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Exploring Data Patterns and Choosing a Forecasting Technique
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Business Forecasting Study Set 1

Business

Quiz 2 :
Exploring Data Patterns and Choosing a Forecasting Technique

Quiz 2 :
Exploring Data Patterns and Choosing a Forecasting Technique

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Explain the differences between qualitative and quantitative forecasting techniques.
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Qualitative forecasting techniques:
The qualitative forecasting technique depends on human judgment and examination of clients with the ranking scale which are subjective. In other words, prior data is not available. The qualitative forecasting techniques are listed below:
• Executive opinions
• Delphi technique
• Sales force pooling
• Consumes surveys
Quantitative forecasting techniques:
The above technique forecast the future data when past data is available. This method is based on the historical data. The quantitative forecasting techniques are listed below:
• Naive methods
• Moving average methods
• Exponential smoothing average methods
• Trend analysis
• Decomposition of time series
Differences between qualitative and quantitative forecasting techniques are tabulated below:
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What is a time series?
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Time series:
The data that are collected, recorded, or observed over successive increments of time is termed as time series. In other words, the time series is calculated over a time interval. That is, the data, which can be measured in year, week, month etc, is a time series data. Moreover, the time-series plot explains how the value of a variable has changed over time.

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Describe each of the components in a time series.
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The components of the time series model are,
• Trend component
• Cyclical component
• Irregular component
• Seasonal component
Trend component indicates the overall long term upward or downward pattern of tendency in the time series.
The cyclical component shows the repeating up and down movements through all phases of the time series.
Irregular component data do not follow the trend modified by the cyclic components and the data have only random fluctuations in the series.
When the data have periodic fluctuations and that data are recorded monthly or quarterly, then the time series represents Seasonal component.

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What is autocorrelation?
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What does an autocorrelation coefficient measure?
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Describe how correlograms are used to analyze autocorrelations for various lags of a time series.
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Indicate whether each of the following statements describes a stationary or a non-stationary series. a. A series that has a trend b. A series whose mean and variance remain constant over time c. A series whose mean value is changing over time d. A series that contains no growth or decline
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Descriptions are provided for several types of series: random, stationary, trending, and seasonal. Identify the type of series that each describes. a. The series has basic statistical properties, such as the mean and variance, that remain constant over time. b. The successive values of a time series are not related to each other. c. A high relationship exists between each successive value of a series. d. A significant autocorrelation coefficient appears at time lag 4 for quarterly data. e. The series contains no growth or decline. f. The autocorrelation coefficients are typically significantly different from zero for the first several time lags and then gradually decrease toward zero as the number of lags increases.
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List some of the forecasting techniques that should be considered when forecasting a stationary series. Give examples of situations in which these techniques would be applicable.
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List some of the forecasting techniques that should be considered when forecasting a trending series. Give examples of situations in which these techniques would be applicable.
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List some of the forecasting techniques that should be considered when forecasting a seasonal series. Give examples of situations in which these techniques would be applicable.
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List some of the forecasting techniques that should be considered when forecasting a cyclical series. Give examples of situations in which these techniques would be applicable.
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The number of marriages in the United States is given in Table P-13. Compute the first differences for these data. Plot the original data and the difference data as a time series. Is there a trend in either of these series? Discuss. TABLE P-13 img Source: Based on Statistical Abstract of the United States, various years.
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Compute the 95% confidence interval for the autocorrelation coefficient for time lag 1 for a series that contains 80 terms.
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Which measure of forecast accuracy should be used in each of the following situations? a. The analyst needs to determine whether a forecasting method is biased. b. The analyst feels that the size or magnitude of the forecast variable is important in evaluating the accuracy of the forecast. c. The analyst needs to penalize large forecasting errors.
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Which of the following statements is true concerning the accuracy measures used to evaluate forecasts? a. The MAPE takes into consideration the magnitude of the values being forecast. b. The MSE and RMSE penalize large errors. c. The MPE is used to determine whether a model is systematically predicting too high or too low. d. The advantage of the MAD method is that it relates the size of the error to the actual observation.
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Allie White, the chief loan officer for the Dominion Bank, would like to analyze the bank's loan portfolio for the years 2001 to 2006. The data are shown in Table P-17. a. Compute the autocorrelations for time lags 1 and 2. Test to determine whether these autocorrelation coefficients are significantly different from zero at the.05 significance level. b. Use a computer program to plot the data and compute the autocorrelations for the first six time lags. Is this time series stationary? TABLE P-17 Quarterly Loans ($ millions) for Dominion Bank, 2001-2006 img Source: Based on Dominion Bank records.
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This question refers to Problem 17. Compute the first differences of the quarterly loan data for Dominion Bank. a. Compute the autocorrelation coefficient for time lag 1 using the differenced data. b. Use a computer program to plot the differenced data and compute the autocorrelations for the differenced data for the first six time lags. Is this time series stationary?
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Analyze the autocorrelation coefficients for the series shown in Figures 3-18 through 3-21. Briefly describe each series. FIGURE 3-18 Autocorrelation Function img FIGURE 3-19 Autocorrelation Function img FIGURE 3-20 Autocorrelation Function img FIGURE 3-21 Autocorrelation Function img
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An analyst would like to determine whether there is a pattern to earnings per share for the Price Company, which operated a wholesale/retail cash-and-carry business in several states under the name Price Club. The data are shown in Table P-20. Describe any patterns that exist in these data. a. Find the forecast value of the quarterly earnings per share for Price Club for each quarter by using the naive approach (i.e., the forecast for first-quarter 1987 is the value for fourth-quarter 1986,.32). b. Evaluate the naive forecast using MAD. c. Evaluate the naive forecast using MSE and RMSE. d. Evaluate the naive forecast using MAPE. e. Evaluate the naive forecast using MPE. TABLE P-20 Quarterly Earnings per Share for Price Club: 1986-1993 Quarter img Source: The Value Line Investment Survey (New York: Value Line, 1994), p. 1646.
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