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Contents

1 Data Science
1-0 Tools of Data Science (G7, W7, M7)
1-0-1 Softwares of Data Science
1-0-2 Difference from Hopes
1-0-3 Selection of Methods
1-0-3-1 Difficulty of Complicated Models
1-0-3-2 Robust Analysis
1-0-4 Isolation of Data, Methods and Indexes

Find From Data (1)
1-1 Statistics
1-1-0 Strength and Weakness of Big Data
1-1-1 Normal Distribution and Others
1-1-1-1 Made by Normal Distribution But Not Normal Distribution
1-1-1-2 Extreme Value Statistics
1-1-2 Statistical Value
1-1-2-1 Average and Median
1-1-2-2 Standard Deviation
1-1-3 Hypothesis Testing
1-1-3-0 Type I error and Type II error
1-1-3-1 Hypothesis Testing for Diffrence of Average
1-1-3-1-1 ANOVA
1-1-3-1-2 Paired t-test
1-1-3-2 Hypothesis Testing for Diffrence of Dispersion
1-1-3-3 Hypothesis Testing for Difference of Ratio
1-1-4 Estimation
1-1-5 Information Theory
1-1-5-1 Information Statistical Mechanics
1-1-6 Bayesian Statistics
1-1-6-1 Generative Model and Discriminative Model
1-1-6-2 Hierarchical Bayes Model

Find From Data (2)
1-2 Multi-Variable Analysis
1-2-1 Regression Analysis
1-2-1-0 Scatter Plot and Correlation
1-2-1-0-1 Faults When We Find Correlation
1-2-1-0-2 Application of Suspected Correlation
1-2-1-1 Single Regression Analysis
1-2-1-1-1 Regression Analysis for Curve
1-2-1-1-2 Prediction Interval of Regression Analysis
1-2-1-1-3 Measurement Errors in Regression Analysis
1-2-1-2 Multi-Regression Analysis
1-2-1-2-1 Multicollinearity
1-2-1-2-1-1 Principal Component Regression Analysis
1-2-1-2-2 Selection of Variables
1-2-1-2-2-1 Sparce Modeling
1-2-1-2-3 Linearity
1-2-1-2-4 Partial Correlation
1-2-1-3 Pass Analysis
1-2-1-4 Correlation Analysis for Multi-Variable
1-2-1-4-1 Graphical Lasso
1-2-1-5 Generalized Linear Mixed Model
1-2-1-6 Gaussian Process Regression Model

1-2-2 Pattern Recognition
1-2-2-0 Confusion Matrix
1-2-2-0-1 ROC Curve and AUC
1-2-2-1 Discriminant Analysis
1-2-2-2 Logistic Regression Analysis
1-2-2-3 Support Vector Machine
1-2-2-3-1 Kernel Method
1-2-2-3-2 One-Class SVM
1-2-2-4 MT method
1-2-2-4-1 Unit Space
1-2-2-4-2 Mahalanobis' Distance
1-2-2-4-3 Process of MT method
1-2-2-4-3-1 Aprroach for Chance of MT
1-2-2-4-3-2 Principal Component MT
1-2-2-4-4 Decision of Abnormal by MT method
1-2-2-5 Difference of Good Distribution by Methods
1-2-2-6 Difference of Output by Methods

1-2-3 Principal Component Analysis
1-2-4 Multi Dimensional Scaling
1-2-5 Quantification Methods from 1 to 4
1-2-6 Factor Analysis
1-2-6-1 SEM and Covariance Structure Analysis
1-2-6-2 Independent Component Analysis
1-2-7 Analysis of Many vs Many
1-2-7-1 Matrix Decomposition
1-2-7-2 Eigenvalue Analysis
1-2-7-3 Canonical Correlation Analysis
1-2-7-4 Contingency Table and Cross Tabulation
1-2-7-5 Independence Test

Find From Data (3)
1-3 Data Mining
1-3-1 Decision Tree
1-3-2 Associations Analysis
1-3-2-1 Correlation of Category Data
1-3-2-2 Asymmetry of Quantity Data
1-3-3 Rough Sets Analysis
1-3-4 Q-Analysis
1-3-5 k-NN
1-3-5-1 LOF
1-3-6 Analysis of Similarity of Samples
1-3-6-1 Self Organizing Map
1-3-6-2 Cluster Analysis
1-3-7 Text Mining
1-3-7-1 From Sentences to Words
1-3-7-1-1 Word Cloud
1-3-7-2 Software of Text Mining
1-3-7-3 Without Dictionary
1-3-7-4 Co-occurrence
1-3-7-5 Analyze Unit of Text

