"One feature [...] which requires much more justification than is usually given, is the setting up of unplausible null hypotheses. For example, a statistician may set out a test to see whether two drugs have exactly the same effect, or whether a regression line is exactly straight. These hypotheses can scarcely be taken literally." (Cedric A B Smith, "Book review of Norman T J Bailey: Statistical Methods in Biology", Applied Statistics 9, 1960)
"The method of least squares is used in the analysis of data from planned experiments and also in the analysis of data from unplanned happenings. The word 'regression' is most often used to describe analysis of unplanned data. It is the tacit assumption that the requirements for the validity of least squares analysis are satisfied for unplanned data that produces a great deal of trouble." (George E P Box, "Use and Abuse of Regression", 1966)
"Stepwise regression is probably the most abused computerized statistical technique ever devised. If you think you need stepwise regression to solve a particular problem you have, it is almost certain that you do not. Professional statisticians rarely use automated stepwise regression." (Leland Wilkinson, "SYSTAT", 1984)
"Longitudinal data sets in which the outcome variable cannot be transformed to be Gaussian are more difficult to analyze for two reasons. First, simple models for the conditional expectation of the outcome do not imply equally simple models for the marginal expectation, as is the case for Gaussian data. Hence, the analyst must choose to model either the marginal or conditional expectation. Second, likelihood analyses often lead to estimators of the regression coefficients which are consistent only when the time dependence is correctly specified." (Scott L Zeger & Kung-Yee Liang, "Longitudinal Data Analysis for Discrete and Continuous Outcomes", Biometrics Vol. 42(1), 1986)
"Someone has characterized the user of stepwise regression as a person who checks his or her brain at the entrance of the computer center." (Dick R Wittink, "The application of regression analysis", 1988)
"Exploratory regression methods attempt to reveal unexpected patterns, so they are ideal for a first look at the data. Unlike other regression techniques, they do not require that we specify a particular model beforehand. Thus exploratory techniques warn against mistakenly fitting a linear model when the relation is curved, a waxing curve when the relation is S-shaped, and so forth." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)
"We have two objectives for statistical models of longitudinal data: (1) to adopt the conventional regression tools, which relate the response variables to the explanatory variables; and (2) to account for the within subject correlation." (Scott L Zeger & Kung‐Yee Liang, "An overview of methods for the analysis of longitudinal data", Statistics in medicine vol. 11, 1992)
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