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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Statistics with Python の専門講座ミシガン大学（University of Michigan）このコースについて
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修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における3の3コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
Completion of the first two courses in this specialization; high school-level algebra
約15時間で修了
英語
字幕：英語, 韓国語
習得するスキル
Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における3の3コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
Completion of the first two courses in this specialization; high school-level algebra
約15時間で修了
英語
字幕：英語, 韓国語
提供：
ミシガン大学（University of 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.
シラバス - 本コースの学習内容
3時間で修了
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.
3時間で修了
8件のビデオ （合計73分）, 6 readings, 1 quiz
8件のビデオ
Fitting Statistical Models to Data with Python Guidelines5 分
What Do We Mean by Fitting Models to Data?18 分
Types of Variables in Statistical Modeling13 分
Different Study Designs Generate Different Types of Data: Implications for Modeling9 分
Objectives of Model Fitting: Inference vs. Prediction11 分
Plotting Predictions and Prediction Uncertainty8 分
Python Statistics Landscape2 分
6件の学習用教材
Course Syllabus5 分
Meet the Course Team!10 分
Help Us Learn More About You!10 分
About Our Datasets2 分
Mixed effects models: Is it time to go Bayesian by default?15 分
Python Statistics Landscape1 分
1の練習問題
Week 1 Assessment15 分
5時間で修了
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.
5時間で修了
6件のビデオ （合計85分）, 4 readings, 3 quizzes
6件のビデオ
Linear Regression Inference15 分
Interview: Causation vs Correlation18 分
Logistic Regression Introduction15 分
Logistic Regression Inference7 分
NHANES Case Study Tutorial (Linear and Logistic Regression)17 分
4件の学習用教材
Linear Regression Models: Notation, Parameters, Estimation Methods30 分
Try It Out: Continuous Data Scatterplot App15 分
Importance of Data Visualization: The Datasaurus Dozen10 分
Logistic Regression Models: Notation, Parameters, Estimation Methods30 分
3の練習問題
Linear Regression Quiz20 分
Logistic Regression Quiz15 分
Week 2 Python Assessment20 分
4時間で修了
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.
4時間で修了
8件のビデオ （合計121分）, 2 readings, 2 quizzes
8件のビデオ
Multilevel Linear Regression Models21 分
Multilevel Logistic Regression models14 分
Practice with Multilevel Modeling: The Cal Poly App12 分
What are Marginal Models and Why Do We Fit Them?13 分
Marginal Linear Regression Models19 分
Marginal Logistic Regression11 分
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10 分
2件の学習用教材
Visualizing Multilevel Models10 分
Likelihood Ratio Tests for Fixed Effects and Variance Components10 分
2の練習問題
Name That Model15 分
Week 3 Python Assessment20 分
3時間で修了
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.
3時間で修了
6件のビデオ （合計105分）, 3 readings, 1 quiz
6件のビデオ
Bayesian Approaches to Statistics and Modeling15 分
Bayesian Approaches Case Study: Part I13 分
Bayesian Approaches Case Study: Part II19 分
Bayesian Approaches Case Study - Part III23 分
Bayesian in Python19 分
3件の学習用教材
Other Types of Dependent Variables20 分
Optional: A Visual Introduction to Machine Learning20 分
Course Feedback10 分
1の練習問題
Week 4 Python Assessment20 分
レビュー
FITTING STATISTICAL MODELS TO DATA WITH PYTHON からの人気レビュー
BSにより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.
SMにより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.
AFにより2019年3月11日
The course is actually pretty good, however the mix between basic subjects (like univariate linear regression) and relatively advanced topics (marginal models) may discourage some students.
JJにより2020年5月23日
Very informative. But had few confusions in the last course. Also the python code explanations were not good as the instructor was rushing through it without explaining.
Statistics with Python専門講座について
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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いつ講座や課題にアクセスできるようになりますか？
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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