Table 3

Initial exploratory regression showing only the variables which passed the cut-off p value

 Independent variable Ordinary least squares estimation number of observations: 286R-squared: 0.532Akaike info: 2105.81Jarque-Bera prob: 0.000 Summary of variable significance among all possible variable combinations Coefficient t-statistic P value % Significant % Negative % Positive WPS recipients—Veterans 20.374 2.312 0.022 73.05% 0% 100% No central heating −28.46 −2.931 0.004 70.72% 80.88% 19.12% SIMD 14.809 1.605 0.11 18.41% 6.13% 93.87% AFPS recipients—Veterans 11.502 1.117 0.265 50% 0.67% 99.33% AFCS recipients—Veterans 6.749 1.111 0.267 25% 6.25% 93.75% No qualifications −10.835 −0.998 0.319 7.70% 78.56% 21.44% Unemployment 28.187 0.844 0.399 49.41% 16.24% 83.76% Alcohol −6.504 −0.802 0.424 7.35% 81.20% 18.80%
• R-squared: indicates how much variation of a dependent variable is explained by the independent variable(s).

• Akaike info: estimates the relative amount of information lost by a given model: the less information a model loses, the higher the quality of that model.

• Jarque-Bera prob: indicates if the data have a normal distribution. If it is far from zero, it signals the data do not have a normal distribution.

• Coefficient: indicates the change in the dependent variable for one unit of change in the independent variable. A negative coefficient suggests that as the independent variable increases, the dependent variable tends to decrease.

• t-statistic: the coefficient divided by its standard error, thus estimating the likelihood that the regression coefficient is different from zero.

• p value: estimates what the odds are of the results to have happened. The lower it is, the less likely the results could have happened due to random chance.

• AFCS, Armed Forces Compensation Scheme; SIMD, Scottish Index of Multiple Deprivation; WPS, War Pension Scheme.