Beyond the T-Test / by Scott A. Pardo.

Format:

Book

Author/Creator:

Pardo, Scott A.

Language:

English

Subjects (All):

Social sciences--Statistical methods.

Social sciences.

Machine learning.

Stochastic models.

Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.

Statistical Learning.

Stochastic Modelling in Statistics.

Local Subjects:

Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.

Statistical Learning.

Stochastic Modelling in Statistics.

Physical Description:

1 online resource (329 pages)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

This book was inspired by years of questions asked by non-statistical professionals, from social scientists, public policy analysts, regulatory affairs specialists, engineers, and physical scientists. It provides them with both an intuitive explanation of many common statistical methods and enough mathematical background to help them justify those methods to others, such as regulatory agencies. It provides an introduction to commonly used methods that are not covered in a first elementary statistics course, such as partial least squares, MCMC, and neural networks. It also provides R code for making all the computations described in the text. As a textbook, it could be used as a second course in statistics for non-statisticians, in fields such as social sciences, public policy, engineering, chemistry, and physics. Many first-year graduate students have had an elementary statistics course, but were not exposed to enough of the mathematics to justify the application of those methods. Furthermore, they often encounter methods and concepts not touched upon in their first statistics course. This book provides the tools required to give a deeper understanding of statistical methods without being all about theorems and proofs.

Contents:

Chapter 1. Populations, Samples, Parameters, and Statistics

Chapter 2. Some Probability Concepts

Chapter 3. Estimation, Hypothesis Testing and the Scientific Method

Chapter 4. Binary Random Variables and Acceptance Sampling Plans

Chapter 5. Continuous Variables, the Normal Distribution, and the Central Limit Theorem

Chapter 6. Continuous Variables and Acceptance Sampling Plans

Chapter 7. Confidence

Chapter 8. Some Confidence Interval Computations, Including Bootstrapping

Chapter 9. Linear Regression, Correlation, and Least Squares

Chapter 10. Analysis of Variance

Chapter 11. Poisson and Exponential Variables, Rate, and Time-to-Event

Chapter 12. 2k Factorial Experiments

Chapter 13. Nonparametric Methods – Rank-Based Tests, Permutation Tests and Resampling Methods

Chapter 14. Nonlinear and Logistic Regression

Chapter 15. Model Building

Chapter 16. Multivariate Analysis

Chapter 17. Bayesian Methods – Markov Chain Montel Carlo Approach

Chapter 18. Machine Learning and Data-Intensive Methods

Chapter 19. Time Series and Dynamic Systems

Index.

ISBN:

3-031-84479-3

OCLC:

1523373319