Lecturer IV and Research Fellow
Inferential Statistical Analysis with Python
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.
4 Modules
24 Hours
6 hrs per module (approx.)
Rating
About Inferential Statistical Analysis with Python
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.
Skills You'll Gain
- Data Analysis
- Data Literacy
- Pandas (Python Package)
- Python For Data Analysis
- Python (Programming Language)
- Statistical Programming
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Inferential Statistical Analysis with Python, the second course in the Statistics with Python series, builds skills in statistical inference using real-world data and Python programming. Learners explore confidence intervals, hypothesis testing, and interpretation of inferential results while applying concepts through hands-on labs using Jupyter Notebooks and Python libraries such as Pandas, Statsmodels, and Seaborn.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Overview & Inference Procedures
- Video: Welcome to the Course!
- Video: Inferential Statistical Analysis with Python Guidelines
- Reading: Course Syllabus
- Reading: Meet the Course Team!
- Reading: Formula Help Sheets
- Reading: About Our Datasets
- Reading: Help Us Learn More About You!
- Video: Introduction to Inference Methods: Oh the Things You Will See!
- Video: Bag A or Bag B?
- Discussion Prompt: Research Questions in Real Life
- Video: This or That? Language and Notation
- Reading: This or That Reference
- Video: The Python Statistics Landscape
- Ungraded Lab: Review of Course 1 Python Concepts
- Ungraded Lab: Functions and Lambda Functions, Reading Help Files
- Reading: Python Statistical Functions Cheat Sheet
- Ungraded Lab: Python Basics Assessment Notebook
Module 2: Confidence Intervals
- Video: Estimating a Population Proportion with Confidence
- Video: Understanding Confidence Intervals
- Video: Demo: Seeing Theory
- Video: Assumptions for a Single Population Proportion Confidence Interval
- Video: Conservative Approach & Sample Size Consideration
- Graded Assignment: Practice Quiz: All About Confidence Intervals
- Video: Estimating a Difference in Population Proportions with Confidence
- Video: Interpretations & Assumptions for Two Population Proportion Intervals
- Video: Estimating a Population Mean with Confidence
- Video: Estimating a Mean Difference for Paired Data
- Video: Estimating a Difference in Population Means with Confidence (for Independent Groups)
- Reading: Confidence Intervals: Other Considerations
- Reading: What Affects the Standard Error of an Estimate?
- Reading: t-distributions vs. z-distributions
- Ungraded Lab: Introduction to Confidence Intervals in Python
- Ungraded Lab: Confidence Intervals for Differences between Population Parameters
- Ungraded Lab: Case Study Using Confidence Intervals with NHANES
- Reading: Additional Practice: An Introductory Guide to PDFs and CDFs
- Ungraded Lab: More Practice: Confidence intervals with NHANES
- Ungraded Lab: Solutions to "More Practice: Confidence intervals with NHANES"
- Reading: Napping and Non-Napping Toddlers Article for Python Assessment
- Ungraded Lab: Confidence Intervals in Python Assessment Notebook
Module 3: Hypothesis Testing
- Video: Setting Up a Test for a Population Proportion
- Video: Testing a One Population Proportion
- Video: Setting Up a Test of Difference in Population Proportions
- Video: Testing a Difference in Population Proportions
- Video: Interview: P-Values, P-Hacking and More
- Discussion Prompt: P-Values and P-Hacking
- Video: One Mean: Testing about a Population Mean with Confidence
- Video: Testing a Population Mean Difference
- Video: Testing for a Difference in Population Means (for Independent Groups)
- Reading: Hypothesis Testing: Other Considerations
- Reading: The Relationship between Confidence Intervals & Hypothesis Testing
- Video: Demo: Name That Scenario
- Video: Chocolate & Cycling Assignment
- Ungraded Lab: Introduction to Hypothesis Testing in Python
- Ungraded Lab: Walk-Through: Hypothesis Testing with NHANES
- Ungraded Lab: Case Study Using Hypothesis Testing with NHANES
- Ungraded Lab: More Practice: Hypothesis testing with NHANES
- Ungraded Lab: Solutions to "More Practice: Hypothesis testing with NHANES"
- Ungraded Lab: Hypothesis Testing in Python Assessment Notebook
Module 4: Learner Application
- Reading: Assumptions Consistency
- Video: The Importance of Good Research Questions for Sound Inference
- Video: Descriptive Inference Examples for Single Variables Using Confidence Intervals
- Video: Descriptive Inference Examples for Single Variables Using Hypothesis Testing
- Video: Comparing Means for Two Independent Samples: An Example
- Video: Comparing Means for Two Paired Samples: An Example
- Reading: Comparing Proportions for Two Independent Samples
- Video: Comparing Proportions for Two Independent Samples: An Example
- Reading: Revisiting Examples: Accounting for Complex Samples
- Reading: Course Feedback
- Reading: Keep Learning with Michigan Online
Grading Policy
Learners must earn an overall passing score based on multiple assessments. The course grade is based on three quizzes worth 10% each (30%), a module one Python assessment (10%), two additional Python assessments worth 20% each (40%), and a peer-review assessment worth 20%.
Professor
Research Associate Professor
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
High school algebra, successful completion of Course 1 in this specialization or equivalent background
