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82 4 Bivariate Statistics
Curvilinear Regression
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700
600
i-th data point
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Barium content (%) 400 95% Confidence Bounds
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95% Confidence Bounds
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Regression line
ï100
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0 2 4 6 8 10 12 14 16 18 20
Depth in sediment (meters)
Fig. 4.8 Curvilinear regression from barium contents. The plot shows the original data points
(plus signs), the regression line for a polynomial of degree n=2 (solid line) as well as the error
bounds (dashed lines) of the regression.
The plot nicely shows that the quadratic model for this data is a good one.
The quality of the result could again be tested by exploring the residuals,
employing resampling schemes or cross validation. The combination of re-
gression analysis with one of these methods represent a powerful tool in
bivariate data analysis, whereas Pearson·s correlation coeffi cient should be
used only as a first test for linear relationships.
Recommended Reading
Alberède F (2002) Introduction to Geochemical Modeling. Cambridge University Press
Davis JC (2002) Statistics and data analysis in geology, third edition. John Wiley and Sons,
New York
Draper NR, Smith, H (1998) Applied Regression Analysis. Wiley Series in Probability and
Statistics, John Wiley & Son
Efron B (1982) The jackknife, the bootstrap, and other resampling plans. Society of