User Tools

Site Tools


pre-assumptions_of_regression_analysis

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision
Previous revision
pre-assumptions_of_regression_analysis [2016/04/27 08:02] hkimscilpre-assumptions_of_regression_analysis [2016/05/11 08:37] (current) – [Outliers] hkimscil
Line 2: Line 2:
 ====== 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
   * [[:Homoscedasticity|Homogeneity]] of variance (or [[Homoscedasticity]]) - the error variance should be constant   * [[:Homoscedasticity|Homogeneity]] of variance (or [[Homoscedasticity]]) - the error variance should be constant
   * 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
Line 8: Line 8:
  
   * [[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 =====
Line 15: Line 15:
 |  **Model Summary(b) **   ||||||   |  **Model Summary(b) **   ||||||  
 |  Model    R    R Square    Adjusted R Square    Std. Error of the Estimate    Durbin-Watson    |  Model    R    R Square    Adjusted R Square    Std. Error of the Estimate    Durbin-Watson   
-|  1    0.375935755   <bgcolor="yellow"> 0.141327692    0.093623675    277.9593965    1.770202598   +|  1    0.375935755   |@yellow0.141327692    0.093623675    277.9593965    1.770202598   
 | a Predictors: (Constant), income   ||||||  | a Predictors: (Constant), income   |||||| 
 | b Dependent Variable: sales   |||||| | b Dependent Variable: sales   ||||||
Line 40: Line 40:
 But, the result might be due to some outliers. So, check outliers by examining: But, the result might be due to some outliers. So, check outliers by examining:
   * scatter plot: (z-predicted(x), z-residual(y)). The shape should be rectangular.   * scatter plot: (z-predicted(x), z-residual(y)). The shape should be rectangular.
-  * Mahalanovis score+  * [[Mahalanobis distance]] score
   * Cook distance   * Cook distance
   * Leverage   * Leverage
pre-assumptions_of_regression_analysis.txt · Last modified: 2016/05/11 08:37 by hkimscil

Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki