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 forma parte de Programa especializado: Statistics with Python
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Programa especializado: Statistics with PythonUniversidad de MíchiganAcerca de este Curso
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Fechas límite flexibles
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Curso 2 de 3 en
Nivel intermedio
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Aprox. 19 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)
Qué aprenderás
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 obtendrás
- Confidence Interval
- Python Programming
- Statistical Inference
- Statistical Hypothesis Testing
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 2 de 3 en
Nivel intermedio
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Aprox. 19 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)
ofrecido por
Universidad de Míchigan
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 - Qué aprenderás en este curso
3 horas para completar
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 completar
9 videos (Total 67 minutos), 6 lecturas, 1 cuestionario
7 horas para completar
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 completar
12 videos (Total 118 minutos), 5 lecturas, 3 cuestionarios
7 horas para completar
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 completar
12 videos (Total 138 minutos), 4 lecturas, 3 cuestionarios
2 horas para completar
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 completar
6 videos (Total 77 minutos), 4 lecturas, 1 cuestionario
Reseñas
- 5 stars73,90 %
- 4 stars17,56 %
- 3 stars5,48 %
- 2 stars1,58 %
- 1 star1,46 %
Principales reseñas sobre INFERENTIAL STATISTICAL ANALYSIS WITH PYTHON
por SS31 de may. de 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
por XJ21 de jun. de 2019
A very in-depth learning material for inferential statistics. Very good explanation of p-value which clarifies some of the prevailing misunderstandings.
por MG10 de sep. de 2019
It was very good course, everything was very well explained and the activities were challenging enough to practice the knowledges obtain.
por RZ1 de abr. de 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.
Acerca de Programa especializado: 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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