Multiple linear regression
This learning resource summarises the main teaching points about multiple linear regression (MLR), including key concepts, principles, assumptions, and how to conduct and interpret MLR analyses. Prerequisites: 
What is MLR?[edit]
 Multiple linear regression (MLR) is a multivariate statistical technique for examining the linear correlations between two or more independent variables (IVs) and a single dependent variable (DV).
 Research questions suitable for MLR can be of the form "To what extent do X1, X2, and X3 (IVs) predict Y (DV)?"
e.g., "To what extent does people's age and gender (IVs) predict their levels of blood cholesterol (DV)?"  MLR analyses can be visualised as path diagrams and/or venn diagrams
Assumptions[edit]
View the accompanying screencast: [1] 
 Level of measurement
 IVs: MLR involves two or more continuous (interval or ratio) or dichotomous variables (may require recoding into dummy variables)
 DV: One continuous (interval or ratio) variable
 Recode predictors if necessary (e.g., as dichotomous or dummy variables)
 Sample size (some rules of thumb):
 Total N based on ratio of cases to IVs:
 Min. 5 cases per predictor (5:1) (basically, you need enough data to provide reliable correlation estimates)
 Ideally 20 cases per predictor (20:1), with an overall N of at least 100; this allows sufficient power to detect a medium ES of R^{2} of .13 (Francis, p. 128))
 Total N based on a constant plus ratio of cases to IVs:
 Tabachnick and Fidell (2007) suggest that N should ideally be 50 + 8(k) for testing a full regression model or 104 + k when testing individual predictors (where k is the number of IVs).
 Total N based on ratio of cases to IVs:
 Normality
 Check the univariate descriptive statistics (M, SD, skewness and kurtosis)
 Check the histograms with a normal curve imposed
 Estimates of correlations will be more reliable and stable when the variables are normally distributed
 Linearity
 Are the bivariate relationships linear?
 Check scatterplots and correlations between the DV (Y) and each of the IVs (Xs)
 Check for influence of bivariate outliers
 Homoscedasticity
 Are the bivariate distributions reasonably evenly spread about the line of best fit?
 Check scatterplots between Y and each of Xs and/or check scatterplot of the residuals (ZRESID) and predicted values (ZPRED))
 Multicollinearity
 Is there multicollinearity between the IVs? Predictors should not be overly correlated with one another. Ways to check:
 Examine bivariate correlations and scatterplots between each of the IVs (i.e., are the predictors overly correlated e.g., above .7?).
 Check the collinearity statistics in the coefficients table:
 The Variance Inflation Factor (VIF) should be low (< ~310) and/or
 Tolerance should be high (> .1 to .3) (Note that TOL=1/VIF so only one needs to be used).
 Is there multicollinearity between the IVs? Predictors should not be overly correlated with one another. Ways to check:
 Multivariate outliers (MVOs)
 Check whether there are influential MVOs using Mahalanobis' Distance (MD) and/or Cook’s D (CD).
 SPSS: Linear Regression  Save  Mahalanobis (can also include Cook's D)
 After execution, new variables called mah_1 (and coo_1) will be added to the data file.
 In the output, check the Residuals Statistics table for the maximum MD and CD.
 The maximum MD should not exceed the critical chisquare value with degrees of freedom (df) equal to number of predictors, with critical alpha =.001. CD should not be greater than 1.
 If outliers are detected, go to the data file, sort the data in descending order by mah_1, and check the cases with mah_1 distances above the critical value (these cases have an unusual combination of responses for the variables in the analysis). Consider removing these cases and rerunning the MLR. If the results are very similar (e.g., similar R^{2} and conclusions for each of the predictors, then it is best to use the original results, i.e., including the multivariate outliers. If the results are different when the MVOs are not included, then these cases probably have had undue influence and it is best to report the results without these cases.
 See also Francis 5.1.4.2 Screening for influential case
 Normality of residuals
 Residuals are more likely to be normally distributed if each of the variables normally distributed
 Check histograms of all variables in an analysis
 Normally distributed variables will enhance the MLR solution
 See also
 Allen & Bennett 13.3.2.1 Assumptions (pp. 178179)
 Francis 5.1.4 Practical Issues and Assumptions (pp. 126128)
Types[edit]
There are several types of MLR, including:
Type  Characteristics 

