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R과 파이썬을 활용한 논문연구법

R과 파이쎈을 활용한

ISBN-13

979-11-88109-19-7 (93320)
출판사 / 임프린트

도서출판 창명 / 도서출판 창명
정가

30,000 원 확정정가
발행일

2020-09-03
출간상태

출간
저자

차경천
번역

-
메인주제어

경제, 재무, 비즈니스, 경영
추가주제어

-
키워드

#경상계열 #국내도서 #대학교재/전문서적 #통계
도서유형

종이책, 양장
대상연령

모든 연령, 대학 교재
도서상세정보

178 * 242 mm, 304 Page

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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

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저자 : 차경천
KAIST(카이스트)에서 경영공학 박사학위를 받고 서울과학종합대학원을 거쳐 현재 동아대학교 경영학과 교수로 재직하고 있다. 서울대, 성균관대 경영전문대학원 연구교수로 활동했으며, KAIST 예측연구실 창업벤처기업인 폴비존을 3년간 운영했다. 한국마케팅학회 최우수논문상과 우수논문상을 수상했다. 스포츠, 전자제품, 피자, 커피, 위스키, 보험, 핸드폰, 통화량, 외식업, 전자소자, 인터넷쇼핑몰, 직무만족도, TV, 관광, 영화관, 주유소 등과 관련한 예측 문제를 다루어왔으며, 지금은 신상품 수요예측 관련 연구에 힘쓰고 있다. 최근작 : 기초 통계적 연구방법론,광고의 예상을 빗나간 마케팅효과,R과 파이썬을 활용한 논문연구법 … 총 10종

R과 파이썬을 활용한 논문연구법

R과 파이쎈을 활용한

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