Essential Statistics, Regression, and Econometrics
- 1st Edition - June 22, 2011
- Author: Gary Smith
- Language: English
Essential Statistics, Regression, and Econometrics provides students with a readable, deep understanding of the key statistical topics they need to understand in an econometrics co… Read more
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
- 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
Essential Statistics, Regression, and Econometrics is for an introductory non-calculus based statistics course offered in business/finance/psychology departments for undergraduate students of any major who take a term course in basic Statistics or a year course in Probability and Statistics.
Introduction
Chapter 1. Data, Data, Data
1.1 Measurements
1.2 Testing Models
1.3 Making Predictions
1.4 Numerical and Categorical Data
1.5 Cross-Sectional Data
1.6 Time Series Data
1.7 Longitudinal (or Panel) Data
1.8 Index Numbers (Optional)
1.9 Deflated Data
Chapter 2. Displaying Data
2.1 Bar Charts
2.2 Histograms
2.3 Time Series Graphs
2.4 Scatterplots
2.5 Graphs: Good, Bad, and Ugly
Chapter 3. Descriptive Statistics
3.1 Mean
3.2 Median
3.3 Standard Deviation
3.4 Boxplots
3.5 Growth Rates
3.6 Correlation
Chapter 4. Probability
4.1 Describing Uncertainty
4.2 Some Helpful Rules
4.3 Probability Distributions
Chapter 5. Sampling
5.1 Populations and Samples
5.2 The Power of Random Sampling
5.3 A Study of the Break-Even Effect
5.4 Biased Samples
5.5 Observational Data versus Experimental Data
Chapter 6. Estimation
6.1 Estimating the Population Mean
6.2 Sampling Error
6.3 The Sampling Distribution of the Sample Mean
6.4 The t Distribution
6.5 Confidence Intervals Using the t Distribution
Chapter 7. Hypothesis Testing
7.1 Proof by Statistical Contradiction
7.2 The Null Hypothesis
7.3 P Values
7.4 Confidence Intervals
7.5 Matched-Pair Data
7.6 Practical Importance versus Statistical Significance
7.7 Data Grubbing
Chapter 8. Simple Regression
8.1 The Regression Model
8.2 Least Squares Estimation
8.3 Confidence Intervals
8.4 Hypothesis Tests
8.5 R2
8.6 Using Regression Analysis
8.7 Prediction Intervals (Optional)
Chapter 9. The Art of Regression Analysis
9.1 Regression Pitfalls
9.2 Regression Diagnostics (Optional)
Chapter 10. Multiple Regression
10.1 The Multiple Regression Model
10.2 Least Squares Estimation
10.3 Multicollinearity
Chapter 11. Modeling (Optional)
11.1 Causality
11.2 Linear Models
11.3 Polynomial Models
11.4 Power Functions
11.5 Logarithmic Models
11.6 Growth Models
11.7 Autoregressive Models
Appendix
References
Index
- Edition: 1
- Published: June 22, 2011
- Language: English
GS
Gary Smith
Gary Smith received his Ph.D. in Economics from Yale University and was an Assistant Professor there for seven years. He has won two teaching awards and written (or co-authored) more than 100 academic papers and 20 books. His Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics (Overlook/Duckworth, 2015) was a London Times Book of the Week and has been translated into Chinese, Japanese, Korean, and Turkish. The AI Delusion (Oxford University Press, 2018) argues that, in this age of Big Data, the real danger is not that computers are smarter than us, but that we think computers are smarter than us and, so, trust computers to make important decisions they should not be trusted to make. The 9 Pitfalls of Data Science (Oxford University Press, 2019, co-authored with Jay Cordes), won the PROSE award for Excellence in Popular Science & Popular Mathematics. His statistical and financial research has been featured in various media, including The New York Times, Wall Street Journal, Wired, NPR Tech Nation, NBC Bay Area, CNBC, WYNC, WBBR Bloomberg Radio, NBC Think, Silicon Valley Insider, Motley Fool, Scientific American, Forbes, MarketWatch, MoneyCentral.msn, NewsWeek, Fast Company, The Economist, MindMatters, OZY, Slate, and BusinessWeek.
Affiliations and expertise
Fletcher Jones Professor, Department of Economics, Pomona College, Claremont, CA, USARead Essential Statistics, Regression, and Econometrics on ScienceDirect