Statistics: Confidence Intervals and Hypothesis Tests
This course is part of Statistics, Confidence Intervals and Hypothesis Tests.
This advanced statistics course focuses on two fundamental methodologies: confidence intervals and hypothesis testing. Students learn to make probabilistic statements about population parameters and test statistical hypotheses rigorously. The course covers various scenarios including normal distributions, variance analysis, and proportion testing. Through practical examples like drug efficacy studies and electoral polling, students develop skills in formulating and interpreting statistical tests and confidence intervals.
Instructors:
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What you'll learn
Formulate and interpret confidence intervals for various distributions
Design and conduct hypothesis tests with error control
Analyze normal distributions with known and unknown parameters
Evaluate differences between population means and variances
Apply statistical tests to real-world research questions
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 2 modules in this course
This comprehensive course delves into statistical inference methods, focusing on confidence intervals and hypothesis testing. The curriculum covers both theoretical foundations and practical applications, from basic interval estimation to complex hypothesis testing scenarios. Students learn to analyze various probability distributions, assess statistical significance, and make informed decisions based on data. Through real-world examples and hands-on exercises, participants develop skills in formulating and interpreting statistical analyses for research and decision-making.
Confidence Intervals
Module 1
Hypothesis Testing
Module 2
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Statistics, Confidence Intervals and Hypothesis Tests
Instructor
8 Courses
A Distinguished Scholar in Operations Research and Simulation
David Goldsman serves as the Coca-Cola Foundation Professor in the School of Industrial and Systems Engineering at Georgia Tech, where he has established himself as a leading expert in computer simulation and applied statistics since 1984. After completing degrees from Syracuse University (BS/BA in Math/Physics, MS in Math and Computer Science) and Cornell University (MS and PhD in Operations Research), he has made seminal contributions to simulation methodology and applications. His research focuses on computer simulation with emphasis on statistical output analysis, applied probability, ranking and selection, and applications in health systems and airline safety. His excellence has been recognized through numerous awards including the 2023 INFORMS Simulation Society Lifetime Professional Achievement Award, the 2020 INFORMS Fellow Award, and multiple teaching honors including the Alpha Pi Mu Teacher of the Year Award. Beyond his research contributions, which include over 80 journal articles garnering more than 8,800 citations, he has supervised 34 PhD students and served in key leadership roles including Director of Master's Programs at Georgia Tech's ISyE school. His impact extends beyond academia through consulting work in healthcare, airlines, automotive, and banking industries, while maintaining active involvement in professional societies and editorial boards of leading journals in the field.
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