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.
Ce cours fait partie de la Spécialisation Statistics with Python
Offert par
Spécialisation Statistics with PythonUniversité du MichiganÀ propos de ce cours
90,796 consultations récentes
Résultats de carrière des étudiants
33%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
50%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 3 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Approx. 18 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
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
Résultats de carrière des étudiants
33%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
50%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 3 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Approx. 18 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
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.
3 heures pour terminer
9 vidéos (Total 67 min), 5 lectures, 1 quiz
9 vidéos
Inferential Statistical Analysis with Python Guidelines4 min
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
About Our Datasets2 min
Help Us Learn More About You!10 min
This or That Reference10 min
1 exercice pour s'entraîner
Python Basics Assessment15 min
5 heures pour terminer
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 will appear throughout the week to test your understanding. In addition, you’ll learn how to create confidence intervals in Python.
5 heures pour terminer
12 vidéos (Total 118 min), 3 lectures, 3 quiz
12 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
Introduction to Confidence Intervals in Python12 min
Confidence Intervals for Differences between Population Parameters21 min
3 lectures
Confidence Intervals: Other Considerations15 min
What Affects the Standard Error of an Estimate?10 min
Additional Practice: Confidence Intervals1 min
3 exercices pour s'entraîner
Practice Quiz: All About Confidence Intervals30 min
Sample Size & Assumptions
Confidence Intervals Assessment1 h
6 heures pour terminer
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. Quizzes and a peer assessment will appear throughout the week to test your understanding.
6 heures pour terminer
12 vidéos (Total 138 min), 4 lectures, 3 quiz
12 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
Chocolate & Cycling Assignment2 min
Introduction to Hypothesis Testing in Python20 min
Walk-Through: Hypothesis Testing with NHANES13 min
4 lectures
Hypothesis Testing: Other Considerations10 min
The Relationship between Confidence Intervals & Hypothesis Testing5 min
Chocolate & Cycling Assignment Instructions5 min
Additional Practice: Hypothesis Testing1 min
2 exercices pour s'entraîner
Name That Scenario15 min
Hypothesis Testing in Python Assessment1 h
4 heures pour terminer
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.
4 heures pour terminer
6 vidéos (Total 77 min), 4 lectures, 1 quiz
6 vidéos
Descriptive Inference Examples for Single Variables Using Confidence Intervals12 min
Descriptive Inference Examples for Single Variables Using Hypothesis Testing12 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
4 lectures
Assumptions Consistency5 min
Revisiting Examples: Accounting for Complex Samples10 min
Course Feedback10 min
Keep Learning with Michigan Online10 min
1 exercice pour s'entraîner
Assessment30 min
Avis
Meilleurs avis pour INFERENTIAL STATISTICAL ANALYSIS WITH PYTHON
par RZ1 avr. 2020
This is a very great course. Statistics by itself is a very powerful tool for solving real world problems. Combine it with the knowledge of Python, there no limit to what you can achieve.
par AA27 mai 2020
The best part of this that it is designed in a way that it encourages people to dig deeper and explore more. The instructors have done a great job in making the curriculam this good.
par SS19 mars 2020
Great Course. There are so many example to understand the topic. I really enjoyed every lesson of this specialization. I am going forward for the next one.
par RR6 mars 2019
If you are interested in statistics and statistical analysis, this course gets you grounded in the essential aspects of statistics. Excellent instructors.
À propos du 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
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Is financial aid available?
D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.
