Essential Statistics, Regression, and Econometrics (eBook)

Gary Smith (Autor)

eBook Download: PDF | EPUB

2011 | 1. Auflage
394 Seiten
Elsevier Science (Verlag)
978-0-12-382222-2 (ISBN)

Infos

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

Gary Smith received his B.S. in Mathematics from Harvey Mudd College and his PhD in Economics from Yale University. He was an Assistant Professor of Economics at Yale University for seven years. He is currently the Fletcher Jones Professor of Economics at Pomona College. He has won two teaching awards and has written (or co-authored) seventy-five academic papers, eight college textbooks, and two trade books (most recently, Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie With Statistics, Overlook/Duckworth, 2014). His research has been featured in various media including the New York Times, Wall Street Journal, Motley Fool, NewsWeek and BusinessWeek. For more information visit www.garysmithn.com.

Inhalt

Front Cover 1
Essential Statistics, Regression,and Econometrics 4
Copyright 5
Table of Contents 6
Introduction 12
Chapter 1. Data, Data, Data 14
1.1 Measurements 15
1.2 Testing Models 17
1.3 Making Predictions 18
1.4 Numerical and Categorical Data 19
1.5 Cross-Sectional Data 19
1.6 Time Series Data 21
1.7 Longitudinal (or Panel) Data 23
1.8 Index Numbers (Optional) 23
1.9 Deflated Data 25
Exercises 30
Chapter 2. Displaying Data 40
2.1 Bar Charts 40
2.2 Histograms 47
2.3 Time Series Graphs 56
2.4 Scatterplots 60
2.5 Graphs: Good, Bad, and Ugly 64
Exercises 72
Chapter 3. Descriptive Statistics 86
3.1 Mean 86
3.2 Median 88
3.3 Standard Deviation 89
3.4 Boxplots 90
3.5 Growth Rates 93
3.6 Correlation 98
Exercises 103
Chapter 4. Probability 114
4.1 Describing Uncertainty 115
4.2 Some Helpful Rules 121
4.3 Probability Distributions 132
Exercises 144
Chapter 5. Sampling 154
5.1 Populations and Samples 155
5.2 The Power of Random Sampling 156
5.3 A Study of the Break-Even Effect 159
5.4 Biased Samples 162
5.5 Observational Data versus Experimental Data 165
Exercises 167
Chapter 6. Estimation 176
6.1 Estimating the Population Mean 177
6.2 Sampling Error 178
6.3 The Sampling Distribution of the Sample Mean 179
6.4 The t Distribution 185
6.5 Confidence Intervals Using the t Distribution 187
Exercises 192
Chapter 7. Hypothesis Testing 202
7.1 Proof by Statistical Contradiction 203
7.2 The Null Hypothesis 204
7.3 P Values 205
7.4 Confidence Intervals 210
7.5 Matched-Pair Data 211
7.6 Practical Importance versus Statistical Significance 214
7.7 Data Grubbing 216
Exercises 221
Chapter 8. Simple Regression 232
8.1 The Regression Model 232
8.2 Least Squares Estimation 237
8.3 Confidence Intervals 239
8.4 Hypothesis Tests 241
8.5 R2 243
8.6 Using Regression Analysis 247
8.7 Prediction Intervals (Optional) 251
Exercises 253
Chapter 9. The Art of Regression Analysis 272
9.1 Regression Pitfalls 273
9.2 Regression Diagnostics (Optional) 287
Exercises 293
Chapter 10. Multiple Regression 310
10.1 The Multiple Regression Model 311
10.2 Least Squares Estimation 316
10.3 Multicollinearity 323
Exercises 325
Chapter 11. Modeling (Optional) 346
11.1 Causality 346
11.2 Linear Models 347
11.3 Polynomial Models 348
11.4 Power Functions 350
11.5 Logarithmic Models 357
11.6 Growth Models 358
11.7 Autoregressive Models 363
Exercises 366
Appendix 378
References 380
Chapter 1 Data, Data, Data 380
Chapter 2 Displaying Data 380
Chapter 3 Descriptive Statistics 381
Chapter 4 Probability 382
Chapter 5 Sampling 383
Chapter 6 Estimation 384
Chapter 7 Hypothesis Testing 385
Chapter 8 Simple Regression 386
Chapter 9 The Art of Regression Analysis 386
Chapter 10 Multiple Regression 387
Chapter 11 Modeling (Optional) 388
Index 390

Produkt-Details

• Erscheint lt. Verlag: 21.5.2011
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Technik
• Themenwelt: Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie
• ISBN-10: 0-12-382222-X / 012382222X
• ISBN-13: 978-0-12-382222-2 / 9780123822222