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2019 Academic Year Course Description and Syllabus

Course Name

Statistics 2(4credits) [INLA121]

Instructor Name

Tomohiro Uchiyama

Course numbers are displayed in blue color after course names.

Semester　Fall Semester

General Description

An introduction to the ideas, techniques, and applications of statistics and probability. The emphasis is on applying statistics to problems, selecting sensible techniques, following the methodology and interpreting the results. Understanding the concepts and computer-based solutions are emphasised and applications to commerce, social sciences, humanities, science and engineering are considered. Particular topics include probability, inferences involving two populations, applications of Chi-square, analysis of variances, linear correlations and regression analyses (including confidence intervals, hypothesis testings), and nonparametric statistics.

Goals and Objectives

A student who successfully completes this course will:
understand a range of basic statistical concepts in
1. probability: meaning of probability, sets, events, and distributions;
2. inference involving two populations: hypotheses, tests, test statistics, test
interpretations;
3. analysis of variances (ANOVA);
4. linear correlations and regressions;
5. nonparametric statistics.
perform and interpret a range of basic statistical procedures in
1. probability: calculations and identification/application of distributions;
2. inference involving two populations: hypothesis tests and model buildings;
3. analysis of variances (ANOVA);
4. linear correlations and regressions;
5. nonparametric statistics.

General Education / Faculty Courses: Most relevant Learning Outcomes for this course.

	acquire academic knowledge across the fields of humanities and social sciences
◎	develop skills in quantative and qualitative research methodologies
	conduct directed research based on knowledge from across the fields of humanities and social sciences
	develop advanced English language skills to conduct academic writing, discussions and presentations
	develop critical thinking and analysis skills through reading, writing, discussion and presentation
	develop cross-cultural understanding to collaborate with individuals from diverse cultural backgrounds
	identify issues and work towards solutions

Course Syllabus

	Content
Class 1	Lecture contents	Guidance
Class 1	Self-study Assignments
Class 2	Lecture contents	Probability, Events, Conditional probability
Class 2	Self-study Assignments	Read Chap.4 of the textbook.
Class 3	Lecture contents	Rules of probability, Mutually exclusive events, Special addition rule.
Class 3	Self-study Assignments
Class 4	Lecture contents	Independent events, Special multiplication rule.
Class 4	Self-study Assignments
Class 5	Lecture contents	Review of statistical inferences from statistics 1 (Estimations)
Class 5	Self-study Assignments
Class 6	Lecture contents	Review of statistical inferences from statistics 1 (Hypothesis testings, Confidence intervals)
Class 6	Self-study Assignments
Class 7	Lecture contents	Inferences involving two populations: Dependent and independent samples.
Class 7	Self-study Assignments	Read Chap.10 of the textbook.
Class 8	Lecture contents	Inferences involving two populations: Inferences concerning the mean difference.
Class 8	Self-study Assignments
Class 9	Lecture contents	Inferences involving two populations: Inferences concerning the difference between two proportions.
Class 9	Self-study Assignments
Class 10	Lecture contents	Inferences involving two populations: Inferences concerning the ratio of variances, F-distributions.
Class 10	Self-study Assignments
Class 11	Lecture contents	Applications of Chi-square: Chi-square statistics
Class 11	Self-study Assignments	Read Chap.11 of the textbook.
Class 12	Lecture contents	Applications of Chi-square: Inferences concerning multinomial experiments, Inferences concerning contingency tables.
Class 12	Self-study Assignments
Class 13	Lecture contents	Analysis of variance: Logic behind ANOVA
Class 13	Self-study Assignments	Read Chap.12 of the textbook
Class 14	Lecture contents	Analysis of variance: Logic behind ANOVA
Class 14	Self-study Assignments
Class 15	Lecture contents	Analysis of variance: Applications of single factor ANOVA.
Class 15	Self-study Assignments
Class 16	Lecture contents	Analysis of variance: Hypothesis tests for the equality of several means.
Class 16	Self-study Assignments
Class 17	Lecture contents	Linear correlations and regressions: Linear correlation analysis.
Class 17	Self-study Assignments	Read Chap.13 of the textbook.
Class 18	Lecture contents	Linear correlations and regressions: Inferences about the linear correlation coefficient.
Class 18	Self-study Assignments
Class 19	Lecture contents	Linear correlations and regressions: Confidence intervals and hypothesis testings.
Class 19	Self-study Assignments
Class 20	Lecture contents	Linear correlations and regressions: Linear regression analysis
Class 20	Self-study Assignments
Class 21	Lecture contents	Linear correlations and regressions: Inferences concerning the slope of the regression line.
Class 21	Self-study Assignments
Class 22	Lecture contents	Linear correlations and regressions: Inferences concerning the slope of the regression line.
Class 22	Self-study Assignments
Class 23	Lecture contents	Linear correlations and regressions: Confidence intervals for regressions, Prediction intervals.
Class 23	Self-study Assignments
Class 24	Lecture contents	Linear correlations and regressions: Relationships between correlations and regressions.
Class 24	Self-study Assignments
Class 25	Lecture contents	Introduction to nonparametric statistics: Nonparametric statistics.
Class 25	Self-study Assignments	Read Chap.14 of the textbook.
Class 26	Lecture contents	Introduction to nonparametric statistics: The sign test, the runs test.
Class 26	Self-study Assignments
Class 27	Lecture contents	Review of the course.
Class 27	Self-study Assignments
Class 28	Lecture contents	Review of the course.
Class 28	Self-study Assignments
Class 29	Lecture contents	Preparatory test.
Class 29	Self-study Assignments
Class 30	Lecture contents	Final examination.
Class 30	Self-study Assignments

Evaluation/Assessment

Assessment	Percentage	Evaluation Criteria (Explanation)
Final Exam	40%	To get B, a student needs to answer 70% of problems correctly.
Midterm
Papers
Performance/Works
Continuous Assessment (quizzes, assignments, etc.)	60%	To get B, a student needs to attempt and show effort in 70% of problems.
Other

Grading Method:ABC

Course Materials

　1．R.Johnson, P.Kuby (2011) STAT2, second edition, Cengage Learning

Reference Materials

Advice for Prospective Students

This course is a continuation of Statistics I. Only students who have passed Statistics I (or have done something equivalent) can take this course. Prior knowledge in Excel and R is required.

Estimated Out-of-class Study Time Per Week (incl. assignments, tests, papers, etc) : 4hrs

Implementation of Active Learning

Yes

- Discussion and/or debate

Will you use ICT for class or to support self-learning?

Yes

- Laptop or tablet device (mandatory).

How to give feedback for assignments (mid-term exams, reports, etc.)

Make time to review or explain in class.
Correct and return tests or reports.

Language used in class

English

Link URL：　 https://plas.soka.ac.jp/csp/plas/slb.csp?nd=2019&sm=2&mk=11&lc=94744