regression
... logistic regression is a model used for prediction of the probability of occurrence of an event by fitting data to a logistic curve. It makes use of several predictor variables that may be either numerical or categorical. For example, the probability that a person has a heart attack within a speci ...
... logistic regression is a model used for prediction of the probability of occurrence of an event by fitting data to a logistic curve. It makes use of several predictor variables that may be either numerical or categorical. For example, the probability that a person has a heart attack within a speci ...
Response Surface Regression
... exponents of the variables in a term are also in the model. For example, if the term X1X22 is in the model, so are X1, X2, X1X2, and X22.Notice that each of these terms can be created be decreasing the exponents of the two variables that form the original term (noting that X0j = 1). Note that the fi ...
... exponents of the variables in a term are also in the model. For example, if the term X1X22 is in the model, so are X1, X2, X1X2, and X22.Notice that each of these terms can be created be decreasing the exponents of the two variables that form the original term (noting that X0j = 1). Note that the fi ...
Logistic Regression
... Logistical regression is regularly used rather than discriminant analysis when there are only two categories of the dependent variable. Logistic regression is also easier to use with SPSS than discriminant analysis when there is a mixture of numerical and categorical independent variable’s, because ...
... Logistical regression is regularly used rather than discriminant analysis when there are only two categories of the dependent variable. Logistic regression is also easier to use with SPSS than discriminant analysis when there is a mixture of numerical and categorical independent variable’s, because ...
Interaction (statistics)
In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive. Most commonly, interactions are considered in the context of regression analyses.The presence of interactions can have important implications for the interpretation of statistical models. If two variables of interest interact, the relationship between each of the interacting variables and a third ""dependent variable"" depends on the value of the other interacting variable. In practice, this makes it more difficult to predict the consequences of changing the value of a variable, particularly if the variables it interacts with are hard to measure or difficult to control.The notion of ""interaction"" is closely related to that of ""moderation"" that is common in social and health science research: the interaction between an explanatory variable and an environmental variable suggests that the effect of the explanatory variable has been moderated or modified by the environmental variable.