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      Angular and Metric Distance in Road Network Analysis: A nationwide correlation study

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      Computers, Environment and Urban Systems
      Elsevier BV

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          Tests for comparing elements of a correlation matrix.

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            Toward using confidence intervals to compare correlations.

            G Zou (2007)
            Confidence intervals are widely accepted as a preferred way to present study results. They encompass significance tests and provide an estimate of the magnitude of the effect. However, comparisons of correlations still rely heavily on significance testing. The persistence of this practice is caused primarily by the lack of simple yet accurate procedures that can maintain coverage at the nominal level in a nonlopsided manner. The purpose of this article is to present a general approach to constructing approximate confidence intervals for differences between (a) 2 independent correlations, (b) 2 overlapping correlations, (c) 2 nonoverlapping correlations, and (d) 2 independent R2s. The distinctive feature of this approach is its acknowledgment of the asymmetry of sampling distributions for single correlations. This approach requires only the availability of confidence limits for the separate correlations and, for correlated correlations, a method for taking into account the dependency between correlations. These closed-form procedures are shown by simulation studies to provide very satisfactory results in small to moderate sample sizes. The proposed approach is illustrated with worked examples. Copyright (c) 2008 APA.
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              Natural movement: or, configuration and attraction in urban pedestrian movement

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                Author and article information

                Contributors
                Journal
                Computers, Environment and Urban Systems
                Computers, Environment and Urban Systems
                Elsevier BV
                01989715
                March 2019
                March 2019
                : 74
                : 194-207
                Article
                10.1016/j.compenvurbsys.2018.11.003
                b1d30dab-e70f-4d15-9e9a-158a61241a2c
                © 2019

                https://www.elsevier.com/tdm/userlicense/1.0/

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