pre-assumptions_of_regression_analysis
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| pre-assumptions_of_regression_analysis [2016/04/27 08:09] – hkimscil | pre-assumptions_of_regression_analysis [2016/05/09 09:23] – hkimscil | ||
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| ====== pre-asumptions in regression test ====== | ====== pre-asumptions in regression test ====== | ||
| * [[Linearity]] - the relationships between the predictors and the outcome variable should be linear | * [[Linearity]] - the relationships between the predictors and the outcome variable should be linear | ||
| - | * [Normality]] - the errors should be normally distributed - technically normality is necessary only for the t-tests to be valid, estimation of the coefficients only requires that the errors be identically and independently distributed | + | * [[:Normality]] - the errors should be normally distributed - technically normality is necessary only for the t-tests to be valid, estimation of the coefficients only requires that the errors be identically and independently distributed |
| * [[: | * [[: | ||
| * Independence - the errors associated with one observation are not correlated with the errors of any other observation | * Independence - the errors associated with one observation are not correlated with the errors of any other observation | ||
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| * [[Influence]] - individual observations that exert undue influence on the coefficients | * [[Influence]] - individual observations that exert undue influence on the coefficients | ||
| - | * [[Collinearity]] or [Singularity] - predictors that are highly collinear, i.e. linearly related, can cause problems in estimating the regression coefficients. | + | * [[Collinearity]] or [[Singularity]] - predictors that are highly collinear, i.e. linearly related, can cause problems in estimating the regression coefficients. |
| ===== Outliers ===== | ===== Outliers ===== | ||
pre-assumptions_of_regression_analysis.txt · Last modified: 2016/05/11 08:37 by hkimscil