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Journal Article

Use of the chi-square test for the analysis of orientation data Available to Purchase

C. K. Ballantyne, R. Cornish
Published: 01 September 1979
Journal of Sedimentary Research (1979) 49 (3): 773–776.
DOI: https://doi.org/10.1306/212F7842-2B24-11D7-8648000102C1865D
...C. K. Ballantyne; R. Cornish Abstract The chi-square test has been extensively employed by geologists and geomorphologists as a measure of the strength or statistical significance of preferred trends within orientation data. The x 2 value obtained is dependent on arbitrary selection of the pattern...
View PDF for article title, Use of the <span class="search-highlight">chi</span>-<span class="search-highlight">square</span> test for the analysis of orientation data
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Journal Article

Use of Chi-Square for the Identification of Peaks in Orientation Data: Comment Available to Purchase

A. M. HAY, M. A. ABDEL RAHMAN
Journal: GSA Bulletin
Published: 01 December 1974
GSA Bulletin (1974) 85 (12): 1963–1966.
DOI: https://doi.org/10.1130/0016-7606(1974)85<1963:UOCFTI>2.0.CO;2
View PDF for article title, Use of <span class="search-highlight">Chi</span>-<span class="search-highlight">Square</span> for the Identification of Peaks in Orientation Data: Comment
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Journal Article

Use of Chi-Square on Percentage Orientation Data: Discussion Available to Purchase

J. M. GRAY
Journal: GSA Bulletin
Published: 01 May 1974
GSA Bulletin (1974) 85 (5): 833.
DOI: https://doi.org/10.1130/0016-7606(1974)85<833a:UOCOPO>2.0.CO;2
View PDF for article title, Use of <span class="search-highlight">Chi</span>-<span class="search-highlight">Square</span> on Percentage Orientation Data: Discussion
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Journal Article

Use of Chi-Square on Percentage Orientation Data: Reply Available to Purchase

PAUL W. WILLIAMS
Journal: GSA Bulletin
Published: 01 May 1974
GSA Bulletin (1974) 85 (5): 833–834.
DOI: https://doi.org/10.1130/0016-7606(1974)85<833b:UOCOPO>2.0.CO;2
View PDF for article title, Use of <span class="search-highlight">Chi</span>-<span class="search-highlight">Square</span> on Percentage Orientation Data: Reply
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Chi‐square quantile–quantile plots for (a) ASIH–EPDV, (b) EPDH–ASIV, (c) PGAH–PGVV, and (d) PGVH–PGAV. ASI, acceleration spectrum intensity; H, horizontal; PGA, peak ground acceleration; PGV, peak ground velocity; and V, vertical.

Chi‐square quantile–quantile plots for (a)  ASI H – EPD V , (b)... Available to Purchase

Published: 17 November 2022
Figure 3. Chi‐square quantile–quantile plots for (a)  ASI H – EPD V , (b)  EPD H – ASI V , (c)  PGA H – PGV V , and (d)  PGV H – PGA V . ASI, acceleration spectrum intensity; H, horizontal; PGA, peak ground acceleration; PGV, peak ground velocity
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Values for the maximum likelihood (ML) chi-square test and log odds ratio. ... Available to Purchase

Published: 01 November 2020
Table 2. Values for the maximum likelihood (ML) chi-square test and log odds ratio. Calculated from 2 × 2 contingency tables of traits of extant species that have a fossil record within or missing from Nukumaruan (2.4 Ma) and Castlecliffian (1.63 Ma) to recent. None of the p- values
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Chi-square quantile-quantile plots of distribution of Mahalanobis distances of PGA MS-AS (a) intraevent residuals and (b) interevent residuals.

Chi-square quantile-quantile plots of distribution of Mahalanobis distances... Available to Purchase

Published: 01 February 2019
Figure 7. Chi-square quantile-quantile plots of distribution of Mahalanobis distances of PGA MS-AS (a) intraevent residuals and (b) interevent residuals.
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Chi square distribution for generated five equations using Indian and Ameri... Available to Purchase

Published: 01 July 2018
Table 2. Chi square distribution for generated five equations using Indian and American database India America No. of data: 34 No. of data: 82 Degree of Freedom: 133 Degree of Freedom: 181 Critical Chi-square value 99.99% Critical Chi-square value 99.99
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Chi square distribution for generated five equations using Indian and Ameri... Available to Purchase

Published: 01 July 2018
Table 2. Chi square distribution for generated five equations using Indian and American database India America No. of data: 34 No. of data: 82 Degree of Freedom: 133 Degree of Freedom: 181 Critical Chi-square value 99.99% Critical Chi-square value 99.99
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Changes in the GPS velocity reduced chi‐square misfit as a function of slip rate on a few selected faults in the WUS region.

Changes in the GPS velocity reduced chi‐square misfit as a function of slip... Available to Purchase

Published: 10 October 2017
Figure 14. Changes in the GPS velocity reduced chi‐square misfit as a function of slip rate on a few selected faults in the WUS region.
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Chi-square distributions 1, 2 are transformed to the Gaussian distributions by the anamorphosis function, merged in the Gaussian space (distribution 3) using the error ellipse approach and back-transformed to the chi-square distribution by the inverse of the anamorphosis function . The vertical axis is the standard chi-square distribution function and the horizontal axis is the standard normal distribution function.

Chi-square distributions 1, 2 are transformed to the Gaussian distributions... Available to Purchase

Published: 27 July 2017
Fig. 27. Chi-square distributions 1, 2 are transformed to the Gaussian distributions by the anamorphosis function, merged in the Gaussian space (distribution 3) using the error ellipse approach and back-transformed to the chi-square distribution by the inverse of the anamorphosis function
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Chi-square distributions 1, 2 are transformed to the Gaussian distributions by the anamorphosis function, merged in the Gaussian space (distribution 3) using the error ellipse approach and back-transformed to the chi-square distribution by the inverse of the anamorphosis function . The vertical axis is the standard chi-square distribution function and the horizontal axis is the standard normal distribution function.

