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.
Ce cours fait partie de la Spécialisation Statistics with Python
Inferential Statistical Analysis with Python
proposé par
À propos de ce cours
3.6
9 notes
Ce que vous allez apprendre
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.
Compétences que vous acquerrez
Confidence IntervalPython ProgrammingStatistical InferenceStatistical Hypothesis Testing
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1WEEK 1 - OVERVIEW & INFERENCE PROCEDURES
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.
8 vidéos (Total 62 min), 5 lectures, 1 quiz
8 vidéos
Introduction to Inference Methods: Oh the Things You Will See!3 min
Bag A or Bag B?13 min
Introduction to Bayesian4 min
This or That? Language and Notation13 min
The Python Statistics Landscape2 min
Intermediate Python Concepts: Lists vs Numpy Arrays10 min
Functions and Lambda Functions, Reading Help Files11 min
5 lectures
Course Syllabus5 min
Meet the Course Team!10 min
Help Us Learn More About You!10 min
About Our Datasets2 min
This or That Reference10 min
1 exercice pour s'entraîner
Python Basics Assessment15 min
Semaine
2WEEK 2 - CONFIDENCE INTERVALS
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 and a peer assessment will appear throughout the week to test your understanding. In addition, you’ll learn how to create confidence intervals in Python.
13 vidéos (Total 121 min), 4 lectures, 4 quiz
13 vidéos
Understanding Confidence Intervals10 min
Demo: Seeing Theory5 min
Assumptions for a Single Population Proportion Confidence Interval3 min
Conservative Approach & Sample Size Consideration8 min
Estimating a Difference in Population Proportions with Confidence6 min
Interpretations & Assumptions for Two Population Proportion Intervals4 min
Estimating a Population Mean with Confidence14 min
Estimating a Mean Difference for Paired Data10 min
Estimating a Difference in Population Means with Confidence (for Independent Groups)14 min
Chocolate & Cycling Assignment2 min
Introduction to Confidence Intervals in Python12 min
Confidence Intervals for Differences between Population Parameters21 min
4 lectures
Confidence Intervals: Other Considerations15 min
What Affects the Standard Error of an Estimate?10 min
Chocolate & Cycling Assignment Instructions5 min
Additional Practice: Confidence Intervals1 min
3 exercices pour s'entraîner
Practice Quiz: All About Confidence Intervals14 min
Sample Size & Assumptions
Confidence Intervals Assessments
Semaine
3WEEK 3 - HYPOTHESIS TESTING
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.
11 vidéos (Total 136 min), 3 lectures, 2 quiz
11 vidéos
Testing a One Population Proportion8 min
Setting Up a Test of Difference in Population Proportions7 min
Testing a Difference in Population Proportions8 min
Interview: P-Values, P-Hacking and More24 min
One Mean: Testing about a Population Mean with Confidence17 min
Testing a Population Mean Difference13 min
Testing for a Difference in Population Means (for Independent Groups)12 min
Demo: Name That Scenario2 min
Introduction to Hypothesis Testing in Python20 min
Walk-Through: Hypothesis Testing with NHANES13 min
3 lectures
Hypothesis Testing: Oher Considerations10 min
The Relationship between Confidence Intervals & Hypothesis Testing5 min
Additional Practice: Hypothesis Testing1 min
2 exercices pour s'entraîner
Name That Scenario15 min
Hypothesis Testing in Python Assessments
Semaine
4WEEK 4 - LEARNER APPLICATION
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.
6 vidéos (Total 77 min), 3 lectures, 1 quiz
6 vidéos
Descriptive Inference Examples for Single Variables Using Hypothesis Testing12 min
Descriptive Inference Examples for Single Variables Using Confidence Intervals12 min
Comparing Means for Two Independent Samples: An Example14 min
Comparing Means for Two Paired Samples: An Example12 min
Comparing Proportions for Two Independent Samples: An Example13 min
3 lectures
Assumptions Consistency5 min
Revisiting Examples: Accounting for Complex Samples10 min
Course Feedback10 min
1 exercice pour s'entraîner
Assessment10 min
Enseignants
Brenda Gunderson
Lecturer IV and Research FellowDepartment of Statistics
Brady T. West
Research Associate ProfessorInstitute for Social Research
Kerby Shedden
ProfessorDepartment of Statistics
À propos de Université du Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
À propos de la Spécialisation Statistics with Python
This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.
Foire Aux Questions
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