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R과 파이썬을 활용한 『논문연구법』은 〈참고문헌 읽는 법〉, 〈R & Python 준비하기〉, 〈Regression model〉, 〈Diffusion model〉 등 논문연구법에 대한 기초적이고 전반적인 내용이 수록되어 있다.

Chapter 01 참고문헌 읽는 법
1. 참고문헌 읽는 순서 ··························· 13
2. 좋은 연구란? ··························· 14
3. 왜 기존 문헌연구가 필요한가? ··························· 15
4. 위키피디아 ··························· 16
5. 논문의 구성 ··························· 17
6. 가설의 설정 ··························· 19
Chapter 02 R & Python 준비하기
1. R 설치하기 ··························· 29
2. Python 설치하기 ··························· 33
Chapter 03 Regression model
1. 회귀분석 ···························· 47
2. 표준화된 계수 ···························· 52
3. 설정오류 ···························· 58
4. 다양한 회귀분석 모형들 ···························· 60
5. 회귀분석으로 이원분산 분석하기 ···························· 61
6. 최적치 추정하기 ···························· 62
7. R 실습 ···························· 63
8. Python 실습 ···························· 68
9. 컨조인트 분석을 회귀분석으로 ··························· 69
10. 논문작성의 예 ··························· 78

Chapter 04 Diffusion model
1. Bass Diffusion model ····························· 83
2. 확산모형의 한계와 개선점 ····························· 86
3. Generalized Bass Diffusion model과 다양한 시도들 ······· 87
4. R 실습 ····························· 89
5. Python 실습 ····························· 96
6. 논문작성의 예 ························ 96
Chapter 05 Price response model
1. 가격변화에 따른 수요반응 모형들 ····························· 103
2. Asymmetric model 추정방법 ······························ 107
3. R 실습 ······························ 113
4. Python 실습 ······························· 116
5. 논문작성의 예 ·································· 117
Chapter 06 Marketing dynamics
1. Leeflang et al.(2000) ································ 121
2. 논문작성의 예 ································· 126
Chapter 07 Time series model
1. ARIMA model ······························ 131
2. White Noise Process ···························· 135
3. R 실습 ··························· 137
4. Python 실습 ······························· 138
5. 논문작성의 예 ·································· 140
Chapter 08 Panel data model
1. Models for panel data ··························· 145

2. Fixed effect model ···························· 148
3. Random effect model ····························· 149
4. Test for model selection ······························ 150
5. R 실습 ······················ 151
6. Python 실습 ···························· 160
7. 논문작성의 예 ························· 166
Chapter 09 System equation model
1. System equation ····················· 171
2. Vector Autoregressive model ······················· 176
3. Vector Error Correction model ························ 180
4. Seemingly Unrelated Regression ·························· 182
5. R 실습 ····························· 184
6. Python 실습 ···························· 189
7. 논문작성의 예 ···························· 191
Chapter 10 Limited dependent model
1. Logit ···························· 197
2. Probit ······························ 200
3. Logistic regression ······························ 202
4. Multinomial ······························· 203
5. Censored, Truncated case ························ 203
6. R 실습 ························ 205
7. Python 실습 ··························· 213
8. 논문작성의 예 ······························· 216
Chapter 11 Count data model
1. Poisson Regression ······················· 221
2. Negative Binomial Model ······················· 223
3. Test for model selection: Likelihood ratio test ············ 226
4. R 실습 ······················· 226
5. Python 실습 ·························· 234
6. 논문작성의 예 ····························· 236
Chapter 12 Network centrality
1. 사회연결망 분석 ······················· 241
2. Granovetter(’73) ················· 257
3. Recommendation Algorithm ···················· 258
4. 논문작성의 예 ······················ 277
Chapter 13 Difference-in-Difference
1. DiD ···························· 281
2. Endogeneity ·························· 282
3. Difference-in-Difference ························ 283
Chapter 14 Regression Discontinuity Design
1. RDD ······················· 287
2. RDD model ···························· 288
3. 논문작성의 예 ························· 290
부 록 통계 분포들
1. 이산형 확률분포 ······················ 295
2. 연속형 확률분포 ························ 296
■찾아보기 ···························· 301