Spatial Analysis GEOG 5670
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This 3 page Class Notes was uploaded by Dr. Cristopher Deckow on Wednesday September 30, 2015. The Class Notes belongs to GEOG 5670 at Western Michigan University taught by Staff in Fall. Since its upload, it has received 35 views. For similar materials see /class/216847/geog-5670-western-michigan-university in Geography at Western Michigan University.
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Date Created: 09/30/15
1 Elementary Statistical Concepts GEOG567 Data Considerations Types of data Primary data Acquired directly from the original source Secondary data Collected by organization and provided to user 3 Levels of Measurement Nominal categorical data 7 Oualita we a a nut greaterthan ur less tnaniust mrrerent rmrn each Either r Binarydichotomousvanables Ordinal Data 7 Data are ranked with respect to one another Rankingisrreduentlysubjective Intervalratio Data 7 Differences between tWO observations can be measured on a scale The same data can be expressed at different measurement scales 7 Data are ALMOST never down raded Unless assumptiuns ur surne statistical tests are Vlulated 4 Measurement Error Four basic sources of measurement error Human error in data collection Errors of method poorly constructed variable Systematic errors bias values are consistently too high or low Random errors uctuation attributable to chance We want data to be accurate and precise We want to use VALID variables and reliable consistent and stable data Parametric tests Used only with intervalratio data Most powerful statistical tests Usually data must satisfy certain assumptions normal distribution random Examples ttests Pearson s correlation regression factorprincipal components analysis cluster analysis 6 Nonparametric tests Used with nominal and ordinal data Also used when intervalratio data do not meet assumptions Many parametric tests have noonparametric equivalents Examples Chisquare Wilcoxon test MannWhitney test Runs test Spearman s correlation 7 Descriptive statistics Quantitative summary ofthe characteristics of some variable Usually summarized as a single value Examples Mean median standard deviation coefficient of variation lnferential statistics Use laws of probability and information obtained from a sample of a given population to make inferences generalizations about that population Use descriptive statistics obtained from samples to make statements about the characteristics of the population from which the sample is taken 9 Population vs sample Population Total set ofinformation or data under investigation Greek symbols Sam Ie Subset of data that is typical or representative of the entire population Sample statistics are used in inferential statistics to make generalizations about a population Population parameters vs sample statistics When sample data are used to estimate a population parameter n1 is used in the denominator Correction factor so variance is not underestimated One degree of freedomquot is lost 10 Descriptive Statistics Descriptive statistics provide summary measures for populations and samples Central tendency Mean mode median Measures of dispersion Variance standard deviation Skewness Kurtosis 11 Central tendency 12 Mode Mo Value that occurs most frequently in a group of observations More than one mode is possible b39modal Point at which he frequency distribu ion curve peaks Median Md Middle value of a set of observations that have been ranked Equal number of observations above and below Mean Usually most appropriate measure for intervalratio data Sum ofobservation values divided by the number of observa ions Measures of Dispersion variability How spread out are the data Range Mean deviation Variance Standard deviation Coefficient of variation Standard error ofthe mean 13 Descriptive statistics for spatial data Mean center Standard distance
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