Forecasting and Time Series: An Applied Approach
This book illustrates the importance of forecasting and the various statistical techniques that can be used to produce forecasts. Bruce L. Bowerman and Richard T. O'Connell clearly demonstrate the necessity of using forecasts to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, and strategic management.
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Absorbent Paper Towel ARIMA average hourly temperature Box-Jenkins model calculate compute Conditional Least Squares confidence interval consider curve degrees of freedom denote diagnostic checking differencing discuss double exponential smoothing dummy variable error terms example F-distribution forecast errors independent variables Least Squares Estimation least squares point linear regression model mean method Minitab output model describing model of order monthly moving average model multicollinearity nonseasonal moving average normally distributed obtained output of estimation Paper Towel sales Partial Autocorrelations period point forecast point prediction population prediction interval presents the SAS prob-value random shock regression analysis regression model reject H0 room averages RSAC RSPAC SAC and SPAC sample SAS output seasonal variation series values setting a equal simple linear regression smoothing constant spike at lag squares point estimates standard deviation standard error stationary time series statistic tentatively identify transformation trend Type I error updated variance versus zero