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Introductory regression analysis : with computer application for business and economics /
Regression analysis is arguably the single most powerful and widely applicable tool in any effective examination of common business issues. Every day, decision-makers face problems that require constructive actions with significant consequences, and regression procedures can prove a meaningful and v...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Taylor and Francis,
2013.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; INTRODUCTORY REGRESSION ANALYSIS; Title Page; Copyright Page; Table of Contents; Preface; Chapter 1: A Review of Basic Concepts; Introduction; 1.1 The Importance of Making Systematic Decisions; 1.2 The Process of Statistical Analysis; Data Collection; Organizing the Data; Analyzing the Data; Interpreting the Results; Prediction and Forecasting; 1.3 Our "Arabic" Number System; 1.4 Some Basic Definitions; Populations and Samples; Sampling Error; Sources of Sampling Error: Sampling Bias and Plain Bad Luck; A Sampling Distribution; Types of Variables
- 1.5 Levels of Data Measurement Nominal Data; Ordinal Data; Interval Data; Ratio Data; 1.6 Properties of Good Estimators; A Good Estimator Is Unbiased; A Good Estimator Is Efficient; A Good Estimator Is Consistent; A Good Estimator Is Sufficient; 1.7 Other Considerations; 1.8 Probability Distributions; 1.9 The Development and Application of Models; 1.10 "In God We Trust-Everybody Else Has to Bring Data"; Chapter Problems; Appendix: Excel Commands and Common Probability Distributions; The Normal Distribution; Student's t-Distribution; The F-Distribution
- The Chi-Square DistributionChapter 2: An Introduction to Regression and Correlation Analysis; Introduction; 2.1 The Simple Regression Model; 2.2 Estimating the Model: Ordinary Least Squares; Multiple Regression: A Look Ahead; Calculating the Residuals; 2.3 Why the Process Is Called Ordinary Least Squares; 2.4 Properties and Assumptions of the OLS Model; 2.5 The Gauss-Markov Theorem; 2.6 Measures of Goodness of Fit; The Standard Error of the Estimate; The Coefficient of Determination; How r2 Can Be Used as a Measure of Goodness of Fit; 2.7 Limitations of Regression and Correlation