This comprehensive course, "Inferenzstatistik," offered by Duke University, delves into the fundamental statistical inference methods for analyzing numerical and categorical data. Through a series of engaging and practical modules, participants will gain a deep understanding of hypothesis testing, interpretation of p-values, and reporting analysis results. The course emphasizes the utilization of R and RStudio for lab exercises and a final project, providing hands-on experience in statistical software and practical data analysis. Learners will become proficient in setting up hypothesis tests, interpreting p-values, and effectively reporting analysis outcomes for both numerical and categorical data.
Throughout the course, learners will explore practical tools for data analysis and develop the necessary skills to interpret and report results accurately. The modules cover a wide range of topics, including the central limit theorem, confidence intervals, inference and significance, comparison of means, inference for proportions, and culminates in a data analysis project. By the end of the course, participants will have a strong foundation in statistical inference methods and be capable of applying these skills in real-world scenarios.
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The course modules in "Inferenzstatistik" cover a wide array of topics, including the central limit theorem, confidence intervals, inference and significance, comparison of means, inference for proportions, and culminates in a data analysis project.
This module provides an overview of the course specialization and its objectives, setting the stage for an in-depth exploration of statistical inference methods.
Participants will delve into the central limit theorem and confidence intervals, gaining a comprehensive understanding of sample variability and its significance in statistical inference.
This module delves into the concepts of inference and significance, covering topics such as hypothesis tests, decision errors, and the practical significance of statistical findings.
Participants will explore inference for comparing means, including t-distribution, variance analysis, and bootstrapping, providing a comprehensive understanding of comparing and analyzing mean values.
This module focuses on inference for proportions, covering topics such as confidence intervals, hypothesis testing, and the comparison of proportions, providing essential skills in analyzing and interpreting categorical data.
The final module entails a comprehensive data analysis project, allowing participants to apply their newly acquired skills to a real-world scenario, solidifying their proficiency in statistical inference methods.
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