Study Guide Chapter 3 TM 745 First Exam Spring 2008 Know the assumptions and definitions of the The exam is open book, open notes. Calculators are moving average and exponential smoothing. allowed. Students may have up to three hours for Particularly know that they do not include the exam. The exam covers chapters 1-4. trend, seasonal, or cyclical components. And, know the formulas for computation. There are a few calculations, but most questions are multiple choice. Be familiar with methods of choosing smoothing constants. Chapter 1 Be familiar with the assumptions for Holtz Be familiar with the characteristics of subjective forecasts.method, Winter’s Method, and Adaptive Be able to contrast Sales Force Composites, Customer Response Single Exponential smoothing. Surveys, Jury of Executive Opinion, and the Delphi Method Know that first deseasonalizing the original data, then reseasonalizing the forecasts can Know the similarity of RMSE and the standard allow moving averages and exponential deviation. Know how RMSE is influenced by large soothing to be used more generally. and small errors in the model fit. Know the usage of the term diffusion model in ForecastX. Chapter 2 Know that common usages of event models Know how to apply Table 2.1 in selecting and and indices include various advertising comparing forecasting methods, and what methods. characteristics it includes. Know how to interpret event and seasonal Be familiar with the GAP data in the integrative indices, and the meaning of the indices being example and its complexity including trend and positive or negative. seasonality. Chapter 4 Be able to answer questions involving definitions of time series components: Know the technical meaning of the least Trend, Seasonal, Cyclical and Irregular. squares and the definition of the term residuals. Know how to apply the book’s definition of a stationary series. Know the relationship between sign of the correlation coefficient and the sign of the slop Know the meaning of a zero and negative of the regression line. correlation coefficient. Recall that a parabola can be fit with a zero correlation. Recall that the Ascombe examples gave the same fitted line and correlation coefficient Be familiar with the definition of autocorrelation. from many different data sets. Know how to apply seasonsal indices, including when to multiple and divide in deseasonalizing the original data, then reseasonalizing the forecasts. Know the usage of R-squared and interpreting its bounds 0 and 1, and as a portion of variability. Generally, know how to interpret a Durbin-Watson (DW) statistic. Separated Calculation problems Know how to compute a Z-table and a t-table confidence interval. Know how to compute moving averages and exponential smoothing models.
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