논문연구법(R과 파이썬을 활용한)(양장본 HardCover)
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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.
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출판사 리뷰
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목차
목차
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
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
저자
저자
차경천
차경천 교수는 KAIST(한국과학기술원)에서 경영공학 박사학위를 받고, 현재동아대학교 경영학과 교수로 재직 중이다. 한국마케팅학회 부회장을 맡았다.수요예측 전문 벤처기업을 3년간 운영한 바 있다. 국내/외 다수의 학술저널에 논문을 게재했으며, 한국마케팅학회 최우수논문상과 우수논문상을 받았다. 한국마케팅학회의 학술지 『마케팅연구』의 편집위원장을 맡았다. 저서로는 『예측의 힘』, 『기초 통계적 연구방법론』, 『분석적 마케팅 조사론』이 있다. 스포츠 기록, 전자제품 수요, 네트워크 사업 매출, 피자, 커피, 위스키,외식업, 핸드폰, 보험 신규계약 건수, 핸드폰 통화량, 전자소자, 인터넷 쇼핑몰, 영화관 매출과 위치 선정, 핸드폰 위치 정보 분석, 관광실태 조사, 주유소 위치 선정, TV display panel size, 직무만족도, Big Data 자문 등과 관련한 다양한 예측 문제를 해결해왔다
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