MBA 7098, Fall 2015

Statistics and Data Analysis

Instructor: Ling-Chieh Kung

Department of Information Management

National Taiwan University


Go

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
  • Jeff Lee (李維哲). E-mail: r04725023(AT)ntu.edu.tw.
  • Share Lin (林怡安). E-mail: r04725037(AT)ntu.edu.tw.
Lectures
  • 6:25-9:05pm, 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
2015/8/21 Link The basic planning of this course
2015/9/22 Link Cancellation of one homework
2015/10/24 Link A slight modification of the schedule

Important Dates

Week Date Special Events
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

Lecture materials

Week Topic Lecture Video Related Files
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

Handouts

Handout Description
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

Homework

Exam

Problems Data Solution
Midterm problems Midterm data Midterm solution
Sample final problems Sample final data N/A
Final problems Final data Final solution

Project

Item Description
Project What one should do in the final project
Schedule Presentation schedule
Format suggestions My suggestions for formatting your reports