7.2.9. BoxCox Regression
The ordinary least squares regression assumes normal distribution of residuals. When this is not the case, the BoxCox Regression procedure may be useful (see Box, G. E. P. and Cox, D. R. 1964). It will transform the dependent variable using the BoxCox Transformation function and employ maximum likelihood estimation to determine the optimal level of the power parameter lambda. In order to run a BoxCox Regression, the dependent variable should not contain any non positive values.
Variable selection and Ordinary Least Squares Output dialogues for this procedure are identical to that of Linear Regression. There is a separate output dialogue for maximum likelihood estimation, which contains diagnostic and graphic options for the estimation parameters and normal probability plots of data before and after the transformation. It is possible to run a BoxCox Regression without any independent variables. In this case the results will be similar to that of Data Transformation procedure with BoxCox option (available under Statistics 2 → Quality Control menu).
7.2.9.1. BoxCox Regression Model Description
BoxCox Regression will transform the dependent variable as follows:
and determine the optimal value of lambda by maximising the following loglikelihood function:
where is the estimate of the least squares variance using the transformed y variable.
A golden section minimisation algorithm is employed to minimise the negative of the log likelihood function within the range of 3 ≤ λ ≤ 3. These limits can be changed by the user if necessary and the changes will be stored by the program.
7.2.9.2. BoxCox Regression Variable Selection
As in Linear Regression, BoxCox Regression can be used to estimate models with or without a constant term, with or without weights and regressions can be run on a subset of cases as determined by the levels of an unlimited number of factor columns. An unlimited number of dependent variables can be selected in order to run the same model on different dependent variables. It is also possible to include interaction terms, dummy and lag/lead variables in the model, without having to create them as spreadsheet columns first (see 2.1.4. Creating Interaction, Dummy and Lag/Lead Variables).
It is compulsory to select at least one numeric data column as a dependent variable. When more than one dependent variable is selected, the analysis will be repeated as many times as the number of dependent variables, each time only changing the dependent variable and keeping the rest of selections unchanged.
You can transform a single variable without using any predictor (independent) variables. In this case the results will be similar to that of the Data Transformation procedure with BoxCox option (available under Statistics 2 → Quality Control menu). A column containing numeric data can be selected as a weights column.
An intermediate inputs dialogue is displayed next.
7.2.9.3. BoxCox Regression Intermediary Inputs
Tolerance: This value is used to control the sensitivity of minimisation procedure employed. Under normal circumstances, you do not need to edit this value. If a convergence cannot be achieved, then larger values of this parameter can be tried by removing one or more zeros.
Maximum Number of Iterations: When convergence cannot be achieved with the default value of 100 function evaluations, a higher value can be tried.
Minimum Lambda: Limits for the range where the optimum lambda will be searched can be set. Change this value if the optimal lambda cannot be found within the specified range. If the lambda displayed is the same or very near to this minimum, change it to a smaller value. When the limit is changed, a recalculation is forced and lambda is estimated again.
Maximum Lambda: Change this value if the optimal lambda cannot be found within the specified range. If the lambda displayed is the same or very near to this maximum, change it to a higher value. When the limit is changed, a recalculation is forced and lambda is estimated again.
Lambda: You can override the estimated lambda and enter your own value here. You may wish to do this to use a round power value (like 1, 0.5, 0.5, 2). If the estimated lambda is changed, confidence intervals and chisquared tests for lambda will not be available.
7.2.9.4. BoxCox Regression Maximum Likelihood Output Options
BoxCox Transformation:
Results: The first output option displays results for the maximum likelihood estimation (see 9.3.7.2. BoxCox Transformation).
Lambda with Confidence Limits: The confidence interval for optimum lambda is based on the likelihood ratio statistic and it is defined as:
Values corresponding to lower and upper bound of lambda are computed separately using an iterational procedure.
Transformation Formula: The equation applied in transforming the dependent variable is displayed. The same equation is also printed on a separate line with estimated parameter values, in a format suitable for cell calculations in Excel. You can simply copy this equation, replace the variable x with a cell reference and run interpolations.
Likelihood Ratio Test: In BoxCox Regression, this test performed by evaluating the regression equation for lambda fixed at λ_{1} = 1, 0 and 1.
which is chisquare distributed with one degree of freedom.
Normality Tests: AndersonDarling Test of normality is performed on the original and transformed dependent variable thus allowing you to judge whether the transformation was useful. No or a small increase in the tail probability indicates that BoxCox Transformation was not useful.
Transformed Data: The original and transformed dependent variable values and their group membership (if any) are sorted and displayed in a table. If you wish to display the unsorted values, you can use the Case (Diagnostic) Statistics output in Ordinary Least Squares Output option.
If you are using UNISTAT in StandAlone Mode, click on the UNISTAT icon on the Output Medium Toolbar to send all output to UNISTAT spreadsheet. In Excel AddIn Mode select the output matrix as data for further calculations.
Normal Probability Plot: Original Data: A Normal Probability Plot of the original data is displayed together with AndersonDarling Test results in the legend. You can compare this graph with the next one to visualise the improvement provided by the transformation.
Normal Probability Plot: Transformed Data: A Normal Probability Plot of the transformed data is displayed together with AndersonDarling Test results in the legend. You can compare this graph with the previous one to visualise the improvement provided by the transformation.
BoxCox Maximum Likelihood Plot: Log likelihood values are plotted against the specified range of lambda. Lambda and its confidence limits are indicated by vertical lines. A horizontal line is drawn for the log likelihood value corresponding to confidence limits.
BoxCox Root Mean Square Error Plot: Root mean square error (RMSE) of regression is plotted against lambda.
BoxCox Correlation Plot: Values of the regression correlation coefficient are plotted against lambda.
7.2.9.5. BoxCox Regression Ordinary Least Squares Output Options
All output options are as in Linear Regression. The transformed dependent variable is used. The unsorted values for the transformed dependent variable can be accessed from the Case (Diagnostic) Statistics output option.
7.2.9.6. BoxCox Regression Example
Open REGRESS and select Statistics 1 → Regression Analysis → BoxCox Regression. From the Variable Selection Dialogue select temperature, mm, min and ml (C1, C3C5) as [Variable]s and cm (C2) as [Dependent]. On Step 2 leave convergence parameters unchanged.
The Maximum Likelihood Output option generates the following output.
BoxCox Regression
BoxCox Transformation: Results
Variables Selected: cm

