*This 4-course Specialization covers the use of statistical methods in today's business, industrial, and social environments, including several new methods and applications. Prof. Douglas Montgomery reflects: "H.G. Wells foresaw an era when the understanding of basic statistics would be as important for citizenship as the ability to read and write. Modern Statistics for Data-Driven Decision-Making teaches the basics of working with and interpreting data, skills necessary to succeed in Wells’s 'new great complex world' that we now inhabit." *In this course, learners will gain an ability to apply basic statistical methods for data description and visualization, inference, and decision-making. *In the first module, you will enter into Descriptive Statistics, and apply apply basic statistical methods for data description and visualization. We also invite you to orient yourself to the course design, read the instructor bios, and review the learning outcomes. Please begin when ready.

In Module 2, you will learn the probability foundations that support statistical modeling and data-driven decision-making. You will work with discrete and continuous probability distributions, compute probabilities and distribution summaries, and understand how probability models describe uncertainty in real-world contexts. Before starting, be sure to view the course introduction video and review the learning objectives.

In Module 3, we explore the basic concepts of random sampling and the relationship between random sampling and inference. We also construct confidence intervals to estimate means and variances of one or two populations and hypotheses tests and confidence interval estimation on the mean of a population whose variance is known. Be sure to review the learning objectives before beginning work in this module.

In Module 4, we will review bootstrapping methods that can be used to solve a statistical problem. Be sure you review the learning objectives before beginning work in this module.

In Module 5, we will review applications of big data in statistical methods and models. Be sure to view videos for this module, complete the readings, and any assignments. Begin by reviewing the learning objectives before beginning work in this module.

Module 6 introduces core regression methods, including multiple linear regression, diagnostics, regularization, GLMs, and nonlinear regression. Assessments reinforce conceptual understanding and practical interpretation.