PlanningⅡ

Course Information

College	Akashi College	Year	2023
Course Title	PlanningⅡ
Course Code	5432	Course Category	Specialized / Compulsory
Class Format	Lecture	Credits	School Credit: 1
Department	Civil Engineering	Student Grade	4th
Term	Second Semester	Classes per Week	2
Textbook and/or Teaching Materials
Instructor	ISHIMATSU Kazuhito

Course Objectives

1) Understand basic statistics and probability distribution, and can apply them to social phenomena.
2) Can perform statistical processing to mathematize phenomena using various analytical methods.
3) Can build models of social phenomena using various analytical methods.

Rubric

	Ideal Level	Standard Level	Unacceptable Level
Achievement 1	Sufficiently understand basic statistics and probability distribution, and can apply them to social phenomena.	Understand basic statistics and probability distribution, and can apply them to social phenomena.	Do not understand basic statistics and probability distribution, and cannot apply them to social phenomena.
Achievement 2	Can sufficiently perform statistical processing to mathematize phenomena using various analytical methods.	Can perform statistical processing to mathematize phenomena using various analytical methods.	Cannot perform statistical processing to mathematize phenomena using various analytical methods.
Achievement 3	Can sufficiently build models of social phenomena using various analytical methods.	Can build models of social phenomena using various analytical methods.	Cannot sufficiently build models of social phenomena using various analytical methods.

Assigned Department Objectives

Teaching Method

Outline:

　For this course, a faculty member who was registered to a private think tank and was in charge of urban and regional planning and economic analysis, will give lectures on the basic concepts of probability statistics and statistical processing techniques that are needed to plan and design social capital. In addition, students will learn how to mathematize social phenomena and how to optimize systems.

Style:

Classes will be conducted in a lecture style in line with the textbook.
The overall evaluation will be based on 70% on the periodic exam, 20% on exercise assignments and report, 10% on attitude toward class activities such as Q&A sessions. The minimum score for a pass will be 60%.

Notice:

　Understand the basic concept of planning, and master it through exercises. Students will learn the basic concept of modelling of social phenomena, and the interpretation method on the results through exercise assignments, etc.
Students who miss 1/3 or more of classes will not be eligible for evaluation.

Characteristics of Class / Division in Learning

Active Learning	Aided by ICT	Applicable to Remote Class	Instructor Professionally Experienced

Course Plan

Evaluation Method and Weight (%)

	Exams	Exercise Assignment Report	Mutual Evaluations	Attitude Toward Class Activities	Portfolio	Other	Total
Subtotal	70	20	0	10	0	0	100
Basic Proficiency	0	0	0	0	0	0	0
Specialized Proficiency	70	20	0	10	0	0	100
Cross-Disciplinary Proficiency	0	0	0	0	0	0	0

			Theme	Goals
2nd Semester
	3rd Quarter
		1st	Basic statistics A lecture will be given on sampling theory, interval estimation, and sampling.	Can explain about sampling theory, interval estimation, and sampling.
		2nd	Probability distribution I Explain and derive hypergeometric distribution, binomial distribution, multinomial distribution, Poisson distribution, and Gumbel distribution.	Understand and can explain the derivation of hypergeometric distribution, binomial distribution, multinomial distribution, Poisson distribution, and Gumbel distribution.
		3rd	Probability distribution II Explain the relationship of distributions of discrete probability distribution uniformly, and apply to social phenomena.	Understand the relationship of distributions of discrete probability distribution uniformly, and can apply to social phenomena.
		4th	Probability distribution III Conduct exercise for probability distribution. In addition, students will review normal distributions which they have learned in year 2 mathematical exercise.	Understand exercise problems for various probability distributions.
		5th	Correlation analysis Ⅰ Explain and conduct exercise on correlation coefficient, correlation ratio, and attribute correlation, which are the indicators that show the degree of correlation among labels and among individual pieces.	Can explain about correlation coefficient, correlation ratio, and attribute correlation, which are the indicators that show the degree of correlation among labels and among individual pieces.
		6th	Correlation analysis Ⅱ Explain and conduct exercise on correlation coefficient, correlation ratio, and attribute correlation, which are the indicators that show the degree of correlation among labels and among individual pieces.	Can explain about correlation coefficient, correlation ratio, and attribute correlation, which are the indicators that show the degree of correlation among labels and among individual pieces.
		7th	Analysis of variance I Quantitatively analyze the effect of factors, and explain the analysis of variance (concept, factors, standard effects, model structure, and analysis table for analysis of variance).	Can quantitatively analyze the effect of factors, and can explain the analysis of variance (concept, factors, standard of effects, model structure, and analysis table for analysis of variance).
		8th	Analysis of variance II Explain analysis of variance along with examples, based on the F-distribution test and the interpretation of the results of model structure, effect of factors, and variance ratio.	Can evaluate using the F-distribution test and the interpretation of the results of model structure, effect of factors, and variance ratio.
	4th Quarter
		9th	Design of experiments Ⅰ Explain about the design of experiment method that can simplify complex experiments taking multiple factors into account using the orthogonal tables, and conduct exercises.	Can explain about the design of experiment method that can simplify complex experiments taking multiple factors into account using the orthogonal tables.
		10th	Design of experiments II Explain about the design of experiment method that can simplify complex experiments taking multiple factors into account using the orthogonal tables, and conduct exercises.	Can explain about the design of experiment method that can simplify complex experiments taking multiple factors into account using the orthogonal tables.
		11th	Regression analysis I A lecture will be given on the method of least squares of regression analysis. The first class of regression analysis will be on single regression analysis.	Can explain the method of least squares of single regression analysis.
		12th	Regression analysis II A lecture will be given on the degree of conformity and significance, and on the derivation of normal equation, which is the conditional formula for determining parameters. Extension of the single regression analysis to the multiple regression analysis.	Can explain about the degree of conformity and significance, and on the derivation of normal equation, which is the conditional formula for determining parameters.
		13th	Regression analysis III Demonstrate how to apply the regression analysis to various curve data, and derive the normal equation.	Understand how to apply the regression analysis to various curve data, and can derive the normal equation.
		14th	Multiple regression analysis exercise I Explain about the theory of multiple regression analysis, and conduct analysis exercise in the classroom.	Can explain about the theory of multiple regression analysis.
		15th	Multiple regression analysis exercise II The second class of multiple regression analysis will be carrying out calculations and analysis at the Information Center.	Can solve practice problems on multiple regression analysis.
		16th	Final exam