uncertain events. D) Forecasting ultimately deals with probability. E) All the above. ** 2. Which of the following is not part of the recommended nine - step forecast process? A) What role do forecasts play in the business decision process? B) What exactly is to be forecast? C) How urgent is the forecast? D) Is there enough data? E) All the above are parts of the process . ** 3. Of the following model selection criteria, which is often the most important in determining the appropriate forecast method? A) Technical background of the forecast user. B) Patterns the data have exhibited in the past. ** C) How much money is in the forecast budget? D) What is the forecast horizon? E) When is the forecast needed? 4. In a time series plot of a typical The GAP store which of the following data patterns would be most prominent ? A) Trend. B) Seasonal. ** C) Cyclical. D) Random. E) All the above. 5. Which of the following is incorrect ? A) The forecaster should be able to defend why a particular model or procedure has been chosen. B) Forecast errors should be discussed in an objective manner to maximize management’s confidence in the forecast process.

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    2 C) Forecast errors should not be discussed since most people know that forecasting is an inexact science. ** D) You should tailor your presentation to the sophistication of the audience to maximize credibility in the forecast process. E) None of the above. 6. In the model - testing phase of the nine - step process, which of the following refers to that portion of a sample that is best to use to evaluate model - forecast accuracy? A) Fit. B) Forecast horizon. C) Holdout period. ** D) Accuracy. E) None of the above. 7. The text present s a guide to selecting an appropriate forecasting method based on A) data patterns. B) quantity of historical data available. C) forecast horizon. D) quantitative background of the forecast user. E) All the above. ** 8. Which time - series component is said to fluctuate around the long - term trend and is fairly irregular in appearance? A) Trend. B) Cyclical. ** C) Seasonal. D) Irregular. E) None of the above. 9. Forecasting January sales based on the previous month's level of sales is likely to lead to error if the data are _____. A) Stationary. B) Non - cyclical. C) Seasonal. ** D) Irregular. E) None of the above. 10. The difference between seasonal and cyclical components is: A) Duration. B) Source. C) Predictability.

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    3 D) Frequency. E) All the above. ** 11. For which data frequency is seasonality not a problem? A) Daily. B) Weekly. C) Monthly. D) Quarterly. E) Annual. ** 12. One can realistically not expect to find a model that fits any data set perfectly, due to the ____ component of a time series. A) Trend. B) Seasonal. C) Cyclical. D) Irregular. ** E) None of the above. 13. When a time series contains no trend, it is said to be A) nonstationary. B) seasonal. C) nonseasonal. D) stationary. ** E) filtered. 14. Stationarity refers to A) the size of the RMSE of a forecasting model. B) the size of variances of the model's estimates. C) a method of forecast optimization. D) lack of trend in a given time series. ** E) None of the above. 15. Which of the following is not a measure of central tendency in a population? A) Mean. B) Mode. C) Median. D) Range. ** 16. Which of the following is not a descriptive statistic? A) Median .

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    4 B) Mean. C) Range. D) Variance. E) None of the above. ** 17. Which of the following is not an important part of classical statistics? A) Summary measures of probability distributions called descriptive statistics. B) Probability distribution functions, which characterize all outcomes of a variable. C) The use of sampling distributions, which describe the uncertainty in making inference about the population on the basis of a sample. D) The concept of expected value. E) All of the above are important . ** 18. The standard normal probability table A) is equivalent to a t distribution if the sample size is less than 30. B) shows a normal distribution with standard deviation equal to zero. C) is used to make inference for all normally distributed random variables. ** D) All the above. E) None of the above. 19. The median and mode may be more accurate than the sample mean in forecasting the populations mean when A) The sample size is small. B) The sample size is large. C) The sample has one large outlier. ** D) The population is assumed to be normally distributed. E) All the above. 20. The arithmetic average of the occurrence of some random variable is also called the _____. A) Range. B) Mean. ** C) Variance. D) Standard deviation. E) None of the above. 21. In finance, an investor who ignores risk is termed "risk neutral." What descriptive statistic is our risk neutral investor ignoring when she generates stock portfolios? A) Median. B) Mean. C) Mode.

