Forecasting, Time Series, and Regression: An Applied ApproachAwarded Outstanding Academic Book by CHOICE magazine in its first edition, FORECASTING, TIME SERIES, AND REGRESSION: AN APPLIED APPROACH illustrates the vital importance of forecasting and the various statistical techniques that can be used to produce them. With an emphasis on applications, this book provides both the conceptual development and practical motivation you need to effectively implement forecasts of your own. You'll understand why using forecasts to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, and strategic management is so vital. |
Common terms and phrases
average hourly temperature b₁ B₁x B₁x₁ B₂ Box-Jenkins Box-Jenkins model calculate Coef compute confidence interval consider correlation degrees of freedom denote dummy variable Durbin-Watson statistic equation error term example Excel output Exercise exponential smoothing F(model forecast errors forecasting method H₁ hypothesis independent variables least squares point linear regression model mean weekly fuel mileages MINITAB output monthly moving average natural gas nonseasonal normal curve normally distributed output in Figure p-value parameters Partial Autocorrelations point forecast point prediction prediction interval R-Sq regression analysis rejection point residual plot sales volume sample mean SAS output seasonal factors seasonal variation Section series values setting a equal simple linear regression SPAC squares point estimates ẞo standard deviation standard error statistic Std Error t-distribution t-statistic trend Type I error unexplained variation upkeep expenditure variance weekly fuel consumption x₁ y-intercept y₁ Z₁