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Gujarati's "Basic Econometrics" provides an elementary but comprehensive introduction to econometrics without resorting to matrix algebra, calculus, or statistics beyond the elementary level. Because of the way the book is organized, it may be used at a variety of levels of rigor. For example, if matrix algebra is used, theoretical exercises may be omitted. A CD of data sets is provided with the text.
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Table of Contents

Introduction Part I -- Single-Equation Regression Models 1. The Nature of Regression Analysis 2. Two-Variable Regression Analysis: Some Basic Ideas 3. Two Variable Regression Model: The Problem of Estimation 4. Classical Normal Linear Regression Model (CNLRM) 5. Two-Variable Regression: Interval Estimation and Hypothesis Testing 6. Extensions of the Two-Variable Linear Regression Model 7. Multiple Regression Analysis: The Problem of Estimation 8. Multiple Regression Analysis: The Problem of Inference 9. Dummy Variable Regression Models Part 2: Relaxing Assumptions of the Classical Model 10. Multicollinearity: What Happens if the Regressions are Correlated? 11. Heteroscedasticity: What Happens if the Error Variance is Nonconstant? 12. Autocorrelation: What Happens if the Error Terms are Correlated? 13. Econometric Modeling I: Model Specification and Diagnostic Testing? Part 3: Topics in Econometrics 14. Nonlinear Regression Models 15. Qualitative Response Regression Models 16. Panel Data Regression Models 17. Dynamic Econometric Model: Autoregressive and Distributed Lag Models Part 4: Simultaneous Equation Models 18. Simultaneous-Equation Models 19. The Identification Problem 20. Simultaneous-Equation Methods Part 5: Time Series Econometrics 21. Time Series Econometrics: Some Basic Concepts 22. Time Series Econometrics: Forecasting Appendixes A. A Review of Some Statistical Concepts B. Rudiments of Matrix Algebra C. The Matrix Approach to the Linear Regression Model D. Statistical Tables Selected Bibliography Indexes Name Index Subject Index

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