About this Course

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Learner Career Outcomes

33%

started a new career after completing these courses

22%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 15 hours to complete
English
Subtitles: English, Korean

Skills you will gain

Statistical InferenceStatistical Hypothesis TestingR Programming

Learner Career Outcomes

33%

started a new career after completing these courses

22%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 15 hours to complete
English
Subtitles: English, Korean

Offered by

Duke University logo

Duke University

Syllabus - What you will learn from this course

Content RatingThumbs Up93%(6,049 ratings)Info
Week
1

Week 1

20 minutes to complete

About the Specialization and the Course

20 minutes to complete
2 readings
2 readings
About Statistics with R Specialization10m
More about Inferential Statistics10m
3 hours to complete

Central Limit Theorem and Confidence Interval

3 hours to complete
7 videos (Total 65 min), 6 readings, 3 quizzes
7 videos
Sampling Variability and CLT20m
CLT (for the mean) examples10m
Confidence Interval (for a mean)11m
Accuracy vs. Precision7m
Required Sample Size for ME4m
CI (for the mean) examples5m
6 readings
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 practice exercises
Week 1 Practice Quiz12m
Week 1 Quiz14m
Week 1 Lab12m
Week
2

Week 2

2 hours to complete

Inference and Significance

2 hours to complete
7 videos (Total 59 min), 5 readings, 3 quizzes
7 videos
Hypothesis Testing (for a mean)14m
HT (for the mean) examples9m
Inference for Other Estimators10m
Decision Errors8m
Significance vs. Confidence Level6m
Statistical vs. Practical Significance7m
5 readings
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 practice exercises
Week 2 Practice Quiz10m
Week 2 Quiz16m
Week 2 Lab12m
Week
3

Week 3

3 hours to complete

Inference for Comparing Means

3 hours to complete
11 videos (Total 84 min), 5 readings, 3 quizzes
11 videos
t-distribution7m
Inference for a mean9m
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 readings
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 practice exercises
Week 3 Practice Quiz16m
Week 3 Quiz28m
Week 3 Lab14m
Week
4

Week 4

4 hours to complete

Inference for Proportions

4 hours to complete
11 videos (Total 118 min), 5 readings, 3 quizzes
11 videos
Sampling Variability and CLT for Proportions15m
Confidence Interval for a Proportion9m
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 readings
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 practice exercises
Week 4 Practice Quiz18m
Week 4 Quiz24m
Week 4 Lab26m

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About the Statistics with R Specialization

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

Frequently Asked Questions

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    When you enroll in a course that is part of a Specialization (which this course is), you will automatically be enrolled in the entire Specialization. You can unenroll from the Specialization if you’re not interested in the other courses or cancel your subscription once you complete the single course.

  • To enroll in an individual course, search for the course title in the catalog.

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    When you enroll in a course that is part of a Specialization, you will automatically be enrolled in the entire Specialization. You can unenroll from the Specialization if you’re not interested in the other courses.

  • No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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