Chapter
2.1.3 Systematic sampling
2.1.4 Stratified sampling
2.2.1 Frequency distributions and histograms
2.3 Summarizing data numerically
2.3.1 Measures of central tendency: mean, median and mode
2.3.5 Measures of dispersion
2.3.8 Coefficient of variation
2.3.9 Skewness and kurtosis
Chapter 3 Probability and sampling distributions
3.1.1 Probability, statistics and random variables
3.1.2 The properties of the normal distribution
3.2 Probability and the normal distribution: z-scores
3.3 Sampling distributions and the central limit theorem
Chapter 4 Estimating parameters with confidence intervals
4.1 Confidence intervals on the mean of a normal distribution: the basics
4.2 Confidence intervals in practice: the t-distribution
4.4 Confidence intervals for a proportion
Chapter 5 Comparing datasets
5.1 Hypothesis testing with one sample: general principles
5.1.1 Comparing means: one-sample z-test
5.1.3 General procedure for hypothesis testing
5.2 Comparing means from small samples: one-sample t-test
5.3 Comparing proportions for one sample
5.4 Comparing two samples
5.4.1 Independent samples
5.4.2 Comparing means: t-test with unknown population variances assumed equal
5.4.3 Comparing means: t-test with unknown population variances assumed unequal
5.4.4 t-test for use with paired samples (paired t-test)
5.4.5 Comparing variances: F-test
5.5 Non-parametric hypothesis testing
5.5.1 Parametric and non-parametric tests
5.5.2 Mann–whitney U-test
Chapter 6 Comparing distributions: the Chi-squared test
6.1 Chi-squared test with one sample
6.2 Chi-squared test for two samples
Chapter 7 Analysis of variance
7.1 One-way analysis of variance
7.2 Assumptions and diagnostics
7.3 Multiple comparison tests after analysis of variance
7.4 Non-parametric methods in the analysis of variance
7.5 Summary and further applications
8.2 Pearson’s product-moment correlation coefficient
8.3 Significance tests of correlation coefficient
8.4 Spearman’s rank correlation coefficient
8.5 Correlation and causality
Chapter 9 Linear regression
9.1 Least-squares linear regression
9.3 Choosing the line of best fit: the ‘least-squares’ procedure
9.4 Analysis of residuals
9.5 Assumptions and caveats with regression
9.6 Is the regression significant?
9.7 Coefficient of determination
9.8 Confidence intervals and hypothesis tests concerning regression parameters
9.8.1 Standard error of the regression parameters
9.8.2 Tests on the regression parameters
9.8.3 Confidence intervals on the regression parameters
9.8.4 Confidence interval about the regression line
9.9 Reduced major axis regression
Chapter 10 Spatial Statistics
10.1.1 Types of Spatial Data
10.1.2 Spatial Data Structures
10.2 Summarizing Spatial Data
10.2.2 Weighted Mean Centre
10.2.3 Density Estimation
10.3 Identifying Clusters
10.3.2 Nearest Neighbour Statistics
10.4 Interpolation and Plotting Contour Maps
10.5 Spatial Relationships
10.5.1 Spatial Autocorrelation
Chapter 11 Time series analysis
11.1 Time series in geographical research
11.2 Analysing time series
11.2.1 Describing time series: definitions
11.2.2 Plotting time series
11.2.3 Decomposing time series: trends, seasonality and irregular fluctuations
11.2.5 Removing trends (‘detrending’ data)
11.2.6 Quantifying seasonal variation
Appendix A: Introduction to the R package
A.7 Readingand writing data
Appendix B: Statistical tables
Statistical Analysis of Geographical Data
：An Introduction
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