課程名稱 
應用統計學二 APPLIED STATISTICS(II) 
開課學期 
962 
授課對象 
社會科學院 政治理論組 
授課教師 
江瑞祥 
課號 
PS2012 
課程識別碼 
302 29220 
班次 

學分 
2 
全/半年 
半年 
必/選修 
必修 
上課時間 
星期五5,6(12:20~14:10) 
上課地點 
社法16 
備註 
實習時地另定。先修科目：應用統計學一(適用全校學生含研究所)先修科目:應用統計學一(適用全校學生,含研究生)。 總人數上限：180人 外系人數限制：10人 
Ceiba 課程網頁 
http://ceiba.ntu.edu.tw/96229220stat 
課程簡介影片 

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課程概述 
This course is an intermediate level statistics course concentrating on linear and multivariate statistics. The first six weeks will be devoted to bivariate regression (including matrix algebra); the balance of the semester covers multiple regression and other applications of the general multivariate model, including logistic regression, dummy variables, causal models, and multivariate statistical methods.
During the first part of the course students learn to develop good hypotheses, make valid causal inferences, operational concepts, and use the comparative method. The second section of the course introduces students to mechanisms that can be used to "tell the story" of political causation. The third section of the course covers quantitative topics such as central tendency and variation, measures of association, and regression analysis. In order to show the relevance and applicability of the different techniques, real examples will be used whenever possible. A portion of each day will be spent learning how to conduct research on the internet, with an emphasis on searching databases. By the end of the course students will have been exposed to the basic tools necessary to begin conducting, understanding, and critiquing political research. While I do not expect all students to go on to careers in research, the skills developed in this course will allow them to be better consumers of political information.

課程目標 
The prerequisite for the course is introductory statistics (e.g., Political Science 302 29210). In addition students will need to have or obtain some familiarity with computer based data analysis using a statistical package like Stata, SPSS or SAS. Students will be able access SPSS either from the machines at the the Social Science Micro Lab (SSML) in the Social Sciences Building (3rd floor). All students will be set up with accounts on the Political Science Server.
A key part of understanding statistical tools is knowing how to use them to address questions of interest. A good statistical analyst is able to:
(83dc) pose interesting and relevant questions in a clear and welldefined manner,
(83dc) specify the data that may help in answering those questions,
(83dc) analyze the data in sensible ways, including:
－ exploring the data for new insights
－ using the data to estimate parameters and test hypotheses of interest
(83dc) draw appropriate and relevant conclusions from analyses, including:
－ assessing the degree of structure or predictability in the data (e.g., goodness of fit measures)
－ assessing the degree of uncertainty or imprecision in parameter estimates (e.g., with confidence intervals and measures of sampling variability)
－ recognizing the limitations and assumptions of the data or statistical technique,
(83dc) use these conclusions to point the way towards new questions and analyses of interest
(83dc) and of course, have fun all the while!

課程要求 
Since this is an applied methods course, the emphasis will be on applications of the methods discussed in class. Graded homework will be assigned each two to three weeks. Homeworks will require the use of the computer software. Short reports on the conclusions and/or findings of the analysis will be part of the homework requirements. You cannot pass this class without doing the homework. Late homework will be accepted only at a 20 points deduction per day late. One or more quizzes may be given but they will not constitute the bulk of course credit.
 Homework: Top 5 problemsets each 6 points.
 MidTerm and Final: Each 35 points.

預期每週課後學習時數 

Office Hours 
另約時間 
參考書目 

指定閱讀 

評量方式 (僅供參考) 
No. 
項目 
百分比 
說明 
1. 
期中考 
35% 

2. 
期末考 
35% 

3. 
隨堂測驗 
0% 

4. 
作業 
30% 

5. 
報告 
0% 


週次 
日期 
單元主題 
第1週 
2/22 
Review of Inferential Statistics, and Principles of Estimation 
第2週 
2/29 
Campus Closure 
第3週 
3/07 
Introduction to ANOVA (ch. 8) 
第4週 
3/14 
ANOVA and Categorical Data Analysis (ch. 8, 9) 
第5週 
3/21 
Bivariate Regression: The Model, Estimation, Inference, Correlation (ch. 10) 
第6週 
3/28 
Bivariate Regression: Diagnosing and Resolving Problems (ch. 10) 
第7週 
4/04 
National Holiday 
第8週 
4/11 
Multiple Regression and Special Problems with Preditors: Dummy
Variables, Nonlinear Predictors, and Interactions (ch. 11) 
第9週 
4/18 
Multiple Regression and Special Problems with Preditors: Dummy Variables, Nonlinear Predictors, and Interactions (cont., ch. 11) 
第10週 
4/25 
MidTerm 
第11週 
5/02 
Dichotomous Dependent Variables and Other Nonnormal Problems
(ch. 11) 
第12週 
5/09 
Causal Analysis Inference in Regression (additional materials) 
第13週 
5/16 
Diagnostic Procedures in Multiple Regression (additional materials) 
第14週 
5/23 
Remedial Procedures in Multiple Regression (additional materials) 
第15週 
5/30 
Special Problems of Time Series Data: Autocorrelations (ch. 13) 
第16週 
6/06 
Nonparametric Tests (ch. 14) 
第17週 
6/13 
Nonparametric Tests (ch. 14) 
