About this Course

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Coursera Labs
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Intermediate Level

Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.

Approx. 26 hours to complete
English

What you will 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.

Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs External Link
Intermediate Level

Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.

Approx. 26 hours to complete
English

Offered by

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University of Colorado Boulder

Start working towards your degree

This Course is part of an online degree program offered by the University of Colorado Boulder. When you enroll in a for-credit non-degree course through the university and complete it online, it counts as credit hours towards a degree at CU-Boulder. All you have to do is apply through the university.

Syllabus - What you will learn from this course

Week
1
Week 1
7 hours to complete

Point Estimation

7 hours to complete
10 videos (Total 142 min), 11 readings, 4 quizzes
Week
2
Week 2
3 hours to complete

Maximum Likelihood Estimation

3 hours to complete
5 videos (Total 67 min), 5 readings, 2 quizzes
Week
3
Week 3
5 hours to complete

Large Sample Properties of Maximum Likelihood Estimators

5 hours to complete
5 videos (Total 100 min), 5 readings, 2 quizzes
Week
4
Week 4
7 hours to complete

Confidence Intervals Involving the Normal Distribution

7 hours to complete
5 videos (Total 99 min), 5 readings, 3 quizzes

About the Data Science Foundations: Statistical Inference Specialization

Data Science Foundations: Statistical Inference

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