Graphs in Statistical Analysis

By A. Hanna, C. Rao, Thanos Athanasiou. 2010

639 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e6d2d0f5ed8895579c867cd2cea20ddb2f00ede3

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In Statistics and Probability

Citations in the corpus

Robust Correlation Analyses: False Positive and Power Validation Using a New Open Source Matlab Toolbox (2012, background): “As put forward by Anscombe (1973), plotting the data is an important part of any statistical analysis (Figure 1, left column).”
How Well Do Criminologists Explain Crime? Statistical Modeling in Published Studies (2008, methodology): “This approach is particularly common in models using time-related data, where measures of the outcome in prior time periods are naturally included as 9 Anscombe (1973) makes a similar point but does so visually and quantitatively—with an R2 of .667 no less.”
Safe models for risky decisions (2017, background): “A compelling illustration of why it is important to assess absolute model performance prior to the interpretation of model parameters is given by Anscombe’s quartet (Anscombe, 1973; Andraszewicz et al., 2015), presented in Figure 6.”
Pelagic Larval Duration and Dispersal Distance Revisited (2009, background): “This type of data distribution leads to a problematic correlation analysis and, perhaps, even makes a correlation inappropriate (Anscomb, 1973).”
Handbook of Big Data (2016, background): “Consider the venerable example of Anscombe’s quartet [3].”
Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics (2020, background): “More generally, very different underlying relationships between the two variables can have the same value of the correlation coefﬁcient (Anscombe, 1973).”
Business Intelligence: Data Mining and Optimization for Decision Making (2009, background): “Confining ourselves to some relevant contributions, the reader may refer to Tukey (1977), Anscombe (1973), Velleman and Hoaglin (1981), Tufte (1983), Cleveland (1993) and Fayyad et al.”
Dirty Data: The Effects of Screening Respondents Who Provide Low-Quality Data in Survey Research (2017, background): “After data collection is complete, examination of graphical representations of the data may serve to identify aberrant response patterns (Anscombe, 1973).”
Seven steps to reliable cyclic voltammetry measurements for the determination of double layer capacitance (2020, background): “Moreover, it is important to consider that R2 does not discriminate reliable from abnormal data, nor delivers information about the distribution of the data points, as illustrated clearly by the Anscombe’s quartet [48].”
Variations in sustainable development goal interactions: Population, regional, and income disaggregation (2020, methodology): “However, using only one correlation method can lead to undetected relations (Anscombe, 1973).”
Using the coefficient of determination R2 to test the significance of multiple linear regression (2013, methodology): “In this article, we accept this assumption; thus, we will not discuss the evaluation of residuals (for additional details, see Montgomery et al. 2006; Anscombe 1973).”
Harold Jeffreys’s default Bayes factor hypothesis tests: Explanation, extension, and application in psychology (2016, background): “A Bayes factor of BF10 = 100, 000 indicates 9 thatM1 receives much more support from the data than doesM0, but this does not mean 10 thatM1 is any good in an absolute sense (e.g., Andraszewicz et al., 2015; Anscombe, 1973).”
Fitting Models to Data: Residual Analysis, a Primer (2017, background): “[29] ‘Graphs are essential to good statistical analysis’ Anscombe [30] ‘One picture says more than a thousand equations’ Sillen [31]”