Covers the key issues required for students wishing to understand and analyse the core empirical issues in economics. It focuses on descriptive statistics, probability concepts and basic econometric techniques and has an accompanying website that contains all the data used in the examples and provides exercises for undertaking original research.
Covers the key issues required for students wishing to understand and analyse the core empirical issues in economics. It focuses on descriptive statistics, probability concepts and basic econometric techniques and has an accompanying website that contains all the data used in the examples and provides exercises for undertaking original research.
Terence Mills is Professor of Applied Statistics and Econometrics at Loughborough University, UK. He is the author of The Foundations of Modern Time Series Analysis and A Very British Affair: Six Britons and the Development of Time Series Analysis During the 20th Century, both published by Palgrave Macmillan.
Inhaltsangabe
1. Introduction 2. Presenting and Summarising Data 3. Transforming Data 4. Index Numbers 5. Correlation 6. Regression 7. Basic Concepts of Probability 8. Probability Distributions 9. Continuous Random Variables and Probability Density Functions 10. Sampling and Sampling Distributions 11. Estimation and Inference 12. The Classical Regression Model 13. Multiple Regression 14. Autocorrelation 15. Heteroskedasticity 16. Simultaneity, Instrumental Variables and Non-Normal Errors 17. Testing for Stability in Regression Models 18. Basic Time Series Models
1. Introduction 2. Presenting and Summarising Data 3. Transforming Data 4. Index Numbers 5. Correlation 6. Regression 7. Basic Concepts of Probability 8. Probability Distributions 9. Continuous Random Variables and Probability Density Functions 10. Sampling and Sampling Distributions 11. Estimation and Inference 12. The Classical Regression Model 13. Multiple Regression 14. Autocorrelation 15. Heteroskedasticity 16. Simultaneity, Instrumental Variables and Non-Normal Errors 17. Testing for Stability in Regression Models 18. Basic Time Series Models
1. Introduction 2. Presenting and Summarising Data 3. Transforming Data 4. Index Numbers 5. Correlation 6. Regression 7. Basic Concepts of Probability 8. Probability Distributions 9. Continuous Random Variables and Probability Density Functions 10. Sampling and Sampling Distributions 11. Estimation and Inference 12. The Classical Regression Model 13. Multiple Regression 14. Autocorrelation 15. Heteroskedasticity 16. Simultaneity, Instrumental Variables and Non-Normal Errors 17. Testing for Stability in Regression Models 18. Basic Time Series Models
1. Introduction 2. Presenting and Summarising Data 3. Transforming Data 4. Index Numbers 5. Correlation 6. Regression 7. Basic Concepts of Probability 8. Probability Distributions 9. Continuous Random Variables and Probability Density Functions 10. Sampling and Sampling Distributions 11. Estimation and Inference 12. The Classical Regression Model 13. Multiple Regression 14. Autocorrelation 15. Heteroskedasticity 16. Simultaneity, Instrumental Variables and Non-Normal Errors 17. Testing for Stability in Regression Models 18. Basic Time Series Models
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