*2.5. Statistical Analysis*

Quantitative statistical analysis of the speed data was undertaken using the Statistical Package for the Social Sciences (SPSS) v26 (IBM). For the each of the datasets, from before, after, and two years after, descriptive statistics were produced. Z-scores were calculated and used to check for outlying cases of more than three standard deviations from the mean. Levene's test was used to test for equality of variance, and differences in mean were tested for using Welch's *t*-test. The null hypothesis was examined using a two-tailed test with a level of confidence (α) of 0.05.

At an infrastructure level, descriptive statistics were produced for the 15 variables of interest. Pearson's correlation coefficient was used to assess the association between road-environment factors and speed. Checks were made on the speed-data variables prior to the calculation of the correlation coefficients to determine the suitability of each variable for testing. All variables under test were at the interval or nominal level. Values of between 0.5 and 1 were taken as a high correlation, 0.3 to 0.49 as medium, and 0.1 to 0.29 as small.

Linear regression models were created for the dependent variable of speed and the independent variables of road and environmental features. For model estimation, the Ordinary Least Squares (OLS) approach was used [28]. Two models were created, one for sections of road with a 60 mph posted speed limit, the other for the remaining sections with a 70 mph limit. The stepwise regression function in SPSS was used to create the models. Prior to the process commencing, checks for normality and tests of collinearity were used from the output of the correlation testing previously described. No variable was excluded from the initial list of independent variables. Exclusion of variables was undertaken on a stepwise basis, with *p*-values of 0.05 used for entry and 0.10 used for exclusion.
