LibGuides: TIM-8501 v5: Lesson 6 Resources

Lesson 6 Resources

Applied Statistics with Data Analytics (R), Zylabs.com

Read Chapters 15 and 16: Introduction to Data Mining & Data Cleansing and Preparation

This reading discusses training, validation, and test partitions along with scaling/transformation.

Data Science Foundations with R, Zylabs.com

This chapter discusses training, validation, and test sets.

ACCESS your Zylabs.com material from the Lesson module in your course.

An Introduction to Statistical Learning with Applications in R
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2023). An introduction to statistical learning with applications in R (2nd ed.). Springer International Publishing.
Read Chapter 5. The chapter covers cross-validation methods.
NIST/SEMATECH Engineering Statistics Handbook
Smeaton, A. (2003). NIST/SEMATECH Engineering Statistics Handbook. https://www.itl.nist.gov/div898/handbook/
- Read Chapter 6.5.2: What to do when Data are Non-normal.
- Read Chapter 4.6.2.4: Transformations to Improve Fit and Equalize Variances.
- Read Chapter 4.6.3.3: Transformations to Improve Fit.
- Read 1.3.3.6: Box-Cox Normality Plot.
These sections discuss statistical transformations.
R for Data Science
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science. " O'Reilly Media, Inc."
- Read Chapter 3: Data Transformation.
This chapter provides foundational transformation techniques.

About Train, Validation and Test Sets in Machine Learning
Shah, T. (2017, December 6). About train, validation and test sets in machine learning. Medium; Towards Data Science. https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7