President’s Innovation Fund: AI in Teaching and Learning

Appendices

Contributions of following students are sincerely appreciated:

• Innocent Golungbire Abaa
• Jeremiah Ranon Allis
• Autumn Marie Halvorsen
• Deep Sagar Karki
• Christopher Kuetsinya
• Jason David Turk
• Anuradhani Kaushalya Weerawardhana
• Yahang Zheng
• Undergraduate students in Math 4440 Statistical Learning (spring 2025)
• Graduate students in Math 5820 Data Prediction (spring 2025)

Appendix-1 Missing Mechanism

Appendix-2 Sensitivity in Missing Data_Part1

Appendix-2 Sensitivity in Missing Data_Part2

Appendix-3_Prediction in Missing Data_Part1

Appendix-3_Prediction in Missing Data_Part2

Appendix-4_ Data Imoutation_Part1

Appendix-4_ Data Imoutation_Part2

Appendix-4_ Data Imoutation_Part3

Appendix-5_Imputation methods

Appendix-6 Efficacy

Appendix-7 NHANES

Appendix-8 Hypothesis Testing

Appendix-9 Testing Models

Appendix-10 Multiple Testing

Appendix-11 Data Models

Appendix-12 Missing Data Outlines

Appendix-13 Effectiveness

Appendix-14 Nonparametric Imputation