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Statistics with R

Mine Çetinkaya-Rundel, David Banks, Colin Rundel, Merlise A Clyde, Mine Ã‡etinkaya-Rundel, and Colin Rundel

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.

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What's inside

Four courses

Linear Regression and Modeling

(0 hours)

This course introduces simple and multiple linear regression models. These models let you assess relationships between variables in a data set and a continuous response variable. Using the free statistical software R and RStudio, you will learn to fit, examine, and utilize regression models to examine relationships between multiple variables.

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Bayesian Statistics

(0 hours)

This course introduces Bayesian statistics, where inferences are updated as evidence accumulates. You will learn Bayes' rule, the Bayesian paradigm, and practical applications in R. We assume knowledge from the earlier courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."

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Inferential Statistics

(0 hours)

This course covers statistical inference methods for numerical and categorical data. You will learn to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis. Using R and RStudio, you will learn to report estimates of quantities and express the uncertainty of the quantity of interest.

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Introduction to Probability and Data with R

(0 hours)

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.

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Time

162 hours

Via

Coursera

Institution

Duke University

Instructors

Mine Çetinkaya-Rundel

David Banks

Colin Rundel

Merlise A Clyde

Mine Ã‡etinkaya-Rundel

Colin Rundel

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