Regression Analysis by Example: Chatterjee/Regression

By S. Chatterjee, Ali S. Hadi. 2006

652 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/79a32d9229942897b4c833995a4ef0918b5b7e67

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In Computer Networks and Communications

In Statistics and Probability

Citations in the corpus

sdm: a reproducible and extensible R platform for species distribution modelling (2016, background): “A VIF greater than 10 (as a rule of thumb) is a signal that the model has a collinearity problem (Chatterjee and Hadi 2006).”
Under Positive Pressure (2016, background): “As a check for multicollinearity, the variance inflation factors (VIF) should be lower than 10 (Chatterjee & Hadi, 2006).”
Key indicators of Arctic climate change: 1971–2017 (2019, methodology): “Our use of the term ‘change’ refers to the magnitude of linear trends assessed by standard least squares regression (Chatterjee and Hadi 2006), that is, the regression slope multiplied by the duration of data in years.”
The effect of airport atmospherics on satisfaction and behavioral intentions: testing the moderating role of perceived safety (2017, methodology): “The Durbin– Watson test was also undertaken to confirm autocorrelation among residuals, resulting in the satisfactory value ranging from 0 to 4 (Chatterjee & Hadi, 2006).”
Individual ambidexterity: the duality of exploration and exploitation and its relationship with innovative performance (2017, background): “Common recommendations for the evaluation of VIF values argue that values below 5–10 indicate unproblematic collinearity (Chatterjee & Hadi, 2006; Marquardt, 1970; Stine, 1995).”
Is Tree Species Diversity or Species Identity the More Important Driver of Soil Carbon Stocks, C/N Ratio, and pH? (2016, methodology): “211 To avoid the risk of including highly correlated explanatory variables, we calculated variance inflation 212 factors (VIF) to assess multicollinearity and found VIF less than 5 in all models which indicates no 213 collinearity effects (Chatterjee and Hadi, 2006).”
Allometric models for non-destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora) (2008, methodology): “In this context, the ordinary least square method for estimating the model coefficients cannot be reliably used unless corrective action for removing heteroscedasticity needs to be adopted (Chatterjee & Hadi, 2006).”