Find From Data (4)
1-4Graphical Analysis
1-4-0 Software of Graphical Analysis
1-4-0-1 Graphs of Excel
1-4-0-2 ggplot2
1-4-0-3 Plotly
1-4-0-4 Plot of Panda (matplotlib)
1-4-0-5 seaborn
1-4-1 Stratified Graph
1-4-1-1 Bar Plot
1-4-2 Graphs of 1-Dimension Disribution
1-4-2-1 1-Dimension Scatter Plot
1-4-2-2 Histogram
1-4-2-3 Box Plot
1-4-3 Graph for Change
1-4-3-1 Line Graph
1-4-3-2 Heat Map
1-4-4 Graphs for 2-Dimension Distribution and Relationship
1-4-4-1 2-Dimension Scatter Plot
1-4-4-1-1 Scatter Plot of Words
1-4-4-2 Graph of Network
1-4-5 Graph for 3-Dimension

Link and Arrange Data
1-5 Data Literacy
1-5-0 Non-Developed Wide Area of Data Science
1-5-1 Database
1-5-2 Programming
1-5-2-1 Reading and Writing Programs
1-5-2-2 Program Languages

1-5-3 Feature Engineering
1-5-3-1 Probability and Random Variable Transformation
1-5-3-1-1 Logit Transformation and Probit Transformation
1-5-3-1-2 Odds Ratio
1-5-3-2 Transformation Quantity and Quality
1-5-3-2-1 Dummy Variable
1-5-3-2-1-1 Binary Number Transformation
1-5-3-2-2 Fuzzy Theory
1-5-3-3 Normalization
1-5-3-4 Analysis Using Intermediate Layer
1-5-3-5 Analysis Using Category Data
1-5-3-6 Differentiation Data and Integration Data
1-5-3-6-1 Velocity Data (Difference Data)
1-5-3-6-2 Numerical Integration
1-5-3-7 Fourier Transformation and Laplace Transformation
1-5-3-7-1 Spectrum Analysis
1-5-3-8 Treatment of Time
1-5-4 Data Physics
1-5-4-1 Data of Physics
1-5-4-2 Data of Time and Space
1-5-5 Meta Data and Meta Knowledge
1-5-5-1 Make Data of Meta Knowledge
1-5-6 Outlier and Missing Value
1-5-6-1 Cause and Effect Analysis of Outlier and Missing Value
1-5-6-2 Analysis with Outlier and Missing Value
1-5-7 Data Edit

Predict Data
1-6 Prediction and Simulation
1-6-1 Prediction by Statistical Model
1-6-1-1 Over Fitting
1-6-1-2 Extrapolation
1-6-1-3 Time Depended Extrapolation
1-6-1-4 Software for Prediction
1-6-1-5 Gap between Models of Statistics and Real
1-6-2 Mathematical Modeling
1-6-2-1 Dimension Analysis
1-6-2-2 Addition Model and Division Model
1-6-2-3 Probability Model and Deterministic Model
1-6-2-3-1 Mathematical Programming
1-6-2-3-2 Genetic Algorithm
1-6-2-4 Differential Equation Model
1-6-2-4-1 System Dynamics
1-6-3 Dispersion Model
1-6-3-1 Make Dispersion Data
1-6-3-2 Random Walk Model
1-6-4 Model of Abnormal
1-6-4-1 Model of Outlier
1-6-4-2 One-Class Model
1-6-4-3 Model of Abnormal but not Outlier

Collect Data
1-7 Measurement
1-7-0 IoT
1-7-1 Errors
1-7-1-1 Measurement Errors
1-7-1-2 Causes of Errors
1-7-1-3 Propagation of Errors
1-7-1-4 Errors and Sample Number
1-7-1-5 Use of Double Measured Data
1-7-1-5-1 Estimation of Repeatability by Double Measured Data
1-7-1-5-2 Use Double Measured Data for Pattern Recognition

1-7-2 Design of Experiments
1-7-2-1 Sampling of Experiment Data
1-7-2-1-1 Location Experiment
1-7-2-1-2 Interaction
1-7-2-1-3 Orthogonal Array
1-7-2-1-3-1 Mixed Orthogonal Array
1-7-2-1-4 Use of Category Data and Quantity Data
1-7-2-2 Analysis of Experiment Data
1-7-2-2-1 Response Surface Method
1-7-2-2-2 Think Output of Experiment
1-7-3 Sampling
1-7-3-1 Stratified Sampling
1-7-4 Significant Figures
1-7-4-1 Errors by Significant Figures
1-7-5 Questionnaire
1-7-6 Evaluation of Pairs

