Über diesen Kurs
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Empfohlen: 5 weeks of study, 5-7 hours/week...

Englisch

Untertitel: Englisch

Kompetenzen, die Sie erwerben

Statistical InferenceStatistical Hypothesis TestingR Programming

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Anfänger“

Ca. 25 Stunden zum Abschließen

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

Englisch

Untertitel: Englisch

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1
20 Minuten zum Abschließen

About the Specialization and the Course

2 Lektüren
2 Lektüren
About Statistics with R Specialization10m
More about Inferential Statistics10m
3 Stunden zum Abschließen

Central Limit Theorem and Confidence Interval

7 Videos (Gesamt 65 min), 6 Lektüren, 3 Quiz
7 Videos
Confidence Interval (for a mean)11m
Accuracy vs. Precision7m
Required Sample Size for ME4m
CI (for the mean) examples5m
6 Lektüren
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 1 Suggested Readings and Practice Exercises10m
About Lab Choices10m
Week 1 Lab Instructions (RStudio)10m
Week 1 Lab Instructions (RStudio Cloud)10m
3 praktische Übungen
Week 1 Practice Quiz12m
Week 1 Quiz14m
Week 1 Lab12m
Woche
2
2 Stunden zum Abschließen

Inference and Significance

7 Videos (Gesamt 59 min), 5 Lektüren, 3 Quiz
7 Videos
Inference for Other Estimators10m
Decision Errors8m
Significance vs. Confidence Level6m
Statistical vs. Practical Significance7m
5 Lektüren
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 2 Suggested Readings and Practice Exercises10m
Week 2 Lab Instructions (RStudio)10m
Week 2 Lab Instructions (RStudio Cloud)10m
3 praktische Übungen
Week 2 Practice Quiz10m
Week 2 Quiz16m
Week 2 Lab12m
Woche
3
3 Stunden zum Abschließen

Inference for Comparing Means

11 Videos (Gesamt 84 min), 5 Lektüren, 3 Quiz
11 Videos
Inference for comparing two independent means8m
Inference for comparing two paired means9m
Power11m
Comparing more than two means6m
ANOVA9m
Conditions for ANOVA2m
Multiple comparisons6m
Bootstrapping8m
5 Lektüren
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 3 Suggested Readings and Practice Exercises10m
Week 3 Lab Instructions (RStudio)10m
Week 3 Lab Instructions (RStudio Cloud)10m
3 praktische Übungen
Week 3 Practice Quiz16m
Week 3 Quiz28m
Week 3 Lab14m
Woche
4
4 Stunden zum Abschließen

Inference for Proportions

11 Videos (Gesamt 118 min), 5 Lektüren, 3 Quiz
11 Videos
Hypothesis Test for a Proportion9m
Estimating the Difference Between Two Proportions17m
Hypothesis Test for Comparing Two Proportions13m
Small Sample Proportions10m
Examples4m
Comparing Two Small Sample Proportions5m
Chi-Square GOF Test14m
The Chi-Square Independence Test11m
5 Lektüren
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 4 Suggested Readings and Practice Exercises10m
Week 4 Lab Instructions (RStudio)10m
Week 4 Lab Instructions (RStudio Cloud)10m
3 praktische Übungen
Week 4 Practice Quiz18m
Week 4 Quiz24m
Week 4 Lab26m
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Top reviews from Inferential Statistics

von 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!

von 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!!!

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Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science

Über Duke University

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....

Über die Spezialisierung Statistics with R

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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