ANA605 v1

Poldrack, R. (2019). Statistica Thinking for the 21st Century.

• Chapter 8.5 Using Simulation for Statistics: The Bootstrap. Learn about simulating a statistic using bootstrapping, a resampling method that constructs a sampling distribution by repeatedly drawing samples with replacement from an observed dataset.

Russell, J. M. (Ed.). (2021). Significant Statistics: An Introduction to Statistics. Adapted from Illowsky, B., Dean, S., Diez, D., Cetinkaya-Rundel, M., Barr, C. D., Vu, J., & Harrington, D. Department of Statistics, University Libraries at Virginia Tech.

• Chapter 6: Foundations of Inference. 6.1 Point Estimation and Sampling Distributions and 6.2 The Sampling Distribution of the Sample Mean (σ Known). Understand point estimates of a mean using sampling distributions subject to the Central Limit Theorem.

Çetinkaya-Rundel, M. & Hardin, J. Introduction to modern statistics (2e., ch. 12).

• Chapter 12.1 & 12.2. Application of bootstrapping for data simulation.

The Friendly Statistician. (2025). When to use bootstrapping in statistics? [Video]. YouTube.

• This video will provide a summary introduction regarding when to use boostrapping to better understand variability of data.

Starmer, J. (2021). Bootstrapping Main Ideas!!! [Video]. YouTube.

• This video provides detail on bootstrapping and how it can be used to provide more accurate statistica inferences, especially with smaller sample sizes.

Bruce, P., Bruce, A., Gedeck, P. (2020). Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python (2nd). O’Reilly Media, Inc.

• Chapter 2: Data and Sampling Distributions, Section: The Bootstrap. Explore the method of creating a Guassian distribution of statistic through repeated sampling with replacement from a single sample.