Think With Data
1-8 Artificial Intelligence
1-8-1 Cognitive Psychology
1-8-1-1 Heuristics
1-8-1-2 Framing
1-8-1-3 Pattern
1-8-2 Machine Learning
1-8-2-1 Supervised and Non-Supervised Learning
1-8-2-2 Reinforcement Learning
1-8-2-3 Ensemble Learning
1-8-3 Bayesian Network
1-8-3-1 Calculation of Probability by Bayesian Network
1-8-3-2 Structure Analysis by Bayesian Network
1-8-3-3 Pattern Recognition by Bayesian Network
1-8-4 Neural Network
1-8-4-1 Deep Learning
1-8-4-2 Auto Encoder
1-8-4-3 Convolutional Neural Network (CNN)
1-8-4-4 Recurrent Neural Network (RNN)
1-8-5 Cognition of Graphics and Voices
1-8-6 Autocomposition
1-8-7 Artificial Mind

Find From Data (5)
1-9Analysis of Real World
1-9-1 Cause and Effect Analysis
1-9-1-1 Quality Way of Making Hypothesis
1-9-1-1-1 Kinds of Cause and Effect (AND, OR)
1-9-1-1-2 Concept Analysis and Ontology
1-9-1-1-3 Logic
1-9-1-2 Quantity Way of Making Hypothesis
1-9-1-2-1 Making Hypothesis by Correlation
1-9-1-2-2 Relationship Between If-Then and Cause-Effect
1-9-1-2-3 Time Difference of Cause-Effect
1-9-1-3 Analysis of Proof

1-9-2 System Engineering
1-9-2-1 Control Engineering
1-9-2-1-1 Kalman Filter
1-9-2-1-2 Sequential Control
1-9-2-2 Direct problem and Inverse problem
1-9-2-3 System Thoughts
1-9-2-3-1 Analogy
1-9-2-3-2 Balance

1-9-3 Time Series Analysis
1-9-3-1 Condition Analysis
1-9-3-2 Time Analysis
1-9-3-3 Passing Analysis
1-9-3-3-1 Trend Analysis for Many Variables
1-9-3-4 Self Correlation Analysis
1-9-3-4-1 Single Self Correlation Analysis
1-9-3-4-2 AR Model and Others
1-9-3-5 Moving Analysis
1-9-3-5-1 Moving Window without Overlap
1-9-3-5-2 0-1 Data Analysis
1-9-3-6 Sensor Data Analysis
1-9-3-6-1 Sampling from Database
1-9-3-6-2 Analysis of Type 1
1-9-3-6-3 Analysis of Type 1.5
1-9-3-6-4 Analysis of Type 2 (Feature Data)
1-9-3-6-4-1 Making Type 2 Data by Excel
1-9-3-6-4-2 Making Type 2 Data by Python
1-9-3-6-5 Analysis of Type 3
1-9-3-6-6 Reverse Time Aggregation

2 Environment and Quality

2-1 Environment
2-1-0 Environmental Problems

2-1-1 Natural Environment
2-1-1-1 Eco System
2-1-1-2 Flow (Fluid Mechanics)
2-1-1-3 Change (Phase Transition and Chemical Reaction)
2-1-1-4 Energy (Thermodynamics and Exergy)
2-1-1-5 Transformation (Continuum Mechanics)
2-1-1-6 Atoms and Molecules
2-1-1-6-0 Four Elements and Chemistry
2-1-1-6-1 First Principle
2-1-1-6-1-1 Lead Free and First Principle Calculation
2-1-1-6-2 Molecular Dynamics
2-1-1-6-3 Simulated Annealing
2-1-1-6-4 Quantum Computer
2-1-1-7 Chaos
2-1-1-7-1 Attractor
2-1-1-7-2 Complex Systems

2-1-2 From Environment To Human Beings
2-1-2-1 Environmental Physiology
2-1-2-2 Environmental Psychology
2-1-2-3 Honzo and Natural History
2-1-2-4 Stone

2-1-3 From Human Beings To Environment
2-1-3-1 Environmental Assessment
2-1-3-1-0 Index of Environmental Assessment
2-1-3-1-1 LCA
2-1-3-1-2 HEP
2-1-3-2 Trash
2-1-3-3 Trades of Crops
2-1-3-4 Purifying Technology

2-1-4 Sustainable Society
2-1-4-0 Mathematical Sociology
2-1-4-1 Environmental Thought
2-1-4-1-1 Oriental Thoughts
2-1-4-1-2 Yin-Yang and Wu Xing
2-1-4-1-3 Feng shui
2-1-4-2 Environmental Laws
2-1-4-2-1 Licenses
2-1-4-3 Environmental Economics
2-1-4-3-1 Kinds of Environmental Economics
2-1-4-3-2 Inclusion of Externality
2-1-4-3-3 Methods of Inclusion
2-1-4-3-3-1 Money as an index
2-1-4-3-4 Fusion of Ecology and Economy
2-1-4-3-5 Intermediate System
2-1-4-3-6 Index of Economical Assessment
2-1-4-3-7 Economical Data Analysis
2-1-4-3-8 Environmental Kuznets Curve
2-1-4-3-9 Books of Environmental Economics

