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2022　Statistics B

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Academic unit or major
Humanities and social science courses
Instructor(s)
Kezuka Kazuhiro 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Day/Period(Room No.)
Mon1-2(S011)  Thr1-2(S011)  
Group
-
Course number
LAH.T201
Credits
2
Academic year
2022
Offered quarter
2Q
Syllabus updated
2022/4/19
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

Inferential statistics is the tool to grasp the information of population using the survey data. This tool is essential for analysis of social surveys and big data. Students learn inferential statistics with topics of social sciences, especially social inequality.

Student learning outcomes

At the end of this course, students will be able to:
1) understand and utilize the concept of test and estimation,
2) build probability models of social statistics or events.

Keywords

descriptive statistics, inferential statistics, probability distribution, sampling, estimation, hypothesis testing, regression analysis

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

The instructor will lecture about inference statistics based on the textbook. Students should solve some problems and submit their answers at the end of each class. At the end of term, students must take the final exam.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction and Descriptive statistics Understand descriptive statistics and the importance of estimation and test.
Class 2 Gini coefficient Understand how to grasp the inequality.
Class 3 Mobility table Understand how to grasp the maintain of intergenerational inequality.
Class 4 Standard error and random sampling Understand the relationship between standard error and random sampling.
Class 5 Binomial distribution Understand binomial distribution and how to build probability model.
Class 6 Normal distribution Understand normal distribution and Central Limit Theorem.
Class 7 Point estimation and interval estimation Understand the logic of estimation.
Class 8 Unbiased estimator Understand unbiased estimator.
Class 9 Statistical test Understand the logic of test.
Class 10 Testing the difference of means Understand how to test the difference of means.
Class 11 Chi-squared test Understand Chi-squared test for cross table.
Class 12 Analysis of variance Understand analysis of variance.
Class 13 Test of correlation and t test Understand test of correlation and t test.
Class 14 Regression analysis Understand regression analysis

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

Kazuhiro Kezuka, 2022, Introduction to Statistics for Social Sciences, Kodansha Scientific. (In Japanese, Scheduled to be released in Late June)

Reference books, course materials, etc.

Textbooks will be specified by the instructor.

Assessment criteria and methods

Students’ course scores are based on class reports 30% and the final report 70%.

Related courses

  • LAH.T101 ： Statistics A
  • LAH.T301 ： Statistics C
  • LAH.S434 ： Essence of Humanities and Social Sciences38:Statistics

Prerequisites (i.e., required knowledge, skills, courses, etc.)

No prerequisites.

Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).

kkezuka[at]ila.titech.ac.jp

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