Changes in the direction of association between the dependent variable and explanatory variable from simple to multiple regression is a common symptom of collinearity. It is apparent that the baseline risk factors measured in are highly correlated, as they are different manifestations of the same underlying periodontal disease in each patient.

Therefore, interpretation of the unexpected negative associations from the multiple regression model needs to be made with extreme caution. This also indicates that, although associations between the outcome and the explanatory variables are reversed due to collinearity, P -values may still be small and hence highly significant. In a study using guided tissue regeneration GTR to treat molar furcation defects, 19 multiple linear regression was performed to investigate the association between treatment outcome, horizontal bone fill, and six baseline measurements: pocket probing depth PPD , clinical attachment level CAL , gingival margin position GMP , distance between cemento-enamel junction to alveolar crest CEJ-AC , vertical intrabony component VIC , and horizontal defect depth HDD.

Results from the regression analysis revealed that treatment outcomes were significantly associated with baseline HDD in both treatment groups. As there is mathematical coupling 3 , 4 , 5 between baseline HDD and the outcome, horizontal bone fill ie change in HDD , further statistical analyses are warranted to support this purported association. However, notwithstanding mathematical coupling, Table 2 in the original article shows that in the models for each treatment group there was one covariate whose regression coefficient was absent.

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The original table used NA , which was probably the abbreviation for 'not available' or 'not applicable' though no explanation was given in the original article regarding why these regression coefficients were not available. This illustrates how perfect multicollinearity is frequently overlooked because most statistical software will if required to proceed automatically remove one of the perfectly collinear covariates in order to achieve meaningful model estimates of all remaining covariate coefficients. Some researchers perhaps fail to pay sufficient heed to the warnings that often accompany the regression output of many software packages when perfect multicollinearity is present.

It is curious to note that, if executed with slightly different data within the same statistical software package, the final model might in fact exclude a different covariate: for the treatment group of GTR, CAL was removed, but for the treatment group of GTR combined with bone grafting, PPD was removed.

The problems of collinearity and multicollinearity in the three examples might be diagnosed using either the VIF or the condition index. The unexpected direction of associations between the outcome and explanatory variables is an important sign of collinearity and multicollinearity.

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On the contrary, researchers should carefully examine the relations between all the explanatory variables in the regression models. If some of the collinear variables are redundant, in terms of providing no extra useful information, or are simply duplicate measurements of the same variable, a solution is to remove these variables from the model. For instance, in periodontics, the assessment of extent of periodontal breakdown can be made clinically or radiographically, and these two measurements seem to be highly correlated. To include both variables in the same model probably does more harm than good from a statistical viewpoint.

Multicollinearity can be a problem for a covariate when included in a model along with its quadratic form in a non-linear regression or when also included through a product-interaction term with another variable. This additional covariate is created by multiplying the smoking variable the number of cigarettes smoked and the alcohol variable the amount of alcohol consumed. As smoking-alcohol is derived mathematically from both smoking and alcohol , there will be substantial correlations amongst the three variables.

However, the correlation between smoking-alcohol and either smoking or alcohol could be considerably reduced if the interaction term smoking-alcohol was generated after the values of smoking and alcohol were centred, 9 ie transformed by subtracting the mean values of each from the original variables.

For example, suppose there are five patients in a study, and the number of cigarettes smoked per day by each patient is 5, 10, 15, 20, and 25, respectively.

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## Difference Between Classification and Regression in Machine Learning

After centring, the values for the variable smoking become , -5, 0, 5, and 10, since the mean number of cigarettes smoked is Apart from problems caused by quadratic terms and product interaction terms, the centring of explanatory variables, in general, does not solve the problem of collinearity or multicollinearity because, mathematically, the correlation coefficient can be interpreted as a product term of two centred variables divided by their variances.

Principal component analysis PCA has been proposed as a solution to the numerical problems caused by collinearity and multicollinearity. Each principal component is a linear combination of all explanatory variables, and the number of principal components is equivalent to the number of explanatory variables. Researchers then usually select the first few principal components that explain most of the variance of the covariates, and use multiple regression analysis to regress the outcome on the selected principal components.

The regression coefficients of each original explanatory variable are then derived from the regression coefficients of the selected principal components. The advantage of PCA is that, by selecting only a few principal components ie not all , the problem of wrong signs amongst regression coefficients ie the sign of regression coefficient being contradictory to expectation is usually corrected.

