The course includes the basic theory, method, application example and professional software operations of environmental data. The correct statistical analysis of environmental data analysis provides reliable basis and methods for environmental management, environmental criteria, and environmental assessment. A good foundation of data analysis for the sustainable development of students is laid in this course, who are good at data analysis later do industries and scientific research in finance, Internet/e-commerce, computer, medicine, etc. Students taking the course could understand the basic principles of data analysis, analyzed process and result analysis, etc., and can carry out corresponding data collection for specific data, select appropriate data analyzed methods, proficiently operate data analysis software, and clarify final conclusions or results, to solve practical problems or provide guidance.
Overview
Syllabus
- Chapter 1 Welcome to “Environmental Data Analysis”
- 1.1 Data? Data!
- 1.2 How you look like?
- 1.3 Why I take this course?
- 1.4 R related software installation and code running
- 1.5 SPSS Software installation
- 1.6 SPSS Data input-questionnaire
- Chapter 2 Descriptive statistics
- 2.1 Frequencies & Histograms
- 2.2 Central tendency
- 2.3 Statistical dispersion
- 2.4 Shapes of distribution
- 2.5 R Data visualization
- 2.6 ArcGIS Histogram in spatial data display and statistic
- 2.7 SPSS Descriptive analysis
- Chapter 3 Estimation of parameters
- 3.1 Samples and population
- 3.2 Inferential statistics
- Chapter 4 Pre-treatment
- 4.1 Pretreatment and missing data
- 4.2 Standardization and outliers
- Chapter 5 The hypothesis tests
- 5.1 Introduction
- 5.2 The principle of small probability
- 5.3 The principle of hypothesis tests
- 5.4 Steps of hypothesis tests
- 5.5 Types of errors & Related statistics
- Midterm Test
- Chapter 6 T tests
- 6.1 T-test
- 6.2 Single-sample t-test
- 6.3 Independent-sample t-test
- 6.4 Dependent (Paired)-sample t-test
- 6.5 SPSS t-test
- 6.6 SPSS Nonparametric t-test
- 6.7 R t-test
- 6.8 R Nonparametric t-test
- Chapter 7 ANOVA
- 7.1 ANOVA
- 7.2 One-way ANOVA
- 7.3 No interaction effect in two-way ANOVA
- 7.4 Interaction effect in two-way ANOVA
- 7.5 R One-way ANOVA
- 7.6 R Two-way ANOVA
- 7.7 SPSS One-way ANOVA
- 7.8 SPSS Two-way interaction ANOVA
- Chapter 8 Analysis of correlation
- 8.1 Analysis of correlation
- 8.2 The methods for the analysis of correlation: Bi-variances
- 8.3 Partial correlation & Distant correlation
- 8.4 R Two variables correlations
- 8.5 R Partial correlation
- 8.6 SPSS Two variables correlations & SPSS Partial correlation
- 8.7 ArcGIS Moran's I
- Chapter 9 Regression analysis
- 9.1 Regression analysis
- 9.2 Linear regression
- 9.3 Goodness of fit
- 9.4 Non-linear regression
- 9.5 SPSS Simple linear regression
- 9.6 SPSS Nonlinear regression analysis
- 9.7 R Simple linear regression
- 9.8 R Nonlinear regression analysis
- 9.9 ArcGIS Linear regression trend
- Chapter 10 Cluster analysis
- 10.1 Overview of cluster analysis
- 10.2 K-means clustering analysis
- 10.3 Hierarchical methods
- 10.4 SPSS Hierarchical cluster analysis
- 10.5 ArcGIS Clustering analysis
- 10.6 R Cluster analysis
- Chapter 11 Principal component analysis
- 11.1 Principal component analysis
- 11.2 ArcGIS Principal component analysis
- 11.3 R Principal component analysis
- End-term exam
Taught by
Inner Mongolia University
