Buch, Englisch, 496 Seiten, Format (B × H): 203 mm x 254 mm, Gewicht: 1058 g
Buch, Englisch, 496 Seiten, Format (B × H): 203 mm x 254 mm, Gewicht: 1058 g
ISBN: 978-0-415-89933-8
Verlag: Routledge
Taking into consideration current statistical technology, Introductory Regression Analysis focuses on the use and interpretation of software, while also demonstrating the logic, reasoning, and calculations that lie behind any statistical analysis. Furthermore, the text emphasizes the application of regression tools to real-life business concerns. This multilayered, yet pragmatic approach fully equips students to derive the benefit and meaning of a regression analysis.
This text is designed to serve in a second undergraduate course in statistics, focusing on regression and its component features. The material presented in this text will build from a foundation of the principles of data analysis. Although previous exposure to statistical concepts would prove helpful, all the material needed for an examination of regression analysis is presented here in a clear and complete form.
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik EDV | Informatik Business Application Tabellenkalkulation Microsoft Excel
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
- Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
- Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
Weitere Infos & Material
1. Review of Basic Concepts 2. An Introduction to Regression and Correlation Analysis 3. Statistical Inferences in the Simple Regression Model 4. Multiple Regression: Using Two or More Predictor Variables 5. Residual Analysis and Model Specification 6. Using Qualitative and Limited Dependent Variables 7. Heteroscedasticity 8. Autocorrelation 9. Non-Linear Regression and the Selection of the Proper Functional Form 10. Simultaneous Equations: Two Stage Least Squares 11. Forecasting with Time Series Data and Distributed Lag Models