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.
Overview
Syllabus
Course 1: Introduction to Probability and Data with R
- Offered by Duke University. This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. ... Enroll for free.
Course 2: Inferential Statistics
- Offered by Duke University. This course covers commonly used statistical inference methods for numerical and categorical data. You will ... Enroll for free.
Course 3: Linear Regression and Modeling
- Offered by Duke University. This course introduces simple and multiple linear regression models. These models allow you to assess the ... Enroll for free.
- Offered by Duke University. This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. ... Enroll for free.
Course 2: Inferential Statistics
- Offered by Duke University. This course covers commonly used statistical inference methods for numerical and categorical data. You will ... Enroll for free.
Course 3: Linear Regression and Modeling
- Offered by Duke University. This course introduces simple and multiple linear regression models. These models allow you to assess the ... Enroll for free.
Courses
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This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.
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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. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
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This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
Taught by
Mine Çetinkaya-Rundel
