Smith Essential Statistics, Regression, and Econometrics

1. Auflage 2011
ISBN: 978-0-12-382222-2
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 394 Seiten

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.

Autoren/Hrsg.

Smith, Gary

Inhaltsverzeichnis

1;Front Cover;1
2;Essential Statistics, Regression,and Econometrics;4
3;Copyright;5
4;Table of Contents;6
5;Introduction;12
6;Chapter 1. Data, Data, Data;14
6.1;1.1 Measurements;15
6.2;1.2 Testing Models;17
6.3;1.3 Making Predictions;18
6.4;1.4 Numerical and Categorical Data;19
6.5;1.5 Cross-Sectional Data;19
6.6;1.6 Time Series Data;21
6.7;1.7 Longitudinal (or Panel) Data;23
6.8;1.8 Index Numbers (Optional);23
6.9;1.9 Deflated Data;25
6.10;Exercises;30
7;Chapter 2. Displaying Data;40
7.1;2.1 Bar Charts;40
7.2;2.2 Histograms;47
7.3;2.3 Time Series Graphs;56
7.4;2.4 Scatterplots;60
7.5;2.5 Graphs: Good, Bad, and Ugly;64
7.6;Exercises;72
8;Chapter 3. Descriptive Statistics;86
8.1;3.1 Mean;86
8.2;3.2 Median;88
8.3;3.3 Standard Deviation;89
8.4;3.4 Boxplots;90
8.5;3.5 Growth Rates;93
8.6;3.6 Correlation;98
8.7;Exercises;103
9;Chapter 4. Probability;114
9.1;4.1 Describing Uncertainty;115
9.2;4.2 Some Helpful Rules;121
9.3;4.3 Probability Distributions;132
9.4;Exercises;144
10;Chapter 5. Sampling;154
10.1;5.1 Populations and Samples;155
10.2;5.2 The Power of Random Sampling;156
10.3;5.3 A Study of the Break-Even Effect;159
10.4;5.4 Biased Samples;162
10.5;5.5 Observational Data versus Experimental Data;165
10.6;Exercises;167
11;Chapter 6. Estimation;176
11.1;6.1 Estimating the Population Mean;177
11.2;6.2 Sampling Error;178
11.3;6.3 The Sampling Distribution of the Sample Mean;179
11.4;6.4 The t Distribution;185
11.5;6.5 Confidence Intervals Using the t Distribution;187
11.6;Exercises;192
12;Chapter 7. Hypothesis Testing;202
12.1;7.1 Proof by Statistical Contradiction;203
12.2;7.2 The Null Hypothesis;204
12.3;7.3 P Values;205
12.4;7.4 Confidence Intervals;210
12.5;7.5 Matched-Pair Data;211
12.6;7.6 Practical Importance versus Statistical Significance;214
12.7;7.7 Data Grubbing;216
12.8;Exercises;221
13;Chapter 8. Simple Regression;232
13.1;8.1 The Regression Model;232
13.2;8.2 Least Squares Estimation;237
13.3;8.3 Confidence Intervals;239
13.4;8.4 Hypothesis Tests;241
13.5;8.5 R2;243
13.6;8.6 Using Regression Analysis;247
13.7;8.7 Prediction Intervals (Optional);251
13.8;Exercises;253
14;Chapter 9. The Art of Regression Analysis;272
14.1;9.1 Regression Pitfalls;273
14.2;9.2 Regression Diagnostics (Optional);287
14.3;Exercises;293
15;Chapter 10. Multiple Regression;310
15.1;10.1 The Multiple Regression Model;311
15.2;10.2 Least Squares Estimation;316
15.3;10.3 Multicollinearity;323
15.4;Exercises;325
16;Chapter 11. Modeling (Optional);346
16.1;11.1 Causality;346
16.2;11.2 Linear Models;347
16.3;11.3 Polynomial Models;348
16.4;11.4 Power Functions;350
16.5;11.5 Logarithmic Models;357
16.6;11.6 Growth Models;358
16.7;11.7 Autoregressive Models;363
16.8;Exercises;366
17;Appendix;378
18;References;380
18.1;Chapter 1 Data, Data, Data;380
18.2;Chapter 2 Displaying Data;380
18.3;Chapter 3 Descriptive Statistics;381
18.4;Chapter 4 Probability;382
18.5;Chapter 5 Sampling;383
18.6;Chapter 6 Estimation;384
18.7;Chapter 7 Hypothesis Testing;385
18.8;Chapter 8 Simple Regression;386
18.9;Chapter 9 The Art of Regression Analysis;386
18.10;Chapter 10 Multiple Regression;387
18.11;Chapter 11 Modeling (Optional);388
19;Index;390