However, one important drawback of PCA is that the principal components selected might well explain the variances of the covariates but poorly explain the variance of the outcome. As these two methods involve advanced statistical theory and complex mathematical computations, detailed descriptions of these methods are beyond the scope of this article, and we strongly recommend that dental researchers consult professional statisticians before embarking upon such complex analyses.

Multivariable regression analyses are useful tools for oral health research, but only if users properly understand their underlying assumptions and limitations. Although multivariable analysis has been used widely, more effort is needed to improve basic understanding of these complex statistical methods amongst oral health researchers. Regression diagnostics for collinearity should be adopted and reported by studies in which complex regression models are used. We strongly suggest that dental researchers consult professional biostatisticians with experience of statistical modelling of clinical data often collinear , and avoid embarking upon complex statistical analyses themselves.

Altman DG. Statistics in medical journals: developments in the s. Statistics in Medicine ; 10 : — Statistics in medical journals. Statistics in Medicine ; 1 : 59— Is reduction of pocket probing depth correlated with the baseline value or is it 'mathematical coupling'? J Dent Res ; 81 : — Mathematical coupling still undermines the statistical assessment of clinical research: illustration from the treatment of guided tissue regeneration.

J Dent ; 32 : — Ratio variables in regression analysis can give rise to spurious results: a lesson from guided tissue regeneration. Miles J, Shelvin M. Applying regression and correlation. London: Sage Publication, Applied regression and analysis of variance.

New York: McGraw-Hill, Pedhazur EJ. Multiple regression in behavioral research: Explanation and prediction.

## Assumptions of Linear Regression

Fort Worth: Harcourt, Multiple regression for physiological data analysis: the problem of multicollinearity. Amer J Phys ; : R1—R Regression analysis by example. Maddala GS. Introduction to econometrics. Essential medical statistics. If the coefficient for a particular variable is significantly greater than zero, researchers judge that the variable contributes to the predictive ability of the regression equation. In this way, it is possible to distinguish variables that are more useful for prediction from those that are less useful.

This kind of analysis makes sense when multicollinearity is small. But it is problematic when multicollinearity is great. Here's why:. With this in mind, the analysis of regression coefficients should be contingent on the extent of multicollinearity. This means that the analysis of regression coefficients should be preceded by an analysis of multicollinearity.

If the set of independent variables is characterized by a little bit of multicollinearity, the analysis of regression coefficients should be straightforward. If there is a lot of multicollinearity, the analysis will be hard to interpret and can be skipped.

### Tip 3: Correlation Does Not Imply Causation . . . Even in Regression

Note: Multicollinearity makes it hard to assess the relative importance of independent variables, but it does not affect the usefulness of the regression equation for prediction. Even when multicollinearity is great, the least-squares regression equation can be highly predictive. So, if you are only interested in prediction, multicollinearity is not a problem. There are two popular ways to measure multicollinearity: 1 compute a coefficient of multiple determination for each independent variable, or 2 compute a variance inflation factor for each independent variable.

In the previous lesson , we described how the coefficient of multiple determination R 2 measures the proportion of variance in the dependent variable that is explained by all of the independent variables. If we ignore the dependent variable, we can compute a coefficient of multiple determination R 2 k for each of the k independent variables.

We do this by regressing the k th independent variable on all of the other independent variables. That is, we treat X k as the dependent variable and use the other independent variables to predict X k.

### 1 Introduction

How do we interpret R 2 k? If R 2 k equals zero, variable k is not correlated with any other independent variable; and multicollinearity is not a problem for variable k. As a rule of thumb, most analysts feel that multicollinearity is a potential problem when R 2 k is greater than 0. The variance inflation factor is another way to express exactly the same information found in the coefficient of multiple correlation. A variance inflation factor is computed for each independent variable, using the following formula:. In many statistical packages e. In MiniTab, for example, the variance inflation factor can be displayed as part of the regression coefficient table.

The interpretation of the variance inflation factor mirrors the interpretation of the coefficient of multiple determination. One piece of information available was the number of thousands of square feet MSF in the job. Data for 15 randomly selected jobs processed on a particular printing press follow: MSF Hours Suggested Citation: Suggested Citation.

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