polynomial regression in a sentence

• Polynomial regression models are usually fit using the method of least squares.
• Alternatives to difference scores : Polynomial regression analysis and response surface methodology.
• A pioneering optimal design for polynomial regression was suggested by Gergonne in 1815.
• Some of these methods make use of a localized form of classical polynomial regression.
• The polynomial regression equation commonly used in person-environment fit research is as follows:
• On the use of polynomial regression equations as an alternative to difference scores in organizational research.
• Therefore, non-parametric regression approaches such as smoothing can be useful alternatives to polynomial regression.
• *Microsoft Excel makes use of polynomial regression when fitting a trendline to data points on an X Y scatter plot.
• It is often difficult to interpret the individual coefficients in a polynomial regression fit, since the underlying monomials can be highly correlated.
• Although polynomial regression is technically a special case of multiple linear regression, the interpretation of a fitted polynomial regression model requires a somewhat different perspective.
• It's difficult to see polynomial regression in a sentence .
• Although polynomial regression is technically a special case of multiple linear regression, the interpretation of a fitted polynomial regression model requires a somewhat different perspective.
• Many of the problems with difference scores and profile similarity indices can be avoided by using polynomial regression ( Edwards, 1994, 2002; Edwards & Parry, 1993 ).
• The ( implicit ) feature space of a polynomial kernel is equivalent to that of polynomial regression, but without the combinatorial blowup in the number of parameters to be learned.
• An advantage of traditional polynomial regression is that the inferential framework of multiple regression can be used ( this also holds when using other families of basis functions such as splines ).
• A polynomial regression analysis would work fairly well except that to find out the corresponding sinusoidal function, I'd have to do some super-complicated reverse Taylor series . ..
• In her dissertation, which was published in 1918 ( see below ), she invented optimal design where she computed G-optimal designs for polynomial regression of order up to 6.
• The goal of polynomial regression is to model a non-linear relationship between the independent and dependent variables ( technically, between the independent variable and the conditional mean of the dependent variable ).
• In addition to avoiding problems with difference scores, polynomial regression allows for the development and testing of hypotheses that go beyond the simple functions captured by difference scores ( Edwards & Shipp, 2007 ).
• Polynomial regression involves using measures of the person and environment along with relevant higher-order terms ( e . g ., the squares and product of the person and environment measures ) as joint predictors.
• Studies using polynomial regression have found that the restrictive assumptions underlying difference scores are usually rejected, such that the relationship of the person and environment to outcomes is more complex than the simplified functions represented by difference scores.