Direct (or Standard) 

Hierarchical 

Forward 

Backward 

Stepwise 

Results[edit]
 MLR analyses produce several diagnostic and outcome statistics which are summarised below and are important to understand.
 Make sure that you can learn how to find and interpret these statistics from statistical software output.
Correlations[edit]
Examine the linear correlations between (usually as a correlation matrix, but also view the scatterplots):
 IVs
 each IV and the DV
Effect sizes[edit]
R[edit]
 (Big) R is the multiple correlation coefficient for the relationship between the predictor and outcome variables.
 Interpretation is similar to that for little r (the linear correlation between two variables), however R can only range from 0 to 1, with 0 indicating no relationship and 1 a perfect relationship. Large values of R indicate more variance explained in the DV.
 R can be squared and interpreted as for r^{2}, with a rough rule of thumb being .1 (small), .3 (medium), and .5 (large). These R^{2} values would indicate 10%, 30%, and 50% of the variance in the DV explained respectively.
 When generalising findings to the population, the R^{2} for a sample tends to overestimate the R^{2} of the population. Thus, adjusted R^{2} is recommended when generalising from a sample, and this value will be adjusted downward based on the sample size; the smaller the sample size, the greater the reduction.
 The statistical significance of R can be examined using an F test and its corresponding p level.
 Reporting example: R^{2 = .32, F(6, 217) = 19.50, p = .001}
 ^{"6, 217" refers to the degrees of freedom  for more information, see about halfdown this page}
^{Cohen's ƒ2[edit]}
 Cohen's ƒ^{2} is based on the R^{2} and is an alternate indicator of effect size for MLR.
Coefficients[edit]
An MLR analysis produces several useful statistics about each of the predictors. These regression coefficients are usually presented in a Results table which may include:
 B (unstandardised)  used for building a prediction equation
 β (standardised)  indicates the relative strength of the predictors on a scale ranging from 1 to 1.
 Zeroorder correlation (r)  the correlation between a predictor and the outcome variable
 Partial correlations (pr)  indicate the unique correlations between each IV and the DV (labelled "partial" in SPSS output)
 Semipart correlations (sr)  similar to partial correlations (labelled "part" in SPSS output); squaring this value provides the percentage of variance in the DV uniquely explained by each IV (sr^{2})
 t, p  indicate the statistical significance of each IV
 Confidence intervals  indicate the probably range of population values for the βs
Equation[edit]
 A prediction equation can be derived from the regression coefficients in a MLR analysis.
 The equation is of the form
(for predicted values) or
(for observed values)
Residuals[edit]
A residual is the difference between the actual value of a DV and its predicted value. Each case will have a residual for each MLR analysis. Three key assumptions can be tested using plots of residuals:
 Linearity: IVs are linearly related to DV
 Normality of residuals
 Equal variances (Homoscedasticity)
Power[edit]
Advanced concepts[edit]
 Partial correlations
 Use of hierarchical regression to partial out or remove the effect of 'control' variables
 Interactions between IVs
 Moderation and mediation
Writing up[edit]
When writing up the results of an MLR, consider describing:
 Assumptions: How were they tested? To what extent were the assumptions met?
 Correlations: What are they? Consider correlations between the IVs and the DV separately to the correlations between the IVs.
 Regression coefficients: Report a table and interpret
 Causality: Be aware of the limitations of the analysis  it may be consistent with a causal relationship, but it is unlikely to prove causality
FAQ[edit]
What if there are univariate outliers?[edit]
Basically, explore and consider what the implications might be  do these "outliers" impact on the assumptions? A lot depends on how "outliers" are defined. It is probably better to consider distributions in terms of the shape of the histogram and skewness and kurtosi, and whether these values are unduely impacting on the estimates of linear relations between variables. In other words, what are the implications? Ultimately, the researcher needs to decide whether the outliers are so severe that they are unduely influencing results of analyses or whether they are relatively benign. If unsure, explore, test, try the analyses with and without these values etc. If still unsure, be conservative and remove the data points or recode the data.
See also[edit]
Search for Multiple linear regression on Wikipedia. 
 Tutorials/Activities
 Lectures
 Other
External links[edit]
 Correlation and simple least squares regression (Zady, 2000)
 Multiple regression (Statsoft)
 Multiple regression assumptions (ERIC Digest)