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A sample of the slides available to support the teaching of the textbook Statistics for Geography and Environmental Science by Harris & Jarvis (2011). For further information see ...

A sample of the slides available to support the teaching of the textbook Statistics for Geography and Environmental Science by Harris & Jarvis (2011). For further information see www.social-statistics.org

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Sample of slides for Statistics for Geography and Environmental Science Sample of slides for Statistics for Geography and Environmental Science Presentation Transcript

  • Statistics for Geography and Environmental Science:an introductory lecture course (sample) By Richard Harris, with material by Claire Jarvis USA: http://amzn.to/rNBWd5 UK: http://amzn.to/tZ7fVu
  • Based on the textbook
  • Copyright noticeStatistics for Geography and Environmental Science:an introductory lecture course, © RichardHarris, 2011.This course is available at www.social-statistics.organd contains extracts from the publication Statisticsfor Geography and Environmental Science byRichard Harris and Claire Jarvis (Prentice Hall, 2011)You are free to modify these slides for the purpose ofnon-commercial teaching only, subject to thefollowing restrictions:– This work, or any derivative of it, may not be stored or redistributed in any form, paper or electronic, other than to be available to students for their learning and education, with access to the material restricted to the institution to which those students belong.– Any derivative must retain this copyright in full and at the beginning of the work. The words ‗Based on‘ may be inserted in the first paragraph.– Permission to waive or modify these restrictions may be sought from the author (Richard Harris, School of Geographical Sciences, University of Bristol).
  • The modulesOVERVIEW
  • The modulesModule1 makes the case for knowingabout statistics as a transferable skilland to be equipped for social andpolitical debate.Module 2 is about using descriptivestatistics and simple graphicaltechniques to explore and makesense of data.Module 3 discusses the Normalcurve, the properties of whichprovide the basis for inferential
  • The modulesModule 4 is about the principles ofresearch design and effective datacollection.Module 6 discusses the role ofhypothesis testing.Module 7 is about regressionanalysis.
  • The modulesModule 8 moves to modelling pointpatterns, ―hotspot analysis‖ and waysof measuring patterns of spatialautocorrelation in data.Module 9 looks at spatial regressionmodels, geographically weightedregression and multilevel modelling.Each module is explored more fullyin the accompanying textbook,Statistics for Geography andEnvironmental Science.
  • Module 1(Extracts from Chapter 1 of Statistics for Geographyand Environmental Science)DATA, STATISTICS ANDGEOGRAPHY
  • Module overviewTo convince you that studyingstatistics is a good idea!Our argument is that data collectionand analysis are central to thefunctioning of contemporary societyso knowledge of quantitativemethods is a necessary skill tocontribute to social and scientificdebate.
  • About statisticsStatistics are a reflective practice: away of approaching research thatrequires a clear and manageableresearch question to be formulated, ameans to answer that question,knowledge of the assumptions ofeach test used, an understanding ofthe consequences of violating thoseassumptions, and awareness of theresearcher‘s own prejudices whendoing the research.
  • Some reasons to study statisticsReasons for human geographers – Data collection and analysis are central to the functioning of society, to systems of governance and science. – Knowledge of statistics is an entry into debate, informed critique and the possibility of creating change.
  • Some reasons to study statisticsReasons for GI scientists – To address the uncertainties and ambiguities of using data analytical. – Because of the increased integration of mapping capabilities, data visualizations and (geo-) statistical analysis.
  • Some reasons to study statisticsReasons for all students – They provide a transferable skill set using in other areas of research, study and employment. – There is a recognised shortage of students with skills in quantitative methods, especially within the social sciences.
  • Types of statisticDescriptive– Used to provide a summary of a set of measurements, e.g. the average.Inferential– Use the data at hand to convey information about the population (‗the greater something‘) from which the data are drawn.Relational– Consider whether greater or lesser values in one set of data are related to greater or lesser values in another.
  • Geographical dataThese are records of what hashappened at some location on theEarth‘s surface and where.For many statistical tests the whereis largely ignored.However, it is central to geostatisticsand to spatial statistics (as theirnames suggest)
  • Some problems when analysing geographical dataStandard statistical tests assume thateach ‗bit‘ of data (each observation)has a value that is not influenced byany other.However, we may often expect thereto be geographical patterns in thedata.– Spatial autocorrelation: geographical patterns in the measurements
  • Some problems when analysing geographical dataDetermining what causes what in acomplex and dynamic natural orsocial system is extremely tricky.Two things may be associated (e.g.greater income inequality and morenon-recycled waste) without the onedirectly causing the other.
  • Some problems when analysing geographical dataData and structured forms of enquirycan only tell us so much and may notbe appropriate to some types ofresearch for which a morequalitative, participatory or lessrepresentational approach may bebetter.
  • Further readingChapter 1 of Statistics forGeography and EnvironmentalScience by Richard Harris and ClaireJarvis (Prentice Hall / Pearson, 2011)Includes a review of the followingkey concepts: types of statistics;why error is unavoidable;geographical data analysis; andspatial autocorrelation and the firstlaw of geography.
  • Module 2(Extracts from Chapter 2 of Statistics for Geographyand Environmental Science)DESCRIPTIVE STATISTICS
  • Module overviewThis module is about ―everydaystatistics‖, the sort that summarisedata and describe them in simpleways.They include the number of homeruns this season, average maleearnings, numbers unemployed,outside temperature, average cost ofa barrel of oil, regional variations incrime rates, pollution statistics,measures of the economy and other―facts and figures‖
  • Data and variablesData– A collection of observations: measurements made of something.A variable– Another name for a collection of data. Variable because it is unlikely that the data are all the same.Data types– These include discrete, continuous, and categorical data.
  • Simple ways of presenting dataDiscrete data Continuous dataFrequency table Summary tableBar chart (below) Histogram (below, with a rug plot)
  • Frequency and summary tables
  • Information to include in a summary tableMeasures of central tendency(―averages‖)– The mean and/or median • The ―centre‖ of the dataMeasures of spread and variation– The range (minimum to maximum)– The interquartile range (from ‗mid- spread‘ of the data)– The standard deviation,s
  • More about the standard deviation Essentially a measure of average variation around the mean. It is also the square root of the variance. The variance is the sum of squares divided by the degrees of freedom
  • BoxplotsAre useful forshowing themedian,interquartilerange and rangeof a set of data,for indentifyingoutliers and alsofor comparingvariables.
  • Other ways of classifying numeric data Nominal, ordinal, interval and ratio Counts and rates Proportions and percentages Parametric and non—parametric Arithmetic and geometric Primary and secondary
  • Further readingChapter 2 of Statistics for Geographyand Environmental Science by RichardHarris and Claire Jarvis (Prentice Hall /Pearson, 2011)Includes a review of the following keyconcepts: data and variables; discreteand continuous data; the range;histograms, rug plots, and stem andleaf plots; measures of centraltendency; why averages can bemisleading; quantiles; the sum ofsquares; degrees of freedom; thestandard deviation and the variance;box plots; and five and six numbersummaries
  • Thank you for your interest.