University of Michigan
Inferential Statistical Analysis with Python
University of Michigan

Inferential Statistical Analysis with Python

This course is part of Statistics with Python Specialization

Taught in English

Some content may not be translated

Brenda Gunderson
Brady T. West
Kerby Shedden

Instructors: Brenda Gunderson

43,953 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.6

(885 reviews)

|

94%

Intermediate level

Recommended experience

21 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Determine assumptions needed to calculate confidence intervals for their respective population parameters.

  • Create confidence intervals in Python and interpret the results.

  • Review how inferential procedures are applied and interpreted step by step when analyzing real data.

  • Run hypothesis tests in Python and interpret the results.

Details to know

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Assessments

7 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(885 reviews)

|

94%

Intermediate level

Recommended experience

21 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Statistics with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

In this first week, we’ll review the course syllabus and discover the various concepts and objectives to be mastered in weeks to come. You’ll be introduced to inference methods and some of the research questions we’ll discuss in the course, as well as an overall framework for making decisions using data, considerations for how you make those decisions, and evaluating errors that you may have made. On the Python side, we’ll review some high level concepts from the first course in this series, Python’s statistics landscape, and walk through intermediate level Python concepts. All of the course information on grading, prerequisites, and expectations are on the course syllabus and you can find more information on our Course Resources page.

What's included

6 videos7 readings1 quiz1 discussion prompt3 ungraded labs

In this second week, we will learn about estimating population parameters via confidence intervals. You will be introduced to five different types of population parameters, assumptions needed to calculate a confidence interval for each of these five parameters, and how to calculate confidence intervals. Quizzes will appear throughout the week to test your understanding. In addition, you’ll learn how to create confidence intervals in Python.

What's included

10 videos5 readings3 quizzes6 ungraded labs

In week three, we’ll learn how to test various hypotheses - using the five different analysis methods covered in the previous week. We’ll discuss the importance of various factors and assumptions with hypothesis testing and learn to interpret our results. We will also review how to distinguish which procedure is appropriate for the research question at hand. Quizzes and a peer assessment will appear throughout the week to test your understanding.

What's included

10 videos2 readings2 quizzes1 peer review1 discussion prompt6 ungraded labs

In the final week of this course, we will walk through several examples and case studies that illustrate applications of the inferential procedures discussed in prior weeks. Learners will see examples of well-formulated research questions related to the study designs and data sets that we have discussed thus far, and via both confidence interval estimation and formal hypothesis testing, we will formulate inferential responses to those questions.

What's included

6 videos5 readings1 quiz

Instructors

Instructor ratings
4.7 (139 ratings)
Brenda Gunderson
University of Michigan
3 Courses150,292 learners

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