MBA 7098, Fall 2016

Statistics and Data Analysis

Instructor: Ling-Chieh Kung

Department of Information Management

National Taiwan University


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About this Course

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.

Basic information

Instructor
  • Ling-Chieh Kung (孔令傑)
  • E-mail: lckung(AT)ntu.edu.tw
  • Office: Room 413, Management Building II
  • Tel: 02-3366-1176.
  • Office hour: by appointments.
Teaching Assistants
  • Daniel Zhihao Lee. E-mail: r04749037(AT)ntu.edu.tw.
Lectures
  • 14:20-17:20pm, Monday.
  • E-Sun Hall, Management Building I.
References
  • Business Statistics: For Contemporary Decision Making by K. Black.
  • Freakonomics S. Levitt and S. Dubner.
  • Big Data by V. Mayer-Schonberger and K. Cukier.
  • Learn R in a Day by S. Murray.
On-line resources
  • To check grades: CEIBA.
  • To download or link to materials: This website.
  • To discuss: Piazza.

Syllabus

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.

Post Syllabus Notes
2016/9/14 Link The official planning of this course
2016/10/12 Link Switching Weeks 11 and 12

Lecture materials

Week Topic Lecture Video Pre-lecture Problems
1 Introduction Slides N/A N/A
2 MS Excel Operations Slides N/A N/A
3 No class: typhoon N/A N/A N/A
4 Descriptive Statistics (1) Slides, Problems, Data Playlist Problems
5 Descriptive Statistics (2) Slides, Problems, Data Playlist Problems
6 Probability Slides, Problems, Data Playlist Problems
7 Case 1 Presentations Slides of the dice game N/A N/A
8 Distributions and Sampling Slides, Problems, Data Playlist Problems
9 Hypothesis Testing Slides, Problems, Data Playlist Problems
10 Regression Analysis (1) Slides, Problems, Data Playlist Problems, Data
11 Project Milestone N/A N/A N/A
12 Regression Analysis (2) Slides, Problems, Data Playlist Problems, Data
13 Case 2 Presentations N/A N/A N/A
14 R Programming Slides, Data, Codes N/A N/A
15 Logistic Regression and Association Rule Mining Slides, Data, Codes N/A N/A
16 Clustering Slides, Data, Codes N/A N/A

Data Sets and Handouts

Item Description
Data: Bike Data set: bike rentals
Data: Wholesale Data set: wholesale data
Brainstorming In-class brainstorming: Bike Rental Forecast
Math symbols Common mathematical notations and operations
Data Analysis The MS Excel add-on "Data Analysis"
R for Windows R Installation and GUI Setup for Windows
R for Mac R Installation and GUI Setup for Mac

Case studies

Item Description
Case 1 Description of Case Study 1
Case 2 Description of Case Study 2

Project

Item Description
Project Desctiption of the final project