Inferential Statistical Analysis with Python
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Inferential Statistical Analysis with Python
This course is part of Statistics with Python Specialization
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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.
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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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Reviewed on Jan 21, 2021
Very good course content and mentors & teachers. The course content was very structured. I learnt a lot from the course and gained skills which will definitely gonna help me in future.
Reviewed on Jan 22, 2026
Very clear explanations, good that they force you to run your own code. Would have been good to work with more challenging data.
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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