Best Statistical Modeling Books & Best Statistical Modeling Courses 2024

Table of Contents

Best Statistical Modeling Courses 2022

Applied Statistical Modeling for Data Analysis in R

What you’ll learn:

Analyze their own data by applying appropriate statistical techniques
Interpret the results of their statistical analysis
Identify which statistical techniques are best suited to their data and questions
Have a strong foundation in fundamental statistical concepts
Implement different statistical analysis in R and interpret the results
Build intuitive data visualizations
Carry out formalized hypothesis testing
Implement linear modelling techniques such multiple regressions and GLMs
Implement advanced regression analysis and multivariate analysis

Statistical Modeling for Data Science Applications Specialization

Statistical Modeling for Data Science Applications

Learn More on Coursera.org

Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.

This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Statistics Essentials for Analytics

Learn More on Edureka.co

A self-paced course that helps you to understand the various Statistical Techniques from the very basics and how each technique is employed on a real world data set to analyze and conclude insights. Statistics and its methods are the backend of Data Science to “understand, analyze and predict actual phenomena”. Machine learning employs different techniques and theories drawn from statistical & probabilistic fields.

Best Statistical Modeling Books 2022

Statistical Modeling: A Fresh Approach

Kaplan, Daniel T. (Author)
English (Publication Language)
440 Pages - 07/14/2009 (Publication Date) - CreateSpace Independent Publishing Platform (Publisher)

The 2nd edition (green cover) is now available and the first edition (brown cover) is now obsolete. The new edition makes use of the MOSAIC package in R (see www.mosaic-web.org/StatisticalModeling) introduces inference earlier, and incorporates suggestions and corrections offered by readers of the first edition. We continue to make the first edition available for students seeking to match the book used in a class that still uses the first edition.

Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data

Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex...

Dupont, William D. (Author)
English (Publication Language)
10/24/2009 (Publication Date) - Cambridge University Press (Publisher)

For biomedical researchers, the new edition of this standard text guides readers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics. The Stata statistical software package is used to perform the analyses, in this edition employing the intuitive version 10.

Topics covered include linear, logistic and Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measure analysis of variance. Restricted cubic splines are used to model non-linear relationships. Each method is introduced in its simplest form and then extended to cover more complex situations. An appendix will help the reader select the most appropriate statistical methods for their data. The text makes extensive use of real data sets available online through Vanderbilt University.

Statistical Modeling and Inference for Social Science

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Statistical Modeling and Inference for Social Science (Analytical Methods for Social Research)

Hardcover Book
Gailmard, Sean (Author)
English (Publication Language)

This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph.D. students. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables – the primary aim of social scientists. Gailmard explains how social scientists express and test substantive theoretical arguments in various models. Chapter exercises require application of concepts to actual data and extend students’ grasp of core theoretical concepts. Students will complete the book with the ability to read and critique statistical applications in their fields of interest.

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