Characteristics of study population. by Gábor Szaló (22615130)

“…Among those 803 individuals who did not take antihypertensive medication, there was a significant association in linear regression between increase in PSS-10 and decrease in C2 (B: −0.2, 95% CI: −0.4- −0.02; p = 0.03) that was lost after adjustment for physical activity (B: −0.16, 95% CI: −0.35–0.03; p = 0.1). …”

Cross-sectional dependence results. by Ozlem Kutlu Furtuna (20308206)

“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”

Autocorrelation test results. by Ozlem Kutlu Furtuna (20308206)

“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”

Pesaran’s CADF test results for Model I. by Ozlem Kutlu Furtuna (20308206)

“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”

Descriptive statistics of related variables. by Ozlem Kutlu Furtuna (20308206)

“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”

Baseline characteristics of participants. by Mei Zhou (269746)

“…</p><p>Results</p><p>After DRG implementation, the logarithmic mean of total hospitalization expenditures decreased significantly (3.914 ± 0.837 vs. 3.872 ± 1.004), while rates of unplanned readmissions, unplanned reoperations, postoperative complications, and patient complaints within 30 days increased significantly (3.784% vs 4.214%, 0.083% vs 0.166%, 0.207% vs 0.258%, 3.741% vs 5.133%). …”

The framework diagram of this study. by Mei Zhou (269746)

“…</p><p>Results</p><p>After DRG implementation, the logarithmic mean of total hospitalization expenditures decreased significantly (3.914 ± 0.837 vs. 3.872 ± 1.004), while rates of unplanned readmissions, unplanned reoperations, postoperative complications, and patient complaints within 30 days increased significantly (3.784% vs 4.214%, 0.083% vs 0.166%, 0.207% vs 0.258%, 3.741% vs 5.133%). …”

The performance of eigenvectors of the covariance matrix in a model with more complex population structure. by Joshua G. Schraiber (8027060)

Predictors in ordinal regression model for GDS. by Shane Naidoo (20148021)

“…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”

Classification of hand grip strength. by Shane Naidoo (20148021)

“…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”

Rating scale for functional severity [28]. by Shane Naidoo (20148021)

“…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”

Regression model coefficients. by Shane Naidoo (20148021)

“…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”

ICOPE screening positive participant’s responses. by Shane Naidoo (20148021)

“…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”

WHO BMI classification for adults. by Shane Naidoo (20148021)

“…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”

Correlation in site-level DNA methylation estimates between TMS and RRBS data after permutation. by Amy Longtin (21409957)

Performance comparison of ML models. by Gourab Saha (8987405)

“…<div><p>As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. …”

Comparative data of different soil samples. by Gourab Saha (8987405)

“…<div><p>As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. …”

Confusion matrix of random forest model. by Gourab Saha (8987405)

“…<div><p>As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. …”

Sensor value scenario for fuzzy logic algorithm. by Gourab Saha (8987405)

“…<div><p>As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. …”

Evaluation metrics of selected ML models. by Gourab Saha (8987405)

“…<div><p>As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. …”