Statistics ｜ Syllabus data

Course Title

Statistics

Course Title in English

Statistics

Course Type

General Courses

-

Eligible Students

School of Economics and Management

Target Grade

All

Course Numbering Code

IA9991GCA1

Credits

2.00Credits

The course numbering code represents the faculty managing the subject, the department of the target students, and the education category (liberal arts / specialized course). For detailed information, please download the separate manual from the upper right 'question mark'.

Type of Class

講義 (Lecture)

Eligible Year/Semester

Spring semester 2026

(Spring semester)

Instructor

Toru KAISE

Affiliation

Graduate School of Business (MBA)

Language of Instruction

English

Related SDGs

9

Office Hours and Location

Monday 2nd period, Tuesday 2nd period, Friday 2nd period; Office A230

Contact

kaise@mba.u-hyogo.ac.jp

Corresponding Diploma Policy

A double circle indicates the most relevant DP number and a circle indicates the associated DP.

Corresponding Undergraduate School DP

ー

Corresponding Graduate School DP

ー

Corresponding University-Wide DP

1-1◎／4-2◎

Academic Goals of Teacher Training Course

ー

Course Objectives and Learning Outcome

This course offers classes for an introduction to Statistics, and particularly it contains fundamental materials of Mathematics and the Excel handling with PC. Topics of the classes include probability laws, probability models, applications of probability distributions, sampling, hypothesis testing, simple and multiple linear regression models. Our aim is that these topics are applied to analytical methodologies for Economics and Business.

Subtitle and Keywords of the Class

Course Overview and Schedule

We handle the fundamental materials of Statistics for undergraduate students using the software Excel with PC, and the classes include the basic educational treatments of Mathematics to learn the statistical methodologies.

1.Introduction of Statistical Methodologies
2.Organizing Data
3.Descriptive Measures
4.Probability
5.Random Variables
6.Probability Distributions
7.Sampling Distributions
8.Estimation Methods
9.Tests of Hypotheses
10.Regression Models and Estimations
11. Case Studies and Applied Exercises
12.Examination

In-person/Remote Classification

In-person

Implementation Method and Remote Credit Limit Application

For any class conducted remotely online, a detailed explanation will be provided.

Uses of Generative AI

Limited permission for use

Precautions for using Generative AI

For any use of generative AI, a detailed explanation will be provided.

Textbook

David Diez, Mine Cetinkaya-Rundel, Christopher D Barr, OpenIntro Statistics (Fourth Edition) : a free PDF is available at 'openintro.org/book/os '.

References

Contents and Estimated Time for Pre- and Post- Learning (Preparation and Review)

Preparation: 60 minutes,　Review: 60 minutes

Contents of Active Learning

Discussion

Grading Criteria and Methods

Quizzes (during class):5%, Homework assignments and case studies:45%, Examination:50%

How to Disclose Assignments and Exam Results

By the Universal Passport

Precautions and Requirements for Course Registration

The mind interested in the data analysis using PC is necessary to continue the classes with the homework treatments.

Practical Education

Not Available

Remarks

In cases where any differences arise between the English version and the original Japanese version, the Japanese version shall prevail as the official authoritative version.