Dataset Modelability by QSAR: Continuous Response Variable

2021 ◽  
pp. 233-253
Author(s):  
Alexander Golbraikh ◽  
Rong Wang ◽  
Vinicius M. Alves ◽  
Inta Liepina ◽  
Eugene Muratov ◽  
...  
Keyword(s):  
Response Variable ◽  
Continuous Response

More generalized linear modelling.

2021 ◽  
pp. 171-186
Author(s):  
Donald Quicke ◽  
Buntika A. Butcher ◽  
Rachel Kruft Welton
Keyword(s):  
Generalized Linear Models ◽  
Count Data ◽  
Binary Data ◽  
Linear Models ◽  
Explanatory Variable ◽  
Response Variable ◽  
Explanatory Variables ◽  
Continuous Response ◽  
Generalized Linear Modelling ◽  
Linear Modelling

Abstract This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.

Analysis of covariance (ANCOVA).

2021 ◽  
pp. 166-170
Author(s):  
Donald L. J. Quicke ◽  
Buntika A. Butcher ◽  
Rachel A. Kruft Welton
Keyword(s):  
Coral Reef ◽  
Count Data ◽  
Virgin Islands ◽  
Analysis Of Covariance ◽  
Categorical Variables ◽  
Initial Size ◽  
Orchid Species ◽  
Response Variable ◽  
British Virgin Islands ◽  
Continuous Response

Abstract This chapter deals with analysis of covariance or ANCOVA, a combination of ANOVA and regression. It tests the effects of a mix of continuous and categorical variables on a continuous response variable. Two examples are presented. Example 1 is based on a study investigating the effects of two types of tagging (acrylic paint and subcutaneous microtags) on the growth of the coral reef goby, Coryphopterus glaucofraenum, in the British Virgin Islands and included initial size as a continuous explanatory variable. Example 2 analyses data from a study on the number of pollinaria removed by pollinators from inflorescences of two Sirindhornia orchid species (S. monophylla and S. mirabillis) in relation to the number of flowers in the inflorescence (also count data) and the orchid species (categorical).

Analysis of covariance (ANCOVA).

2021 ◽  
pp. 166-170
Author(s):  
Donald L. J. Quicke ◽  
Buntika A. Butcher ◽  
Rachel A. Kruft Welton
Keyword(s):  
Coral Reef ◽  
Count Data ◽  
Virgin Islands ◽  
Analysis Of Covariance ◽  
Categorical Variables ◽  
Initial Size ◽  
Orchid Species ◽  
Response Variable ◽  
British Virgin Islands ◽  
Continuous Response

Abstract This chapter deals with analysis of covariance or ANCOVA, a combination of ANOVA and regression. It tests the effects of a mix of continuous and categorical variables on a continuous response variable. Two examples are presented. Example 1 is based on a study investigating the effects of two types of tagging (acrylic paint and subcutaneous microtags) on the growth of the coral reef goby, Coryphopterus glaucofraenum, in the British Virgin Islands and included initial size as a continuous explanatory variable. Example 2 analyses data from a study on the number of pollinaria removed by pollinators from inflorescences of two Sirindhornia orchid species (S. monophylla and S. mirabillis) in relation to the number of flowers in the inflorescence (also count data) and the orchid species (categorical).

Applications of Classification and Regression Tree Methods in Roadway Safety Studies

1996 ◽  
Vol 1542 (1) ◽  
pp. 1-5 ◽  
Author(s):  
J. Richard Stewart
Keyword(s):  
Regression Model ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Response Variable ◽  
Continuous Response ◽  
Complex Interactions ◽  
Roadway Safety ◽  
Safety Studies ◽  
Tree Methods ◽  
Classification And Regression

In recent years a number of nonparametric regression-type statistical procedures have been developed. Classification and regression trees (CART) is one such method that can be used as a classifier for a discrete-valued response variable or as a regression model for a continuous response variable. Advantages of CART over many other methods are its ability to include a relatively large number of independent variables and to identify complex interactions among these variables. A brief description of the CART procedure is given, and its application as a classifier and as a regression model to highway safety analyses is illustrated.

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