The intuitive approach of Introduction to Econometrics uses interesting applications to motivate students to learn theory and to help them understand the application of the theory. Students come away with a thorough understanding of econometics and of the relationships on which people, businesses, and governments base their decisions.

In keeping with their successful introductory econometrics text, Stock and Watson motivate each methodological topic with a real-world policy application that uses data, so that students apply the theory immediately. Introduction to Econometrics, Brief Edition, is a streamlined version of their text, including the fundamental topics, an early review of statistics and probability, the core material of regression with cross-sectional data, and a capstone chapter on conducting empirical analysis.

**PART ONE INTRODUCTION AND REVIEW**

**Chapter 1 Economic Questions and Data**

1.1 Economic Questions We Examine

1.2 Causal Effects and Idealized Experiments

1.3 Data: Sources and Types

**Chapter 2 Review of Probability**

2.1 Random Variables and Probability Distributions

2.2 Expected Values, Mean, and Variance

2.3 Two Random Variables

2.4 The Normal, Chi-Squared, Student t, and F Distributions

2.5 Random Sampling and the Distribution of the Sample Average

2.6 Large-Sample Approximations to Sampling Distributions

**Chapter 3 Review of Statistics**

3.1 Estimation of the Population Mean

3.2 Hypothesis Tests Concerning the Population Mean

3.3 Confidence Intervals for the Population Mean

3.4 Comparing Means from Different Populations

3.5 Differences-of-Means Estimation of Causal Effects

3.6 Using the t-Statistic When the Sample Size Is Small

3.7 Scatterplot, the Sample Covariance, and the Sample Correlation Using Experimental Data

**PART TWO FUNDAMENTALS OF REGRESSION ANALYSIS**

**Chapter 4 Linear Regression with One Regressor**

4.1 The Linear Regression Model

4.2 Estimating the Coefficients of the Linear Regression Model

4.3 Measures of Fit

4.5 The Sampling Distribution of the OLS Estimators

4.6 Conclusion

**Chapter 5 Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals**

5.1 Testing Hypotheses About One of the Regression Coefficients

5.2 Confidence Intervals for a Regression Coefficient

5.3 Regression When X Is a Binary Variable

5.5 The Theoretical Foundations of Ordinary Least Squares

5.5 The Theoretical Foundations of Ordinary Least Squares

5.6 Using the t-Statistic in Regression When the Sample Size Is Small

5.7 Conclusion

**Chapter 6 Linear Regression with Multiple Regressors**

6.1 Omitted Variable Bias

6.2 The Multiple Regression Model

6.3 The OLS Estimator in Multiple Regression

6.4 Measures of Fit in Multiple Regression

6.5 The Least Squares Assumptions in Multiple Regression

6.6 The Distribution of the OLS Estimators

6.7 Multicollinearity

6.8 Conclusion

**Chapter 7 Hypothesis Tests and Confidence Intervals in Multiple Regression**

7.1 Hypothesis Tests and Confidence Intervals for a Single Coefficient

7.2 Tests of Joint Hypotheses

7.3 Testing Single Restrictions Involving Multiple Coefficients

7.4 Confidence Sets for Multiple Coefficients

7.6 Analysis of the Test Score Data Set

7.7 Conclusion

**Chapter 8 Nonlinear Regression Functions**

8.1 A General Strategy for Modeling Nonlinear Regression Functions

8.2 Nonlinear Functions of a Single Independent Variable

8.4 Nonlinear Effects on Test Scores of the Student–Teacher Ratio

8.5 Conclusion

**Chapter 9 Assessing Studies Based on Multiple Regression**

9.1 Internal and External Validity

9.2 Threats to Internal Validity of Multiple Regression Analysis

9.3 Internal and External Validity When the Regression Is Used for Forecasting

9.4 Example: Test Scores and Class Size

9.5 Conclusion

**Chapter 10 Conducting a Regression Study Using Economic Data**

10.1 Choosing a Topic

10.2 Collecting the Data

10.3 Conducting Your Regression Analysis

10.4 Writing Up Your Results

REVIEW