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.
Este curso faz parte do Programa de cursos integrados Statistics with Python
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Programa de cursos integrados Statistics with PythonUniversidade de MichiganInformações sobre o curso
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Curso 2 de 3 no
Nível intermediário
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Aprox. 19 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
O que você vai aprender
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.
Habilidades que você terá
- Confidence Interval
- Python Programming
- Statistical Inference
- Statistical Hypothesis Testing
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 3 no
Nível intermediário
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Aprox. 19 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Chinês (simplificado), Italiano, Vietnamita, Coreano, Alemão, Russo, Turco, Inglês, Espanhol
oferecido por
Universidade de 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.
Programa - O que você aprenderá com este curso
3 horas para concluir
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 horas para concluir
9 vídeos (Total 67 mín.), 6 leituras, 1 teste
7 horas para concluir
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 horas para concluir
12 vídeos (Total 118 mín.), 5 leituras, 3 testes
7 horas para concluir
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 horas para concluir
12 vídeos (Total 138 mín.), 4 leituras, 3 testes
2 horas para concluir
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 horas para concluir
6 vídeos (Total 77 mín.), 4 leituras, 1 teste
Avaliações
- 5 stars74,07%
- 4 stars17,32%
- 3 stars5,52%
- 2 stars1,59%
- 1 star1,47%
Principais avaliações do INFERENTIAL STATISTICAL ANALYSIS WITH PYTHON
por AA27 de mai de 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.
por AB5 de nov de 2020
Great course with practical experience with Python. There are many courses that teach statistics with R but this is the first one to do so in Python.
por RR26 de jul de 2021
this is absolutely a great course. i happy learning this one. the subjects they explained was crystal clear and i would suggest this to my mates.
por YB28 de mai de 2019
This course is significantly better than the previous one. Nevertheless, if you want to get knowledge about Python, it’s not about this course.
Sobre Programa de cursos integrados 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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