Franklin

Statistical Analysis of Geographical Data : An Introduction.

Author/Creator:
Dadson, Simon James.
Publication:
Hoboken : John Wiley & Sons, Incorporated, 2017.
Format/Description:
Book
1 online resource (267 pages)
Status/Location:
Loading...

Options
Location Notes Your Loan Policy

Details

Other records:
Subjects:
SCIENCE / Earth Sciences / General.
Form/Genre:
Electronic books.
Contents:
Intro
Title Page
Copyright Page
Contents
Preface
Chapter 1 Dealing with data
1.1 The role of statistics in geography
1.1.1 Why do geographers need to use statistics?
1.2 About this book
1.3 Data and measurement error
1.3.1 Types of geographical data: nominal, ordinal, interval, and ratio
1.3.2 Spatial data types
1.3.3 Measurement error, accuracy and precision
1.3.4 Reporting data and uncertainties
1.3.5 Significant figures
1.3.6 Scientific notation (standard form)
1.3.7 Calculations in scientific notation
Exercises
Chapter 2 Collecting and summarizing data
2.1 Sampling methods
2.1.1 Research design
2.1.2 Random sampling
2.1.3 Systematic sampling
2.1.4 Stratified sampling
2.2 Graphical summaries
2.2.1 Frequency distributions and histograms
2.2.2 Time series plots
2.2.3 Scatter plots
2.3 Summarizing data numerically
2.3.1 Measures of central tendency: mean, median and mode
2.3.2 Mean
2.3.3 Median
2.3.4 Mode
2.3.5 Measures of dispersion
2.3.6 Variance
2.3.7 Standard deviation
2.3.8 Coefficient of variation
2.3.9 Skewness and kurtosis
Exercises
Chapter 3 Probability and sampling distributions
3.1 Probability
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
Exercises
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
4.3 Sample size
4.4 Confidence intervals for a proportion
Exercises
Chapter 5 Comparing datasets
5.1 Hypothesis testing with one sample: general principles.
5.1.1 Comparing means: one-sample z-test
5.1.2 p-values
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
Exercises
Chapter 6 Comparing distributions: the Chi-squared test
6.1 Chi-squared test with one sample
6.2 Chi-squared test for two samples
Exercises
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
Exercises
Chapter 8 Correlation
8.1 Correlation analysis
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
Exercises
Chapter 9 Linear regression
9.1 Least-squares linear regression
9.2 Scatter plots
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
9.10 Summary
Exercises
Chapter 10 Spatial Statistics
10.1 Spatial Data
10.1.1 Types of Spatial Data
10.1.2 Spatial Data Structures
10.1.3 Map Projections
10.2 Summarizing Spatial Data
10.2.1 Mean Centre
10.2.2 Weighted Mean Centre
10.2.3 Density Estimation
10.3 Identifying Clusters
10.3.1 Quadrat Test
10.3.2 Nearest Neighbour Statistics
10.4 Interpolation and Plotting Contour Maps
10.5 Spatial Relationships
10.5.1 Spatial Autocorrelation
10.5.2 Join Counts
Exercises
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
11.2.4 Analysing trends
11.2.5 Removing trends ('detrending' data)
11.2.6 Quantifying seasonal variation
11.2.7 Autocorrelation
11.3 Summary
Exercises
Appendix A: Introduction to the R package
A.1 Obtaining R
A.2 Simplecalculations
A.3 Vectors
A.4 Basicstatistics
A.5 Plottingdata
A.6 Multiplefigures
A.7 Readingand writing data
A.8 Summary
Appendix B: Statistical tables
References
Index
EULA.
Notes:
Description based on publisher supplied metadata and other sources.
Local notes:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Other format:
Print version: Dadson, Simon James Statistical Analysis of Geographical Data
ISBN:
9781118525142
9780470977033
OCLC:
971021188