Value 
Lower 95% 
Upper 95% 
Lambda 
0.4162 
2.7747 
1.7807 
BoxCox Transformation:
y = (y ^ Lambda – 1) / Lambda
y = (POWER(y, 0.416232112612545) – 1) / 0.416232112612545
Lambda 
ChiSquare 
DoF 
Probability 
1 
0.2511 
1 
0.6163 
0 
0.1318 
1 
0.7165 
1 
1.5689 
1 
0.2104 
Log of Likelihood = 
8.0864 
Normality Tests
Smaller probabilities indicate nonnormality.

AD Stat 
Probability 
Original Data 
0.5988 
0.1202 
Transformed Data 
0.5682 
0.1434 
Transformed Data

Original Data 
Transformed Data 
1 
6.9000 
1.3273 
2 
7.0000 
1.3337 
3 
7.0000 
1.3337 
… 
… 
… 
31 
11.5000 
1.5332 
32 
11.7000 
1.5394 
33 
12.1000 
1.5514 
Select the Ordinary Least Squares Output option and check only the Regression Results option to obtain the following ordinary least squares regression output.
BoxCox Regression
Dependent Variable: cm
Valid Number of Cases: 33, 0 Omitted
Regression Results

Coefficient 
Standard Error 
tStatistic 
Significance 
Lower 95% 
Upper 95% 
Constant 
1.6405 
0.1886 
8.6991 
0.0000 
1.2542 
2.0268 
temperature 
0.0054 
0.0048 
1.1318 
0.2673 
0.0044 
0.0153 
mm 
0.0436 
0.0302 
1.4439 
0.1599 
0.1055 
0.0183 
min 
0.0125 
0.0114 
1.0896 
0.2852 
0.0110 
0.0359 
ml 
0.0052 
0.0285 
0.1813 
0.8574 
0.0636 
0.0532 
Residual Sum of Squares = 
0.1150 
Standard Error = 
0.0641 
Mean of Y = 
1.4271 
Standard Deviation of Y = 
0.0664 
Correlation Coefficient = 
0.4305 
Rsquared = 
0.1854 
Adjusted Rsquared = 
0.0690 
F(4,28) = 
1.5927 
Significance of F = 
0.2038 
DurbinWatson Statistic = 
1.4016 
Press Statistic = 
0.1800 