Independent variable | Ordinary least squares estimation number of observations: 286 R-squared: 0.532 Akaike info: 2105.81 Jarque-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.