This top-selling book presents, in a straightforward, application-driven manner, the basic statistical techniques necessary for preparing individual business forecasts and long-range plans. The emphasis is on the application of techniques by management for decision-making. This essential book provides understandable coverage of several important topics, often omitted from other books, including econometrics; autocorrelation analysis and the use of Box-Jenkins techniques; judgmental forecasting techniques; and the means of selecting the correct forecasting technique and analyzing data. The book also reviews statistical concepts prior to introducing material that requires an understanding of those concepts. The sixth edition of Business Forecasting has been revised to include instructions on using Excel spreadsheets and the statistical package MINITAB in forecasting. An essential reference for every professional in a business of any size, from large corporations to small family-run firms.
72 pages matching forecasting model in this book
Results 1-3 of 72
What people are saying - Write a review
We haven't found any reviews in the usual places.
A REVIEW OF BASIC STATISTICAL CONCEPTS
21 other sections not shown
accompanying table adjusted annual autocorrelation coefficients autoregressive Box-Jenkins Bump's calculated Chapter chi-square component correlation coefficient correlation matrix CT scans cyclical data points decision degrees of freedom dependent determine dollars Durbin-Watson statistic electric range sales error of estimate error terms example expected value explained exponential smoothing forecast error forecasting methods forecasting model forecasting process gallons increase independent variables indicates Lags Autocorrelations least squares linear Minitab monthly months moving average multiple regression new-car registrations null hypothesis number of visitors observations packages parameters partial autocorrelations pattern percentage period personal income population prediction predictor variables problem procedure random sample regression analysis regression coefficient regression equation regression line relationship residuals sample statistic sampling distribution scatter diagram selected serial correlation Sibyl/Runner smoothing constant standard deviation standard error statistical step SUM OF SQUARES trend equation trend estimate variance variation weights