このコースについて
13,715 最近の表示

次における3の3コース

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。

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12時間で修了

推奨:4 weeks; 4-6 hours/week...

英語

字幕:英語, 韓国語

習得するスキル

Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression

次における3の3コース

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。

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12時間で修了

推奨:4 weeks; 4-6 hours/week...

英語

字幕:英語, 韓国語

シラバス - 本コースの学習内容

1
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.

...
7件のビデオ (合計67分), 6 readings, 1 quiz
7件のビデオ
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
2
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.

...
6件のビデオ (合計85分), 4 readings, 3 quizzes
6件のビデオ
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
3
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.

...
8件のビデオ (合計121分), 2 readings, 2 quizzes
8件のビデオ
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
4
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.

...
6件のビデオ (合計105分), 3 readings, 1 quiz
6件のビデオ
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
4.2
12件のレビューChevron Right

Fitting Statistical Models to Data with Python からの人気レビュー

by 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.

by JXJun 30th 2019

Really thorough and in-depth material about statistical models with python.

講師

Avatar

Brenda Gunderson

Lecturer IV and Research Fellow
Department of Statistics
Avatar

Brady T. West

Research Associate Professor
Institute for Social Research
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Kerby Shedden

Professor
Department of Statistics

ミシガン大学(University of Michigan)について

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の専門講座について

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....
Statistics with Python

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