Chi-square distributions 1, 2 are transformed to the Gaussian distributions... Available to Purchase

Published: 27 July 2017
Fig. 27. Chi-square distributions 1, 2 are transformed to the Gaussian distributions by the anamorphosis function, merged in the Gaussian space (distribution 3) using the error ellipse approach and back-transformed to the chi-square distribution by the inverse of the anamorphosis function
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Empirical chi‐square regressions (black lines) between Mw, from moment tensor catalogs and ML as reported by (a) ISIDe (computed using the Hutton and Boore (1987) function and the theoretical WA magnification factor 2800), and as computed by the piecewise linear functions determined in this work for (b) one and (c) two regions (using the corrected WA magnification factor 2080). Gray lines indicate the exact 1:1 correspondence between ML and Mw. σMw and σML are the average uncertainties of Mw and ML observations, respectively, η is the average variance ratio for chi‐square regression, N is the number of data, σCSQ is the standard deviation of residuals and ΔMw−ML is the average offset between Mw and ML.

Empirical chi‐square regressions (black lines) between M w , from moment ... Available to Purchase

Published: 08 July 2015
Figure 6. Empirical chi‐square regressions (black lines) between M w , from moment tensor catalogs and M L as reported by (a) ISIDe (computed using the Hutton and Boore (1987) function and the theoretical WA magnification factor 2800), and as computed by the piecewise linear functions
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(a) The reduced chi‐square () plot as a function of the regularization weighting parameters (λ1, model smoothness constraint; λ2, for total potency constraint). (b–e) Different realizations of models. The best model is chosen based on the L‐curve knees and on the proximity of values to unity.

(a) The reduced chi‐square ( ) plot as a function of the regularization w... Available to Purchase

Published: 14 October 2014
Figure 6. (a) The reduced chi‐square ( ) plot as a function of the regularization weighting parameters ( λ 1 , model smoothness constraint; λ 2 , for total potency constraint). (b–e) Different realizations of models. The best model is chosen based on the L‐curve knees and on the proximity
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(a) The reduced chi‐square () plot as a function of the regularization weighting parameters (λ1, model smoothness constraint; λ2, for total potency constraint). (b–e) Different realizations of models. The best model is chosen based on the L‐curve knees and on the proximity of values to unity.

(a) The reduced chi‐square ( ) plot as a function of the regularization w... Available to Purchase

Published: 14 October 2014
Figure 6. (a) The reduced chi‐square ( ) plot as a function of the regularization weighting parameters ( λ 1 , model smoothness constraint; λ 2 , for total potency constraint). (b–e) Different realizations of models. The best model is chosen based on the L‐curve knees and on the proximity
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(a) The reduced chi‐square () plot as a function of the regularization weighting parameters (λ1, model smoothness constraint; λ2, for total potency constraint). (b–e) Different realizations of models. The best model is chosen based on the L‐curve knees and on the proximity of values to unity.

(a) The reduced chi‐square ( ) plot as a function of the regularization w... Available to Purchase

Published: 14 October 2014
Figure 6. (a) The reduced chi‐square ( ) plot as a function of the regularization weighting parameters ( λ 1 , model smoothness constraint; λ 2 , for total potency constraint). (b–e) Different realizations of models. The best model is chosen based on the L‐curve knees and on the proximity
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Chi‐square analysis to determine the best‐fitting straight‐line slope to the large‐earthquake (A&gt;537  km2 and M&gt;6.71) data of Hanks and Bakun (2002, 2008), shown here in Figure 3. The chi‐square minimum occurs at a slope of 1.25, which results in equation (12) for the large‐earthquake data.

Chi‐square analysis to determine the best‐fitting straight‐line slope to th... Available to Purchase

Published: 05 August 2014
Figure 2. Chi‐square analysis to determine the best‐fitting straight‐line slope to the large‐earthquake ( A >537  km 2 and M >6.71) data of Hanks and Bakun (2002 , 2008 ), shown here in Figure  3 . The chi‐square minimum occurs at a slope of 1.25, which results in equation  (12
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Chi-square test ρ values for comparisons of the distribution of specific da... Available to Purchase

Published: 01 July 2014
Table 6.— Chi-square test ρ values for comparisons of the distribution of specific damage and encrustation levels, shell wear, and organism maturity in Nautilus assemblages from Bays 1–3 and the 2008 collection. Bold values indicate that the assemblage is significantly different (ρ < 0.05).
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Chi-square plot displayed with gx.md.plot from the output saved from gx.mva applied to the element major and trace element data for the 0–5 cm &lt;2 mm soils, Maritime Provinces, following an isometric log-ratio transformation.

Chi-square plot displayed with gx.md.plot from the output saved from gx.... Available to Purchase

Published: 15 November 2013
Fig. 15. Chi-square plot displayed with gx.md.plot from the output saved from gx.mva applied to the element major and trace element data for the 0–5 cm <2 mm soils, Maritime Provinces, following an isometric log-ratio transformation.
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Chi-square plots displayed with gx.md.plot from the output saved from gx.robmva.closed applied to the major and trace element data for the 0–5 cm &lt;2 mm soils, Maritime Provinces.

Chi-square plots displayed with gx.md.plot from the output saved from gx... Available to Purchase

Published: 15 November 2013
Fig. 16. Chi-square plots displayed with gx.md.plot from the output saved from gx.robmva.closed applied to the major and trace element data for the 0–5 cm <2 mm soils, Maritime Provinces.