This course, offered by Duke University, delves into the realm of statistical inference. Through a series of engaging modules, students will gain proficiency in conducting hypothesis tests, interpreting p-values, and reporting analysis results in an understandable manner for clients or the public. The course emphasizes the use of R and RStudio for lab exercises and a final project, providing practical tools for effective data analysis.
The course commences by introducing the Specialization and the course, followed by an exploration of the Central Limit Theorem and Confidence Interval. Subsequent modules focus on Inference and Significance, Inference for Comparing Means, and Inference for Proportions, delving into various aspects such as hypothesis testing, inference for different estimators, comparing means, and proportions. The culmination involves using real-world data examples to reinforce learning and practical application.
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The course comprises modules exploring the Central Limit Theorem and Confidence Interval, Inference and Significance, Inference for Comparing Means, and Inference for Proportions, all emphasizing practical applications and analysis using R and RStudio.
This module introduces the Specialization and the course, setting the stage for a comprehensive exploration of statistical inference methods.
Students will delve into the Central Limit Theorem and Confidence Interval, gaining insight into sampling variability, accuracy vs. precision, and the required sample size for margin of error. Lab exercises using R and RStudio provide hands-on practice.
Building on foundational knowledge, this module delves into Inference and Significance, covering hypothesis testing, decision errors, and the distinction between significance and confidence level. Students will engage in lab exercises and quizzes to reinforce learning.
This module focuses on Inference for Comparing Means, exploring t-distribution, power, ANOVA, and conditions for ANOVA. Practical examples and lab exercises using R and RStudio enhance understanding and application of concepts.
Students will explore Inference for Proportions, delving into confidence interval for proportions, hypothesis testing, small sample proportions, and chi-square tests. The module culminates with a final project involving real-world data analysis and interpretation.
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