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. 34 hours to complete
English

What you will learn

  • Define a composite hypothesis and the level of significance for a test with a composite null hypothesis.

  • Define a test statistic, level of significance, and the rejection region for a hypothesis test. Give the form of a rejection region.

  • Perform tests concerning a true population variance.

  • Compute the sampling distributions for the sample mean and sample minimum of the exponential distribution.

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. 34 hours to complete
English

Offered by

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University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

Week
1
Week 1
8 hours to complete

Fundamental Concepts of Hypothesis Testing

8 hours to complete
6 videos (Total 70 min), 6 readings, 2 quizzes
Week
2
Week 2
8 hours to complete

Composite Tests, Power Functions, and P-Values

8 hours to complete
7 videos (Total 125 min), 7 readings, 2 quizzes
Week
3
Week 3
8 hours to complete

t-Tests and Two-Sample Tests

8 hours to complete
7 videos (Total 140 min), 7 readings, 2 quizzes
Week
4
Week 4
4 hours to complete

Beyond Normality

4 hours to complete
6 videos (Total 118 min), 6 readings, 2 quizzes

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About the Data Science Foundations: Statistical Inference Specialization

Data Science Foundations: Statistical Inference

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