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. 2023 Dec 16;15(12):e50642. doi: 10.7759/cureus.50642

Table 3. Multiple linear regression analysis of the presence of bad weather-related symptoms for the level of each additional condition.

For each of the four regression models, one dependent variable (workplace stress, gastrointestinal problems, irritability/agitation, or sleep disorders) and three explanatory variables (age, sex, and the presence of bad weather-related symptoms) were used. Data of the bad weather-related symptoms was used as a binary data based on the presence of at least one self-reported condition.

* Dummy variable (Male=1, Female=0; bad weather-related physical conditions “yes” = 1, “no” = 0)

VIF, variance inflation factor; NRS, Numerical Rating Scale

Characteristics Unstandardized coefficient Standardized coefficient (β) t P-value VIF
B Std. error
Dependent variable: Workplace stress (NRS 0–10)
Age -0.0052 0.0176 -0.0264 -0.30 0.7676 1.123
Male * 0.1020 0.2477 0.0352 0.41 0.6812 1.031
Weather-related symptoms * 0.8821 0.2736 0.2834 3.22 0.0016 1.092
Dependent variable: Gastrointestinal problems (NRS 0–10)
Age –0.0000 0.0171 –0.0001 –0.00 0.9991 1.102
Male * –0.0663 0.2383 –0.0243 –0.28 0.7814 1.021
Weather-related symptoms * 0.7357 0.2607 0.2538 2.82 0.0056 1.081
Dependent variable: Irritability/agitation (NRS 0–10)
Age –0.0040 0.0142 –0.0247 –0.28 0.7786 1.102
Male * 0.0111 0.1971 0.0047 0.06 0.9553 1.021
Weather-related symptoms * 0.8853 0.2156 0.3557 4.11 <0.0001 1.081
Dependent variable: Sleep disorders (NRS 0–10)
Age –0.0013 0.0185 –0.0063 –0.07 0.9457 1.102
Male * 0.0126 0.2569 0.0044 0.05 0.9609 1.021
Weather-related symptoms * 0.6188 0.2810 0.2005 2.20 0.0295 1.081