標題: 迴歸分析
Regression Analysis
作者: 黃冠華
Open Education Office
開放教育推動中心
公開日期: 2015
摘要: 課程首頁   本課程是由交通大學統計學研究所提供。   The goals of this course are to introduce regression analysis for continuous and discrete data. Topics include simple and multiple linear regressions, inferences for regression coefficients, confounding and interaction, regression diagnostics, logistic regressions, Poisson regressions, and generalized linear models. The course consists of lectures and laboratory sessions. The lectures are given on Tuesday 9:00-11:00. The lectures will primarily review and reinforce major issues. There is a laboratory session on Tuesday 11:10-12:00. The laboratory exercise will be distributed prior to each class, and students are expected to read each lab exercise at home. Each student will be assigned to a lab group and discuss the exercise with group members in the lab. At the end of the lab, there will be a seminar-type discussion. Each group is required to hand in a write-up of laboratory problems. The course uses the R software for statistical computing. Students are expected to be familiar with the usage of the software.
課程目標/概述     The goals of this course are to introduce regression analysis for continuous and discrete data. Topics include simple and multiple linear regressions, inferences for regression coefficients, confounding and interaction, regression diagnostics, logistic regressions, Poisson regressions, and generalized linear models. The course consists of lectures and laboratory sessions. The lectures are given on Tuesday 9:00-11:00. The lectures will primarily review and reinforce major issues. There is a laboratory session on Tuesday 11:10-12:00. The laboratory exercise will be distributed prior to each class, and students are expected to read each lab exercise at home. Each student will be assigned to a lab group and discuss the exercise with group members in the lab. At the end of the lab, there will be a seminar-type discussion. Each group is required to hand in a write-up of laboratory problems. The course uses the R software for statistical computing. Students are expected to be familiar with the usage of the software.   課程章節   單元主題 內容綱要 A review of basic statistical concepts ILRA APPENDIX C.1, and an introductory statistics book Measures of association with emphasis on the difference of means    Basics of linear regression analysis  ILRA 2.1, 2.2, 2.3 except 2.3.3, 2.4, 2.11 Correlation  ILRA 2.6, 2.12.2 Analysis of variance (ANOVA) table and prediction of y  ILRA 2.3.3, 2.5 Basics of multiple linear regression  ILRA 3.1, 3.2 Hypothesis testing in multiple regression  ILRA 3.3 Polynomial terms and dummy variables ILRA 3.10, 7.1, 7.2.1, 7.2.2, 8.1, 8.2 Interaction and confounding    Regression diagnosis  ILRA 4.1, 4.2, 4.4, 5.1, 5.2, 5.3, 5.4, 5.5, 6.1, 6.2, 6.3 Variable selection and model building ILRA Chapter 10 Relative risk, odds ratio and significance testing for 2x2 tables  ILRA 13.2.1, 13.2.2, 13.2.3, 13.2.4 Introduction to logistic regression    Logistic regression for contingency tables   Goodness-of-t for logistic regression ILRA 13.2.4, 13.2.5 Logistic regression of case-control data and conditional logistic regression    Analysis of polytomous data ILRA 13.2.7 Generalized linear models  ILRA 13.4 Poisson regression  ILRA 13.3   課程書目   Handouts corresponding to each lecture will be available on the course website before each class. The required textbooks for this course are : Montgomery, D.C., Peck, E.A., Vining, G.G. (2012). Introduction to Linear Regression Analysis (5th Edition). Wiley. (ILRA)   評分標準   項目 百分比 Homeworks 25% Write-ups of lab problems 30% Midterm Exam 20% Final exam (25%) 25%  
授課對象:研究生
預備知識:Students are expected to have background on undergraduate probability, and mathematical statistics. Computer programming knowledge on R and/or C/C++ is required.
