Instructor: Ling-Chieh Kung
Department of Information Management
National Taiwan University
Statistics and data analysis are probably playing the most important roles in business analytics nowadays. With the ability to conduct scientific statistical studies and systematically analyze data, managers will be able to understand more about their customers, suppliers, competitors, and the business environment. The insights may then facilitate better decision making and help a company to attain competitive advantages. In this fundamental course in the Global MBA (GMBA) program, we will focus on the techniques for conducting basic statistical studies and data analysis. The hope is that students will be capable of doing scientific data analyses in their future GMBA courses and after graduations. Time will be spent on tools, applications, as well as theories. Statistical software will be taught and used throughout this course. For at least part of this course, I plan to adopt the "flipped classroom" principle, which may be new to some students. Please pay attention to the syllabus to get an idea about the design of this course.
This is a required course offered in the GMBA program in National Taiwan University. The GMBA office does not allow non-GMBA students to take or audit this course.
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For a detailed description about this course, including course policies, grading rules, tentative schedules, etc., please see the syllabus. Whenever there is an update, a new version will be posted with a short note describing the update.
Week | Date | Special Events |
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3 | 2015/9/28 | No class: Mid-autumn Festival |
9 | 2015/11/9 | Midterm exam |
16 | 2015/12/28 | Final exam |
17 | 2016/1/4 | Project presentations |
18 | 2016/1/11 | Project presentations |
Week | Topic | Lecture | Video | Related Files |
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1 | Introduction | Slides | N/A | N/A |
2 | Descriptive Statistics (1) | Slides | Playlist | N/A |
3 | No class: Mid-autumn Festival | N/A | N/A | N/A |
4 | Descriptive Statistics (2) | Slides | Playlist | N/A |
5 | Probability (1) | Slides | Playlist | N/A |
6 | Probability (2) | Slides | Playlist | N/A |
7 | Distributions and Sampling (1) | Slides | Playlist | N/A |
8 | Distributions and Sampling (2) | Slides | Playlist | N/A |
9 | Midterm Exam | N/A | N/A | N/A |
10 | Statistical Estimation | Slides | Playlist | Pre-lecture Problems |
11 | Hypothesis Testing | Slides | Playlist | Pre-lecture Problems |
Supplements for Hypothesis Testing | Slides | N/A | N/A | |
12 | Regression Analysis (1) | Slides | Playlist | Pre-lecture Problems |
13 | Regression Analysis (2) | Slides | Playlist | Pre-lecture Problems |
14 | Regression Analysis (3) | Slides | N/A | N/A |
15 | R Programming and Logistic Regression | Slides | N/A | Scripts and Data |
16 | Final Exam | N/A | N/A | N/A |
17 | Final Project Presentations (1) | N/A | N/A | N/A |
18 | Final Project Presentations (2) | N/A | N/A | N/A |
Handout | Description |
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Forecasts | In-class brainstorming |
Math Symbols | Common mathematical notations and operations |
Data Analysis | MS Excel Data Analysis installation |
Forecasts | In-class brainstorming (2) |
R on Windows | R manual for Windows |
R on Mac | R manual for Mac |
Logistic Regression | An introduction to logistic regression |
Logistic Regression Files | R codes and data for logistic regression |
Problems | Data | Solution |
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Homework 1 | Data 1 | Solution 1 |
Homework 2 | Data 2 | Solution 2 |
Homework 3 | Data 3 | Solution 3 |
Homework 4 | Same as Data 3 | Solution 4 |
Item | Description |
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Project | What one should do in the final project |
Schedule | Presentation schedule |
Format suggestions | My suggestions for formatting your reports |