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初級レベル

約26時間で修了

推奨:5 weeks of study, 5-7 hours/week...

英語

字幕:英語

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Statistical InferenceStatistical Hypothesis TestingR Programming

次における5の2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。

初級レベル

約26時間で修了

推奨:5 weeks of study, 5-7 hours/week...

英語

字幕:英語

シラバス - 本コースの学習内容

1
20分で修了

About the Specialization and the Course

This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Inferential Statistics. Please take several minutes to browse them through. Thanks for joining us in this course!

...
2 readings
2件の学習用教材
About Statistics with R Specialization10 分
More about Inferential Statistics10 分
3時間で修了

Central Limit Theorem and Confidence Interval

Welcome to Inferential Statistics! In this course we will discuss Foundations for Inference. Check out the learning objectives, start watching the videos, and finally work on the quiz and the labs of this week. In addition to videos that introduce new concepts, you will also see a few videos that walk you through application examples related to the week's topics. In the first week we will introduce Central Limit Theorem (CLT) and confidence interval.

...
7件のビデオ (合計65分), 6 readings, 3 quizzes
7件のビデオ
Confidence Interval (for a mean)11 分
Accuracy vs. Precision7 分
Required Sample Size for ME4 分
CI (for the mean) examples5 分
6件の学習用教材
Lesson Learning Objectives10 分
Lesson Learning Objectives10 分
Week 1 Suggested Readings and Practice Exercises10 分
About Lab Choices10 分
Week 1 Lab Instructions (RStudio)10 分
Week 1 Lab Instructions (RStudio Cloud)10 分
3の練習問題
Week 1 Practice Quiz12 分
Week 1 Quiz14 分
Week 1 Lab12 分
2
2時間で修了

Inference and Significance

Welcome to Week Two! This week we will discuss formal hypothesis testing and relate testing procedures back to estimation via confidence intervals. These topics will be introduced within the context of working with a population mean, however we will also give you a brief peek at what's to come in the next two weeks by discussing how the methods we're learning can be extended to other estimators. We will also discuss crucial considerations like decision errors and statistical vs. practical significance. The labs for this week will illustrate concepts of sampling distributions and confidence levels.

...
7件のビデオ (合計59分), 5 readings, 3 quizzes
7件のビデオ
Inference for Other Estimators10 分
Decision Errors8 分
Significance vs. Confidence Level6 分
Statistical vs. Practical Significance7 分
5件の学習用教材
Lesson Learning Objectives10 分
Lesson Learning Objectives10 分
Week 2 Suggested Readings and Practice Exercises10 分
Week 2 Lab Instructions (RStudio)10 分
Week 2 Lab Instructions (RStudio Cloud)10 分
3の練習問題
Week 2 Practice Quiz10 分
Week 2 Quiz16 分
Week 2 Lab12 分
3
3時間で修了

Inference for Comparing Means

Welcome to Week Three of the course! This week we will introduce the t-distribution and comparing means as well as a simulation based method for creating a confidence interval: bootstrapping. If you have questions or discussions, please use this week's forum to ask/discuss with peers.

...
11件のビデオ (合計84分), 5 readings, 3 quizzes
11件のビデオ
Inference for comparing two independent means8 分
Inference for comparing two paired means9 分
Power11 分
Comparing more than two means6 分
ANOVA9 分
Conditions for ANOVA2 分
Multiple comparisons6 分
Bootstrapping8 分
5件の学習用教材
Lesson Learning Objectives10 分
Lesson Learning Objectives10 分
Week 3 Suggested Readings and Practice Exercises10 分
Week 3 Lab Instructions (RStudio)10 分
Week 3 Lab Instructions (RStudio Cloud)10 分
3の練習問題
Week 3 Practice Quiz16 分
Week 3 Quiz28 分
Week 3 Lab14 分
4
4時間で修了

Inference for Proportions

Welcome to Week Four of our course! In this unit, we’ll discuss inference for categorical data. We use methods introduced this week to answer questions like “What proportion of the American public approves of the job of the Supreme Court is doing?”.

...
11件のビデオ (合計118分), 5 readings, 3 quizzes
11件のビデオ
Hypothesis Test for a Proportion9 分
Estimating the Difference Between Two Proportions17 分
Hypothesis Test for Comparing Two Proportions13 分
Small Sample Proportions10 分
Examples4 分
Comparing Two Small Sample Proportions5 分
Chi-Square GOF Test14 分
The Chi-Square Independence Test11 分
5件の学習用教材
Lesson Learning Objectives10 分
Lesson Learning Objectives10 分
Week 4 Suggested Readings and Practice Exercises10 分
Week 4 Lab Instructions (RStudio)10 分
Week 4 Lab Instructions (RStudio Cloud)10 分
3の練習問題
Week 4 Practice Quiz18 分
Week 4 Quiz24 分
Week 4 Lab26 分
4.8
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Inferential Statistics からの人気レビュー

by MNMar 1st 2017

Great course. If you put in a little effort, you will come out with a lot of new knowledge. I recommend using the book after you have seen the movies. It gives a deeper picture of how it works. Great!

by ZCAug 24th 2017

This course by Professor Çetinkaya-Rundel is awesome because it is taught in a very clear and vivid way. Lab section and forum are so dope that I love them so much! Definitely strong recommendation!!!

講師

Avatar

Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science

デューク大学(Duke University)について

Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world....

Statistics with Rの専門講座について

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions....
Statistics with R

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