URI: http://ocw.nctu.edu.tw/course_detail.php?bgid=1&nid=528
http://hdl.handle.net/11536/132446
顯示於類別:開放式課程


文件中的檔案:

  1. Introduction.mp4
  2. Lecture 1-1: A review of basic statistical concepts.mp4
  3. Lecture 1-2: Measures of association with emphasis on the di erence of means.mp4
  4. Lecture 2: Basics of linear regression analysis.mp4
  5. Lab 2 同學報告: 謝念廷 陳柏魁 許凱璋 席瑋辰 陶冠蘭 劉冠妤同學.mp4
  6. Lecture 4: Correlation.mp4
  7. Lab 4 同學報告: 李杰、林經濰、陳珮文、陳奕良、彭恩榮、李驊同學.mp4
  8. Lecture 5-1: Analysis of variance (ANOVA) table and prediction of y.mp4
  9. Lecture 5-2: Basics of multiple linear regression.mp4
  10. Lab 5 同學報告: 石昕秀、李東恩、侯昱德、劉學汝、李俊昌、方思婷同學.mp4
  11. Lecture 6-1: Hypothesis testing in multiple regression.mp4
  12. Lecture 6-2: Polynomial terms and dummy variables.mp4
  13. Lab 6 同學報告: 顏天保、劉又齊、藍玉朋、曾郁翔、唐心誠、林志豪 同學.mp4
  14. Lecture 7: Interaction and confounding.mp4
  15. Lecture 7 補充: Confounding and interaction in epidemiology .mp4
  16. Lab 7 同學報告: 黃郁豪、梁思婕、張登凱、何杰翰、周佳瑜同學.mp4
  17. Lecture 8-1: Regression diagnosis.mp4
  18. Lecture 8-2: Variable selection and model building.mp4
  19. Lab 9 同學報告: 許凱璋同學.mp4
  20. Lab 9 同學報告: 李杰、林經濰、陳珮文、陳奕良、彭恩榮、李驊同學.mp4
  21. Lecture 11: Relative risk, odds ratio and signi cance testing for 2*2 tables.mp4
  22. Lecture 12: Introduction to logistic regression.mp4
  23. Lab 12 同學報告: 石昕秀、李東恩、侯昱德、劉學汝、李俊昌、方思婷同學.mp4
  24. Lecture 13-1: Logistic regression for contingency tables.mp4
  25. Lecture 13-2: Goodness-of- t for logistic regression.mp4
  26. Lab 13 同學報告: 顏天保、劉又齊、藍玉朋、曾郁翔、唐心誠、林志豪同學.mp4
  27. Lecture 14: Logistic regression for case-control data and conditional logistic regression.mp4
  28. Lab 14 同學報告: 何杰翰、梁思婕、張登凱、周家瑜、黃郁豪同學.mp4
  29. Lecture 15: Analysis of polytomous data.mp4
  30. Lab 15 同學報告: 謝念廷 陳柏魁 許凱璋 席瑋辰 陶冠蘭 劉冠妤同學.mp4
  31. Lecture 16: Poisson regression and log-linear model.mp4
  32. Lab 16 同學報告: 李杰、林經濰、陳珮文、陳奕良、彭恩榮、李驊同學.mp4
  33. Lecture 17: Generalized linear models.mp4
  34. Lab 17 同學報告: 石昕秀、李東恩、侯昱德、劉學汝、李俊昌、方思婷同學.mp4
  35. Lecture 1-1: A review of basic statistical concepts.pdf
  36. Lecture 1-2: Measures of association with emphasis on the di erence of means.pdf
  37. Lecture 2: Basics of linear regression analysis.pdf
  38. Lecture 4: Correlation.pdf
  39. Lecture 5-1: Analysis of variance (ANOVA) table and prediction of y.pdf
  40. Lecture 5-2: Basics of multiple linear regression.pdf
  41. Lecture 6-1: Hypothesis testing in multiple regression.pdf
  42. Lecture 6-2: Polynomial terms and dummy variables.pdf
  43. Lecture 7: Interaction and confounding.pdf
  44. Lecture 8-1: Regression diagnosis.pdf
  45. Lecture 8-2: Variable selection and model building.pdf
  46. Lecture 11: Relative risk, odds ratio and signi cance testing for 2*2 tables.pdf
  47. Lecture 12: Introduction to logistic regression.pdf
  48. Lecture 13-1: Logistic regression for contingency tables.pdf
  49. Lecture 13-2: Goodness-of- t for logistic regression.pdf
  50. Lecture 14: Logistic regression for case-control data and conditional logistic regression.pdf
  51. Lecture 15: Analysis of polytomous data.pdf
  52. Lecture 16: Poisson regression and log-linear model.pdf
  53. Lecture 17: Generalized linear models.pdf