2-1-4-4 Region
2-1-4-4-1 Fuudo Technology
2-1-4-4-2 Urban Planning
2-1-4-4-3 Regional Revitalization
2-1-4-4-3-1 Mingei (Works for daily use)
2-1-4-4-4 Location (Spatial Economics)
2-1-4-4-5 Social Psychology
2-1-4-4-6 Behavioral Economics
2-1-4-4-6-1 Prospect Theory
2-1-4-4-6-2 Game Theory
2-1-4-4-7 Network
2-1-4-4-7-1 Network Analysis
2-1-4-4-7-2 Propagation on Network
2-1-4-4-7-3 Growth of Network
2-1-4-5 Quantitative Geography
2-1-4-5-1 GIS
2-1-4-5-2 Landscape Ecology
2-1-4-5-3 Spatial Statistics


2-2 Quality
2-2-1 SPC
2-2-1-1 Process Analysis for Normal Condition
2-2-1-1-1 Process Capacity
2-2-1-1-2 Sampling Inspection
2-2-1-2 Process Analysis for Abnormal Condition
2-2-1-3 Seven QC Tools and New Seven QC Tools
2-2-1-3-1 Pareto Chart
2-2-1-3-2 Control Chart
2-2-1-3-3 PERT
2-2-1-4 DMAIC and QC Story
2-2-2 TQC
2-2-2-1 TQC, TQM, Six-Sigma and Others
2-2-2-2 Data Science for the Change of Manufacturing

2-2-3 Quality Engineering
2-2-3-1 Parameter Design
2-2-3-1-1 Robust Design
2-2-3-1-1-1 SN Ratio
2-2-3-1-1-2 Two Step Analysis
2-2-3-1-1-3 Kinds of Unevennes
2-2-3-1-1-4 "Functionality" of Quality Engineering
2-2-3-1-2 Classification of Characteristics
2-2-3-1-2-1 Difference of Static and Dynamic
2-2-3-1-2-2 Characteristics and SN Ratio
2-2-3-1-2-3 Dynamic SN Ratio
2-2-3-1-2-4 Dynamic Characteristics and Non-linearity
2-2-3-1-2-5 Standard SN Ratio
2-2-3-1-3 Classification of factors
2-2-3-1-3-1 Disturbance and Error Factor
2-2-3-1-3-2 Applied Experiment of Signal Factor
2-2-3-1-4 Outside Arrangement of Orthogonal Array
2-2-3-2 Process Control
2-2-3-3 MT System
2-2-3-4 Words of Quality Engineering
2-2-3-4-1 Future of Quality Engineering
2-2-3-4-2 "Energy" of Quality Engineering
2-2-3-4-3 "T Method" of Quality Engineering

2-2-4 Reliability Engineering
2-2-4-1 Destruction Engineering
2-2-5 Industrial Engineering
2-2-5-1 IE-method
2-2-5-2 Toyota Production System
2-2-5-3 Loss Time
2-2-6 Quality for Environment


2-3 Risk
Kinds of Risk
2-3-1 Risk of Chemical Substances
2-3-1-1 Epidemiology
2-3-2 Risk of Machines
2-3-3 Risk of Finance

Methods of Risk
2-3-4 Risk Management
2-3-5 Risk Cognition
2-3-6 Risk Evaluation
2-3-7 Risk Communication


2-4 Management
2-4-0 Management Engineering
2-4-1 With Management
2-4-1-1 SR (CSR)
2-4-1-2 Standard and ISO
2-4-2 Customer Relationship
2-4-2-1 Marketing
2-4-2-1-1 Brand
2-4-2-1-2 Marketing Science
2-4-2-2 Design
2-4-2-2-1 Value Engineering
2-4-2-2-2 QFD (Quality Function Deployment)
2-4-2-2-3 Sensitivity Engineering
2-4-2-3 Inventory Control
2-4-2-3-1 Reordering Point System
2-4-2-3-2 Kanban System
2-4-3 To Move the Company
2-4-3-1 Management System for Environment, Quality and Risk
2-4-3-2 Solve Problems
2-4-3-2-1 Teaming
2-4-3-2-2 Process to Solve Problems
2-4-3-2-3 Thinking
2-4-3-3 Normalization
2-4-3-4 Management Accounting
2-4-3-5 Decision Making
2-4-3-5-1 Statistical Decision Making
2-4-3-5-2 Psychology for Decision Making
2-4-3-5-3 AHP
2-4-4 Analysis of Management
2-4-4-1 Financial Analysis
2-4-4-2 Profitability Analysis
2-4-4-2-1 Cost Analysis
2-4-4-3 Corporate Value
2-4-4-4 Theory Of Constraints
2-4-4-5 Reengineering