In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations.
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第 3 門課程（共 3 門）
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中級
Completion of the first two courses in this specialization; high school-level algebra
完成時間大約為15 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將獲得的技能
Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 3 門課程（共 3 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
中級
Completion of the first two courses in this specialization; high school-level algebra
完成時間大約為15 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
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密歇根大学
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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 3 小時
WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING
We begin this third course of the Statistics with Python specialization with an overview of what is meant by “fitting statistical models to data.” In this first week, we will introduce key model fitting concepts, including the distinction between dependent and independent variables, how to account for study designs when fitting models, assessing the quality of model fit, exploring how different types of variables are handled in statistical modeling, and clearly defining the objectives of fitting models.
完成時間為 3 小時
8 個視頻 （總計 73 分鐘）, 6 個閱讀材料, 1 個測驗
完成時間為 5 小時
WEEK 2 - FITTING MODELS TO INDEPENDENT DATA
In this second week, we’ll introduce you to the basics of two types of regression: linear regression and logistic regression. You’ll get the chance to think about how to fit models, how to assess how well those models fit, and to consider how to interpret those models in the context of the data. You’ll also learn how to implement those models within Python.
完成時間為 5 小時
6 個視頻 （總計 85 分鐘）, 4 個閱讀材料, 3 個測驗
完成時間為 4 小時
WEEK 3 - FITTING MODELS TO DEPENDENT DATA
In the third week of this course, we will be building upon the modeling concepts discussed in Week 2. Multilevel and marginal models will be our main topic of discussion, as these models enable researchers to account for dependencies in variables of interest introduced by study designs. We’ll be covering why and when we fit these alternative models, likelihood ratio tests, as well as fixed effects and their interpretations.
完成時間為 4 小時
8 個視頻 （總計 121 分鐘）, 2 個閱讀材料, 2 個測驗
完成時間為 3 小時
WEEK 4: Special Topics
In this final week, we introduce special topics that extend the curriculum from previous weeks and courses further. We will cover a broad range of topics such as various types of dependent variables, exploring sampling methods and whether or not to use survey weights when fitting models, and in-depth case studies utilizing Bayesian techniques to derive insights from data. You’ll also have the opportunity to apply Bayesian techniques in Python.
完成時間為 3 小時
6 個視頻 （總計 105 分鐘）, 4 個閱讀材料, 1 個測驗
審閱
來自FITTING STATISTICAL MODELS TO DATA WITH PYTHON的熱門評論
由 BS 提供2020年1月17日
I am very thankful to you sir.. i have learned so much great things through this course. this course is very helpful for my career. i would like to learn more courses from you. thank you so much.
由 FS 提供2020年10月20日
Overall, the course was a great refresher of statistical theory and application with some great Python exercises. However, some of the Python coding instruction itself could have been more detailed.
由 SM 提供2020年6月15日
A great introduction to regression and bayesian analysis in python. I get that the content is hard, but they sum it all well. I would recommend for those who have prior knowledge of statistics.
由 YT 提供2020年9月21日
The code examples may be more precise with detailed comments. Some codes are not understood, in other words codes can be refactored in a way that can be more suitable for reproducible studies.
關於 Statistics with Python 專項課程
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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