## With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

No credit card required ## Book Description

Essential Statistics, Regression, and Econometrics provides students with a readable, deep understanding of the key statistical topics they need to understand in an econometrics course. It is innovative in its focus, including real data, pitfalls in data analysis, and modeling issues (including functional forms, causality, and instrumental variables). This book is unusually readable and non-intimidating, with extensive word problems that emphasize intuition and understanding. Exercises range from easy to challenging and the examples are substantial and real, to help the students remember the technique better.

• Readable exposition and exceptional exercises/examples that students can relate to

• Website includes java applets and Excel applications

• Focuses on key methods for econometrics students without including unnecessary topics

• Covers data analysis not covered in other texts

• Ideal presentation of material (topic order) for econometrics course

1. Cover Page
2. Title Page
4. Introduction
5. Chapter 1: Data, Data, Data
1. 1.1 Measurements
2. 1.2 Testing Models
3. 1.3 Making Predictions
4. 1.4 Numerical and Categorical Data
5. 1.5 Cross-Sectional Data
6. 1.6 Time Series Data
7. 1.7 Longitudinal (or Panel) Data
8. 1.8 Index Numbers (Optional)
9. 1.9 Deflated Data
10. Exercises
6. Chapter 2: Displaying Data
1. 2.1 Bar Charts
2. 2.2 Histograms
3. 2.3 Time Series Graphs
4. 2.4 Scatterplots
5. 2.5 Graphs: Good, Bad, and Ugly
6. Exercises
7. Chapter 3: Descriptive Statistics
1. 3.1 Mean
2. 3.2 Median
3. 3.3 Standard Deviation
4. 3.4 Boxplots
5. 3.5 Growth Rates
6. 3.6 Correlation
7. Exercises
8. Chapter 4: Probability
1. 4.1 Describing Uncertainty
2. 4.2 Some Helpful Rules
3. 4.3 Probability Distributions
4. Exercises
9. Chapter 5: Sampling
1. 5.1 Populations and Samples
2. 5.2 The Power of Random Sampling
3. 5.3 A Study of the Break-Even Effect
4. 5.4 Biased Samples
5. 5.5 Observational Data versus Experimental Data
6. Exercises
10. Chapter 6: Estimation
1. 6.1 Estimating the Population Mean
2. 6.2 Sampling Error
3. 6.3 The Sampling Distribution of the Sample Mean
4. 6.4 The t Distribution
5. 6.5 Confidence Intervals Using the t Distribution
6. Exercises
11. Chapter 7: Hypothesis Testing
1. 7.1 Proof by Statistical Contradiction
2. 7.2 The Null Hypothesis
3. 7.3 P Values
4. 7.4 Confidence Intervals
5. 7.5 Matched-Pair Data
6. 7.6 Practical Importance versus Statistical Significance
7. 7.7 Data Grubbing
8. Exercises
12. Chapter 8: Simple Regression
1. 8.1 The Regression Model
2. 8.2 Least Squares Estimation
3. 8.3 Confidence Intervals
4. 8.4 Hypothesis Tests
5. 8.5 R2
6. 8.6 Using Regression Analysis
7. 8.7 Prediction Intervals (Optional)
8. Exercises
13. Chapter 9: The Art of Regression Analysis
1. 9.1 Regression Pitfalls
2. 9.2 Regression Diagnostics (Optional)
3. Exercises
14. Chapter 10: Multiple Regression
1. 10.1 The Multiple Regression Model
2. 10.2 Least Squares Estimation
3. 10.3 Multicollinearity
4. Exercises
15. Chapter 11: Modeling (Optional)
1. 11.1 Causality
2. 11.2 Linear Models
3. 11.3 Polynomial Models
4. 11.4 Power Functions
5. 11.5 Logarithmic Models
6. 11.6 Growth Models
7. 11.7 Autoregressive Models
8. Exercises
16. Appendix
17. References
18. Index
19. Footnotes