About this Course
최근 조회 13,082

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

Completion of the first two courses in this specialization; high school-level algebra

완료하는 데 약 12시간 필요

권장: 4 weeks; 4-6 hours/week...

영어

자막: 영어, 한국어

귀하가 습득할 기술

Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

Completion of the first two courses in this specialization; high school-level algebra

완료하는 데 약 12시간 필요

권장: 4 weeks; 4-6 hours/week...

영어

자막: 영어, 한국어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 3시간 필요

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.

...
7 videos (Total 67 min), 6 readings, 1 quiz
7개의 동영상
What Do We Mean by Fitting Models to Data'?18m
Types of Variables in Statistical Modeling13m
Different Study Designs Generate Different Types of Data: Implications for Modeling9m
Objectives of Model Fitting: Inference vs. Prediction11m
Plotting Predictions and Prediction Uncertainty8m
Python Statistics Landscape2m
6개의 읽기 자료
Course Syllabus5m
Meet the Course Team!10m
Help Us Learn More About You!10m
About Our Datasets2m
Mixed effects models: Is it time to go Bayesian by default?15m
Python Statistics Landscape1m
1개 연습문제
Week 1 Assessment15m
2
완료하는 데 5시간 필요

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.

...
6 videos (Total 85 min), 4 readings, 3 quizzes
6개의 동영상
Linear Regression Inference15m
Interview: Causation vs Correlation18m
Logistic Regression Introduction15m
Logistic Regression Inference7m
NHANES Case Study Tutorial (Linear and Logistic Regression)17m
4개의 읽기 자료
Linear Regression Models: Notation, Parameters, Estimation Methods30m
Try It Out: Continuous Data Scatterplot App15m
Importance of Data Visualization: The Datasaurus Dozen10m
Logistic Regression Models: Notation, Parameters, Estimation Methods30m
3개 연습문제
Linear Regression Quiz20m
Logistic Regression Quiz15m
Week 2 Python Assessment20m
3
완료하는 데 4시간 필요

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.

...
8 videos (Total 121 min), 2 readings, 2 quizzes
8개의 동영상
Multilevel Linear Regression Models21m
Multilevel Logistic Regression models14m
Practice with Multilevel Modeling: The Cal Poly App12m
What are Marginal Models and Why Do We Fit Them?13m
Marginal Linear Regression Models19m
Marginal Logistic Regression11m
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10m
2개의 읽기 자료
Visualizing Multilevel Models10m
Likelihood Ratio Tests for Fixed Effects and Variance Components10m
2개 연습문제
Name That Model15m
Week 3 Python Assessment20m
4
완료하는 데 3시간 필요

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.

...
6 videos (Total 105 min), 3 readings, 1 quiz
6개의 동영상
Bayesian Approaches to Statistics and Modeling15m
Bayesian Approaches Case Study: Part I13m
Bayesian Approaches Case Study: Part II19m
Bayesian Approaches Case Study - Part III23m
Bayesian in Python19m
3개의 읽기 자료
Other Types of Dependent Variables20m
Optional: A Visual Introduction to Machine Learning20m
Course Feedback10m
1개 연습문제
Week 4 Python Assessment20m
4.1
10개의 리뷰Chevron Right

Fitting Statistical Models to Data with Python의 최상위 리뷰

대학: AFMar 12th 2019

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.

대학: NGMay 25th 2019

Very informative and the example\n\napplications are extremely detailed

강사

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Brenda Gunderson

Lecturer IV and Research Fellow
Department of Statistics
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Brady T. West

Research Associate Professor
Institute for Social Research
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Kerby Shedden

Professor
Department of Statistics

미시건 대학교 정보

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....

Statistics with Python 전문 분야 정보

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....
Statistics with Python

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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