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This study investigated the impact of indoor illuminance and correlated color temperature (CCT) on healthy adults' cognitive performance, subjective mood, and alertness during daytime office hours and differences in time-of-day effects. A 2(illuminance) × 2(CCT) × 2(morning vs. afternoon) mixed design ( N = 60) was employed. Participants felt less sleepy in the bright light exposure. The low "cool" lighting induced the least positive mood. The effects of illuminance and CCT on subjective feelings were not time-of-day dependent. The results demonstrated the slowest responses in inhibition, working memory, and recognition of facial expression tasks in the low "warm" lighting. The effect on long-term memory was most pronounced under the high "cool" light exposure, but only in the afternoon for recognition of neutral words. The findings suggest that future research on good indoor lighting should consider illuminance levels and CCT as well as other variables to optimize lighting effects during regular daytime hours.

AbstractWe developed a novel method to address multicollinearity in linear models called average ordinary least squares (OLS)‐centered penalized regression (AOPR). AOPR penalizes the cost function to shrink the estimators toward the weighted‐average OLS estimator. The commonly used ridge regression (RR) shrinks the estimators toward zero, that is, employs penalization prior in the Bayesian view, which contradicts the common real prior . Therefore, RR selects small penalization coefficients to relieve such a contradiction and thus makes the penalizations inadequate. Mathematical derivations remind us that AOPR could increase the performance of RR and OLS regression. A simulation study shows that AOPR obtains more accurate estimators than OLS regression in most situations and more accurate estimators than RR when the signs of the true s are identical and is slightly less accurate than RR when the signs of the true s are different. Additionally, a case study shows that AOPR obtains more stable estimators and stronger statistical power and predictive ability than RR and OLS regression. Through these results, we recommend using AOPR to address multicollinearity more efficiently than RR and OLS regression, especially when the true s have identical signs.