Inferential Statistical Analysis with Python

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Inferential Statistical Analysis with Python

This course is part of Statistics with Python Specialization

Instructors: Brenda Gunderson

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4 modules

Gain insight into a topic and learn the fundamentals.

935 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

935 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

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.

Skills you'll gain

Tools you'll learn

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Assessments

7 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the Statistics with Python Specialization

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There are 4 modules in this course

In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately.

At the end of each week, learners will apply what they’ve learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

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 assignment1 discussion prompt3 ungraded labs

6 videos Total 40 minutes

Welcome to the Course! 3 minutes
Inferential Statistical Analysis with Python Guidelines 4 minutes
Introduction to Inference Methods: Oh the Things You Will See! 3 minutes
Bag A or Bag B? 14 minutes
This or That? Language and Notation 13 minutes
The Python Statistics Landscape 3 minutes

7 readings Total 62 minutes

Course Syllabus 10 minutes
Meet the Course Team! 10 minutes
Formula Help Sheets 10 minutes
About Our Datasets 2 minutes
Help Us Learn More About You! 10 minutes
This or That Reference 10 minutes
Python Statistical Functions Cheat Sheet 10 minutes

1 assignment Total 15 minutes

Python Basics Assessment 15 minutes

1 discussion prompt Total 10 minutes

Research Questions in Real Life 10 minutes

3 ungraded labs Total 50 minutes

Review of Course 1 Python Concepts 30 minutes
Functions and Lambda Functions, Reading Help Files 20 minutes
Python Basics Assessment Notebook 0 minutes

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 assignments6 ungraded labs

10 videos Total 84 minutes

Estimating a Population Proportion with Confidence 6 minutes
Understanding Confidence Intervals 11 minutes
Demo: Seeing Theory 5 minutes
Assumptions for a Single Population Proportion Confidence Interval 3 minutes
Conservative Approach & Sample Size Consideration 9 minutes
Estimating a Difference in Population Proportions with Confidence 6 minutes
Interpretations & Assumptions for Two Population Proportion Intervals 4 minutes
Estimating a Population Mean with Confidence 15 minutes
Estimating a Mean Difference for Paired Data 10 minutes
Estimating a Difference in Population Means with Confidence (for Independent Groups) 15 minutes

5 readings Total 55 minutes

Confidence Intervals: Other Considerations 15 minutes
What Affects the Standard Error of an Estimate? 10 minutes
t-distributions vs. z-distributions 10 minutes
Additional Practice: An Introductory Guide to PDFs and CDFs 10 minutes
Napping and Non-Napping Toddlers Article for Python Assessment 10 minutes

3 assignments Total 120 minutes

Practice Quiz: All About Confidence Intervals 30 minutes
Sample Size & Assumptions 30 minutes
Confidence Intervals Assessment 60 minutes

6 ungraded labs Total 220 minutes

Introduction to Confidence Intervals in Python 20 minutes
Confidence Intervals for Differences between Population Parameters 20 minutes
Case Study Using Confidence Intervals with NHANES 60 minutes
More Practice: Confidence intervals with NHANES 60 minutes
Solutions to "More Practice: Confidence intervals with NHANES" 60 minutes
Confidence Intervals in Python Assessment Notebook 0 minutes

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 assignments1 peer review1 discussion prompt6 ungraded labs

10 videos Total 103 minutes

Setting Up a Test for a Population Proportion 5 minutes
Testing a One Population Proportion 8 minutes
Setting Up a Test of Difference in Population Proportions 8 minutes
Testing a Difference in Population Proportions 9 minutes
Interview: P-Values, P-Hacking and More 25 minutes
One Mean: Testing about a Population Mean with Confidence 17 minutes
Testing a Population Mean Difference 14 minutes
Testing for a Difference in Population Means (for Independent Groups) 13 minutes
Demo: Name That Scenario 3 minutes
Chocolate & Cycling Assignment 2 minutes

2 readings Total 15 minutes

Hypothesis Testing: Other Considerations 10 minutes
The Relationship between Confidence Intervals & Hypothesis Testing 5 minutes

2 assignments Total 75 minutes

Name That Scenario 15 minutes
Hypothesis Testing in Python Assessment 60 minutes

1 peer review Total 60 minutes

Chocolate & Cycling Assignment 60 minutes

1 discussion prompt Total 10 minutes

P-Values and P-Hacking 10 minutes

6 ungraded labs Total 235 minutes

Introduction to Hypothesis Testing in Python 20 minutes
Walk-Through: Hypothesis Testing with NHANES 20 minutes
Case Study Using Hypothesis Testing with NHANES 60 minutes
More Practice: Hypothesis testing with NHANES 60 minutes
Solutions to "More Practice: Hypothesis testing with NHANES" 60 minutes
Hypothesis Testing in Python Assessment Notebook 15 minutes

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 assignment

6 videos Total 77 minutes

The Importance of Good Research Questions for Sound Inference 10 minutes
Descriptive Inference Examples for Single Variables Using Confidence Intervals 13 minutes
Descriptive Inference Examples for Single Variables Using Hypothesis Testing 13 minutes
Comparing Means for Two Independent Samples: An Example 15 minutes
Comparing Means for Two Paired Samples: An Example 12 minutes
Comparing Proportions for Two Independent Samples: An Example 14 minutes

5 readings Total 45 minutes

Assumptions Consistency 5 minutes
Comparing Proportions for Two Independent Samples 10 minutes
Revisiting Examples: Accounting for Complex Samples 10 minutes
Course Feedback 10 minutes
Keep Learning with Michigan Online 10 minutes

1 assignment Total 30 minutes

Assessment 30 minutes

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Instructors

Instructor ratings

(151 ratings)

Brenda Gunderson

University of Michigan

3 Courses 170,431 learners

Offered by

University of Michigan

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Reviewed on Jun 21, 2019

A very in-depth learning material for inferential statistics. Very good explanation of p-value which clarifies some of the prevailing misunderstandings.

Reviewed on Dec 4, 2019

It is absolutely great. Instructors are veeeery pasionated with what they do, and the course material is very good.I really like this course.

Reviewed on Aug 7, 2022

Useful course to learn basic concepts of inferential statistical analysis. However, I would expect more Python exercises/assignments than the essay-type writing assignment.

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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.