| Home > Regression Forecasting Books | Regression Forecasting BooksRecommended books for "Regression Forecasting" |
|
||
|
|
||||||
| Displaying page 1 of 10 | |
| Next | |
 | Forecasting, Time Series, and Regression (with CD-ROM) (Forecasting, Time Series, & Regression) Awarded Outstanding Academic Book by CHOICE magazine in its first edition, FORECASTING, TIME SERIES, AND REGRESSION: AN APPLIED APPROACH now appears in a fourth edition that 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 students need to effectively implement forecasts of their own. Bruce Bowerman, Richard O'Connell, and Anne Koehler clearly demonstrate the necessity of using forecasts to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, and strategic management. In addition, new technology coverage makes the latest edition the most applied text available on the market. List Price: USD $171.95 Author: Bruce L. Bowerman Year: 2004 No. Pages: 672 |
 | Regression Analysis: Modeling & Forecasting An educational book that describes regression procedures for forecast modeling. Author: George C. S. Wang Year: 2003 No. Pages: 293 |
 | Data Analysis, Regression and Forecasting (Managerial Decision Analysis Series) This book contains many classic Harvard cases and offers contemporary concept development. Its low cost makes it an ideal bundle with other Duxbury titles. It is appropriate for short courses in MBA-level statistics and as a supplement in more comprehensive courses. Emphasizing the practice of data analysis, the authors teach the methodology needed to solve a variety of commonly occurring real-world problems that managers encounter daily. Readers learn how to make inferences from limited data, forecast sales in appropriate ways, and avoid potentially disastrous errors of caustic reasoning. List Price: USD $51.95 Author: David E. Bell Year: 1994 No. Pages: 272 |
 | Forecasting with Dynamic Regression Models One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies. List Price: USD $235.00 Author: Alan Pankratz Year: 1991 No. Pages: 400 |
| Micro Tsp for Use With the IBM Personal Computer for Regression and Forecasting List Price: USD $19.95 Author: E. Lilien Year: 1983 No. Pages: 182 |
| Airline Traffic Forecasting: Regression Analysis Approach Author: Nawal K. Taneja Year: 1978 No. Pages: 248 |
 | Business Forecasting w/ ForecastX Business Forecasting with Forecast X, 4/e by Wilson and Keating is a broad-based survey of business forecasting methods including subjective and objective approaches. The focus, however, is on the most proven acceptable methods used commonly in business and government such as regression, smoothing, decomposition, and Box-Jenkins. This exciting new edition integrates the most comprehensive software tool available in this market, Forecast X. This excel-based tool (which received a 4 point out 5 rating from PC Magazine, Oct. 2, 2000 issue) effectively uses wizards and many tools to make forecasting easy and understandable. The user may customize output from the Forecast X package in a myriad of ways. Author: J. Holton Wilson Year: 2001 No. Pages: 512 |
| Forecasting with Excel: regression analysis can help predict revenues and costs.: An article from: Journal of Accountancy This digital document is an article from Journal of Accountancy, published by American Institute of CPA's on February 1, 2009. The length of the article is 1979 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available immediately after purchase. You can view it with any web browser. Citation Details Title: Forecasting with Excel: regression analysis can help predict revenues and costs. Author: James A. Weisel Publication: Journal of Accountancy (Magazine/Journal) Date: February 1, 2009 Publisher: American Institute of CPA's Volume: 207 Issue: 2 Page: 62(6) Distributed by Gale, a part of Cengage Learning List Price: USD $9.95 Author: James A. Weisel Year: 2009 No. Pages: 7 |
![Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches [An article from: European Journal of Operational Research]](http://ecx.images-amazon.com/images/I/51G4P0G7AGL.jpg "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches [An article from: European Journal of Operational Research]") | Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches [An article from: European Journal of Operational Research] This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser. Description: Multiple linear regression (MLR) is a popular method for producing forecasts when data on relevant independent variables (or cues) is available. The accuracy of the technique in forecasting the impact on Greek TV audience shares of programmes showing sport events is compared with forecasts produced by: (1) a simple bivariate regression model, (2) three different types of artificial neural network, (3) three forms of nearest neighbour analysis and (4) human judgment. MLR was found to perform relatively poorly. The application of Theil's bias decomposition and a Brunswik lens decomposition suggested that this was because of its inability to handle complex non-linearities in the relationship between the dependent variable and the cues and its tendency to overfit the in-sample data. Much higher accuracy was obtained from forecasts based on a simple bivariate regression model, a simple nearest neighbour procedure and from two of the types of artificial neural network. List Price: USD $7.95 Author: K. Nikolopoulos Year: 2007 |
![An exploratory study of object-oriented software component size determinants and the application of regression tree forecasting models [An article from: Information & Management]](http://ecx.images-amazon.com/images/I/51X4Z6EY40L.jpg "An exploratory study of object-oriented software component size determinants and the application of regression tree forecasting models [An article from: Information & Management]") | An exploratory study of object-oriented software component size determinants and the application of regression tree forecasting models [An article from: Information & Management] This digital document is a journal article from Information & Management, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser. Description: Software component size estimation is an important task in software project management. For a component-based approach, two steps may be used to estimate the overall size of object-oriented (OO) software: a designer uses metrics to predict the size of the software components and then utilizes the sizes to estimate the overall project size. Using OO software metrics literature, we identified factors that may affect the size of an OO software component. Using real-life data from 152 software components, we then determined the effect of the identified factors on the prediction of OO software component size. The results indicated that certain factors and the type of OO software component play a significant role in the estimate. It is shown how a regression tree data mining approach can be used to learn decision rules to guide future estimates. List Price: USD $8.95 Author: P.C. Pendharkar Year: 2004 |
| Displaying page 1 of 10 | |
| Next | |
| Share |
| Regression Forecasting Business Software |
| Find Excel Templates & Excel Add-ins in the Excel Templates & Add-ins Directory |
| Explore the User Guides to Empower Business Decision Making with Business Spreadsheets |