Predictor importance for predicting stock market using neural network and linear predictor. by Dania AL-Najjar (11840857)

Multivariable linear regression analyses for predicting factors. by Maurice Pradella (11678817)

Cohesion characteristics and statistical significance analysis of different ingredients across IDDSI levels^b'*'. by Muxi Chen (9294270)

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Adhesion characteristics and statistical significance analysis of different ingredients across IDDSI levels^b'*'. by Muxi Chen (9294270)

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Hardness characteristics and statistical significance analysis of different ingredients across IDDSI levels^b'*'. by Muxi Chen (9294270)

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Multiple linear regression analysis results. by Muxi Chen (9294270)

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Distribution of the same ingredient across different IDDSI levels (Percentages and frequencies) and statistical significance analysis (Chi-square analysis). by Muxi Chen (9294270)

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Distribution of different ingredients at the same IDDSI level (Percentage and frequency) and statistical significance analysis (Chi-square test)^b'*'. by Muxi Chen (9294270)

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Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate... by Hua Huo (1818133)

Model prediction error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model prediction error trend chart. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model prediction error analysis index. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Predicted (orange dashed line) and actual (blue line) numbers of systemic antibiotic (J01) prescriptions between January 2017 and February 2022; all prescriptions and age subgroup... by Heini Kari (12961901)

STL Linear Combination Forecast Graph. by Xiangjuan Liu (618000)

Comparison of the cohesion ranges of different food categories under IDDSI levels. by Muxi Chen (9294270)

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Relationships between cohesiveness and IDDSI levels. by Muxi Chen (9294270)

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Hardness increase across different IDDSI levels and food categories. by Muxi Chen (9294270)

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Relationships between adhesiveness and IDDSI levels. by Muxi Chen (9294270)

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Comparison of adhesiveness ranges for different food categories under IDDSI levels. by Muxi Chen (9294270)

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