Statistics in Society
TeachersTSUMURA KentaStaffInfo
Grade, SemesterYear 1 2nd semest [General Basic Subjects]
CategoryGeneral Basic Subjects
Elective, CreditsElective 2credit
 Syllabus Number0G132

Course Description

 This is an introductory course on basic methods to analyze data in social and natural sciences. This course also overviews and discusses the role and the importance of statistics in society.
 For the beginners of statistics, this course introduces basics of statistical analysis such as chi-squared test, t-test, confidence interval, and regression analysis.

Course Objectives

1. Utilize basic methods of statistical testing and statistical estimation.
2. Acquire basic skills to analyze and discuss statistical data.

Grading Policy

Your overall grade in this class will be decided based on the following:
 - term-end exam: 40%
 - homework assignments and three short papers: 60%

Textbook and Reference

TextbookNo textbook is required.


 To prepare each class, read the handouts up-loaded to LMS in advance and check technical terms and formulas.
 After each class, review the class and do homework assignments.


 This course uses LMS. Handouts will be up-loaded to LMS in advance of the class. Students must print out and bring handouts to the class by themselves.
 No prerequisites are needed. However, students are expected to have knowledge of basic statistics such as mean, standard deviation, normal distribution, and correlation.
 This course assumes no prior knowledge of differential and integral calculus but requires fundamental knowledge of high school algebra.
 In this course, students are required to bring a calculator with square root button to use on exams and quizzes.
 The schedule is subject to change due to circumstances.


2chi-squared test
3practical training for chi-squared test using computer
5practical training for t-test using computer
6presentation of short papers (t-test and chi-squared test)
7estimation of population mean
8estimation of population proportion
9review class 2 to 8
10regression analysis
11practical training for regression analysis using computer
12writing short reports about the results of regression analysis
13presentation of short paper (regression analysis)
14statistics in society (big data, AI)
15term-end exam