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    5 D) Standard deviation. ** E) None of the above. 22. In calculating the sample variance we subtract one from the sample size. This is because A) the population mean is unknown. B) of using the sample mean to estimate the population mean. C) the sum of deviations about the sample mean is zero. D) the sample mean is employed. E) All the above. ** 23. Which statistic is correctly interpreted as the "average" spread of data about the mean? A) Mode. B) Range. C) Variance. D) Standard deviation. ** E) Mean. 24. Which measure of dispersion in a data set is the most intuitive and represents an average? A) Range. B) Mode. C) Standard deviation. ** D) Variance. E) Mean. 25. Which of the following is not an attribute of a normal probability distribution? A) It is symmetrical about the mean. B) Most observations cluster around the mean. C) Most observations cluster around zero. ** D) The distribution is completely determined by the mean and variance. E) All the above are correct. 26. Which of the following is not a foundation of classical statistics? A) Summary measures of probability distribution called descriptive statistics. B) Probability distribution function which characterize all possible outcomes of a random variable. C) The knowledge of thousands and thousands of normal probability tables required for statistical inference of normally distributed random variables. ** D) The concept of expected value, which is the average value of a random variable taken over a large number of samples.

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    6 27. A company claims that the rubber belts, which it manufactures, have a mean service life of at least 800 hours. A random sample of 36 belts from a very large shipment of the company's belts shows a mean life of 760 hours and a standard deviation of 90 hours. Which of the following is the most appropriate on the basis of the sample results? A) The sample results do not warrant rejection of the company's claim if the risk of a Type I error is specified at .05. B) The sample results do warrant rejection of the company's claim if the risk of Type I error is specified at .05. ** C) Since the sample mean falls below the company's claim, the sample results indicate that the company claim is incorrect. D) The sample results are indeterminate since the magnitude of the sample standard deviation is greater than the difference between the company's claimed figure and the sample mean. 28. Based upon ten years of monthly data, the monthly rate of return of the DOW Jones 30 composite stock portfolio was normally distributed with mean .0084 and variance .0014. What is the probability, that in any given month, we observe a rate of return on the DOW above 10 percent? A) Less than one percent. ** B) Two percent. C) Three percent. D) Not enough information is provided to answer the question. 29. Suppose you observe the entire population of a random variable and you wish to test some hypothesis about the mean. To perform your hypothesis test you A) apply a sampling distribution to the problem. B) obtain sample estimates of population parameters. C) simply find the population mean and compare it to the hypothesized value. ** D) Apply the t distribution. E) There is no answer to this question. 30. If two large random samples are drawn from two populations, each having a mean of $100, the relevant sampling distribution of their difference has a mean of: A) $200. B) The sum of the two sample means. C) 0. ** D) The difference between the two sample means. 31. The sampling distribution of the sample mean, when sampling from a normal population with mean  and variance  2 is: A) normally distributed with mean  and variance  2 .

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    7 B) normally distributed with mean  and variance s 2 . C) normally distributed with mean  and variance  2 /n. ** D) normally distributed with mean 0 and variance 1. 32. Type I error A) is said to arise when we reject a true nu ll hypothesis. B) has a probability value equal to the significance level of any statistical test. C) is a measure of the uncertainty associated with rejecting any null hypothesis on the basis of sample data. D) both a) and b) are correct. E) A, B, and C are all correct . ** 33. The sampling distribution of the sample mean is A) normally distributed with mean  and variance  2 . B) normally distributed with mean  and variance s 2 . C) distributed as a t distribution with variance 1. D) normally distributed with mean 0 and variance 1. E) None of the above. ** 34. Sampling distributions A) are the distribu tions of all possible values of a sample statistic based upon repeated sampling. B) are used to make inference when the population of a variable is unobservable. C) exhibit important properties for the ranking of alternative estimators such as unbiasedness and efficiency. D) All the above. ** 35. The null hypothesis is that there is no significant linear relationship between prices and sales. Type I error is: A) To conclude that there is a significant linear relationship when there is. B) To conclude that there is not a significant linear relationship when there is. C) To conclude that there is a significant linear relationship when there is not. ** D) To conclude that the correlation coefficient is equal to zero. 36. An unbiased model A) is one that does not consistently over - estimate or under - estimate the true value of a parameter. ** B) is one that consistently produces estimates with the smallest RMSE. C) is one, which contains no independent variable; it depends solely on time - series pattern recognition. D) is one made up by a team of forecasters.

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