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What you'll learn

Identify characteristics of “good” estimators and be able to compare competing estimators.

Construct sound estimators using the techniques of maximum likelihood and method of moments estimation.

Construct and interpret confidence intervals for one and two population means, one and two population proportions, and a population variance.

Skills you'll gain

Statistical Inference

Statistical Analysis

Sampling (Statistics)

Probability & Statistics

Data Science

Statistical Methods

Statistical Modeling

Probability Distribution

There are 6 modules in this course

This course 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. Logo adapted from photo by Christopher Burns on Unsplash.

Point Estimation

Maximum Likelihood Estimation

Large Sample Properties of Maximum Likelihood Estimators

Confidence Intervals Involving the Normal Distribution

Beyond Normality: Confidence Intervals Unleashed!