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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Statistics with Python
Inferential Statistical Analysis with Python
von
Über diesen Kurs
3.9
56 Bewertungen
Was Sie lernen werden
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.
Kompetenzen, die Sie erwerben
Confidence IntervalPython ProgrammingStatistical InferenceStatistical Hypothesis Testing
Lehrplan - Was Sie in diesem Kurs lernen werden
Woche
13 Stunden zum Abschließen
WEEK 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 Videos (Gesamt 62 min), 5 Lektüren, 1 Quiz
8 Videos
Introduction to Inference Methods: Oh the Things You Will See!3m
Bag A or Bag B?13m
Introduction to Bayesian4m
This or That? Language and Notation13m
The Python Statistics Landscape2m
Intermediate Python Concepts: Lists vs Numpy Arrays10m
Functions and Lambda Functions, Reading Help Files11m
5 Lektüren
Course Syllabus5m
Meet the Course Team!10m
Help Us Learn More About You!10m
About Our Datasets2m
This or That Reference10m
1 praktische Übung
Python Basics Assessment15m
Woche
26 Stunden zum Abschließen
WEEK 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 Videos (Gesamt 121 min), 4 Lektüren, 4 Quiz
13 Videos
Understanding Confidence Intervals10m
Demo: Seeing Theory5m
Assumptions for a Single Population Proportion Confidence Interval3m
Conservative Approach & Sample Size Consideration8m
Estimating a Difference in Population Proportions with Confidence6m
Interpretations & Assumptions for Two Population Proportion Intervals4m
Estimating a Population Mean with Confidence14m
Estimating a Mean Difference for Paired Data10m
Estimating a Difference in Population Means with Confidence (for Independent Groups)14m
Chocolate & Cycling Assignment2m
Introduction to Confidence Intervals in Python12m
Confidence Intervals for Differences between Population Parameters21m
4 Lektüren
Confidence Intervals: Other Considerations15m
What Affects the Standard Error of an Estimate?10m
Chocolate & Cycling Assignment Instructions5m
Additional Practice: Confidence Intervals1m
3 praktische Übungen
Practice Quiz: All About Confidence Intervals14m
Sample Size & Assumptions
Confidence Intervals Assessment
Woche
35 Stunden zum Abschließen
WEEK 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 Videos (Gesamt 136 min), 3 Lektüren, 2 Quiz
11 Videos
Testing a One Population Proportion8m
Setting Up a Test of Difference in Population Proportions7m
Testing a Difference in Population Proportions8m
Interview: P-Values, P-Hacking and More24m
One Mean: Testing about a Population Mean with Confidence17m
Testing a Population Mean Difference13m
Testing for a Difference in Population Means (for Independent Groups)12m
Demo: Name That Scenario2m
Introduction to Hypothesis Testing in Python20m
Walk-Through: Hypothesis Testing with NHANES13m
3 Lektüren
Hypothesis Testing: Oher Considerations10m
The Relationship between Confidence Intervals & Hypothesis Testing5m
Additional Practice: Hypothesis Testing1m
2 praktische Übungen
Name That Scenario15m
Hypothesis Testing in Python Assessment
Woche
44 Stunden zum Abschließen
WEEK 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 Videos (Gesamt 77 min), 3 Lektüren, 1 Quiz
6 Videos
Descriptive Inference Examples for Single Variables Using Hypothesis Testing12m
Descriptive Inference Examples for Single Variables Using Confidence Intervals12m
Comparing Means for Two Independent Samples: An Example14m
Comparing Means for Two Paired Samples: An Example12m
Comparing Proportions for Two Independent Samples: An Example13m
3 Lektüren
Assumptions Consistency5m
Revisiting Examples: Accounting for Complex Samples10m
Course Feedback10m
1 praktische Übung
Assessment10m
50%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
50%
erhalten Sie eine Gehaltserhöhung oder Beförderung
Top-Bewertungen
If you are interested in statistics and statistical analysis, this course gets you grounded in the essential aspects of statistics. Excellent instructors.
Dozenten
Brenda Gunderson
Lecturer IV and Research FellowDepartment of Statistics
Brady T. West
Research Associate ProfessorInstitute for Social Research
Kerby Shedden
ProfessorDepartment of Statistics
Über University of 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.
Über die Spezialisierung 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.
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Sobald Sie sich für ein Zertifikat angemeldet haben, haben Sie Zugriff auf alle Videos, Quizspiele und Programmieraufgaben (falls zutreffend). Aufgaben, die von anderen Kursteilnehmern bewertet werden, können erst dann eingereicht und überprüft werden, wenn Ihr Unterricht begonnen hat. Wenn Sie sich den Kurs anschauen möchten, ohne ihn zu kaufen, können Sie womöglich auf bestimmte Aufgaben nicht zugreifen.
Was bekomme ich, wenn ich diese Spezialisierung abonniere?
Wenn Sie sich für den Kurs anmelden, erhalten Sie Zugriff auf alle Kurse der Spezialisierung und Sie erhalten nach Abschluss aller Arbeiten ein Zertifikat. Ihr elektronisches Zertifikat wird zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.
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