Lectures
Introduction to Information Systems, Doshisha University
infosys-01.pdf (Do-Week), infosys-02.pdf (Oct 6th), infosys-03.pdf (Oct 13th), infosys-04.pdf (Oct 20th), infosys-05.pdf (Oct 27th), infosys-06.pdf (Nov 3rd), infosys-07.pdf (Nov 10th), infosys-08.pdf (Nov 17th, Do-Week), infosys-09.pdf (Nov 17th), infosys-10.pdf (Nov 24th), infosys-11.pdf (Dec 1st), infosys-12.pdf (Dec 8th)
Mini test: minitest-08.pdf (Nov 17th, Do-Week)
知恵とはなにか（情報智慧論）
授業資料（1回目のみ）
参考資料
スケールの科学
授業資料（2026年前期掲載予定）
ダウンロード
- 世界男子スクワットの記録：man_squat_records.csv
- 日本人の平均身長と平均体重
- Zhao, Liang (2023): NHANES 2017-2018 Height Weight Data. figshare. Dataset
- Zhao, Liang (2023): Parliament size and population (2021). figshare. Dataset
BMIの計算式を検証するプロジェクト
参考資料
Introduction to Operations Research (before 2024)
Videos: フカシギの数え方, 世界一わかりやすい（？）最短路アルゴリズム
#####################################################################
# regression.R: A demo R script for Introduction to OR
# version: 1.0 (2022/07/19), liangz
# data source:
# 1. United States House of Representatives, Representatives
# Apportioned to Each State (1st to 23rd Census, 1790–2010),
# https://history.house.gov/Institution/Apportionment/state_apportionment/
# (Accessed: Jan 10, 2021).
# 2. United States Census Bureau, CPH-2, Table-4. Population: 1790–1990.
# https://www2.census.gov/programs-surveys/decennial/1990/tables/cph-2/table-4.pdf
# (Accessed: Jan 10, 2021)
#####################################################################
data <- data.frame(
Year = c(1790,1800,1810,1820,1830,1840,1850,1860,1870,1880,1890,1900,1910,1920),
Population = c(3929214,5308483,7239881,9638453,12866020,17069453,23191876,31443321,38558371,50189209,62979766,76212168,92228531,106021568),
Representatives = c(105,141,181,213,240,223,234,241,292,325,356,386,435,435),
Senates = c(30,32,34,48,48,52,62,68,74,76,88,90,96,96)
)
# First, try the linear regression
X <- data$Population
Y <- data$Representatives
model <- lm(Y ~ X)
summary(model)
plot(X,Y)
abline(model)
# => Y = 2.917e-06 X + 1.601e+02 with Adjusted R^2 = 0.9185, p-value = 4.229e-08
# Next, try the log-log regression
X <- log10(X)
Y <- log10(Y)
model <- lm(Y ~ X)
summary(model)
plot(X,Y)
abline(model)
# => Y = 10^-0.32149 X^0.36902 with Adjusted R^2 = 0.9299, p-value = 1.703e-08