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
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Spécialisation Statistics with PythonUniversité du MichiganÀ propos de ce cours
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Cours 2 sur 3 dans le
Niveau intermédiaire
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Approx. 19 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 Interval
- Python Programming
- Statistical Inference
- Statistical Hypothesis Testing
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
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
Niveau intermédiaire
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Approx. 19 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 de 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), 6 lectures, 1 quiz
7 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.
7 heures pour terminer
12 vidéos (Total 118 min), 5 lectures, 3 quiz
7 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.
7 heures pour terminer
12 vidéos (Total 138 min), 4 lectures, 3 quiz
2 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.
2 heures pour terminer
6 vidéos (Total 77 min), 4 lectures, 1 quiz
Avis
- 5 stars73,80 %
- 4 stars17,38 %
- 3 stars5,66 %
- 2 stars1,63 %
- 1 star1,51 %
Meilleurs avis pour INFERENTIAL STATISTICAL ANALYSIS WITH PYTHON
par SS31 mai 2021
Do you want to have an in-depth understanding of statistics in data science? Take the full specialization and you won't regret it. Fantastic course
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 YP7 juil. 2019
Good theoretical foundation, but lacks explanation on python libraries extensively used in the course.
par JB30 juin 2020
Harder than the first of the three courses in the Specialization and that makes it all the better. Really very well done!
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.
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