Statistics and Data Analysis with R

University of Colorado Boulder via Coursera

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Overview

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This course is the third course in a 3-part specialization entitled "Statistics and Applied Data Analysis." The course is meant for those familiar with statistics but unfamiliar with the programming language R. The purpose of this course is to teach learners how to use the popular open-source (and thus, free) integrated development environment RStudio to perform basic and complex statistical calculations. After an introduction to basic calculations, vector, matrices, data frames, and how to import data from common file types (.xlsx, .csv, .txt), learners are taught how to solve probability and counting problems in R, followed by discrete and continuous probability distribution calculations, one-sample hypothesis tests, and two-sample hypothesis tests (comparisons). Finally, participants will learn how to create regression models in R and perform analysis of variance (ANOVA). One of the most beneficial aspect of the course are the programming assignments, which are completed online in the R programming language in Jupyter notebooks.

Syllabus

Welcome and Introduction to the R Programming Language

Welcome to "Statistics and Data Analysis with R"! In this week, you will be introduced to R and RStudio and will learn how to install and navigate RStudio. You will then learn how to perform basic calculations, use script files, create and work with vectors and matrices, and install and load add-on packages. Finally, you will learn all about data frames and tibbles, how to import data from external files (.xlsx, .csv, and .txt files), and how to work with built-in and user-defined functions. When you are ready, you must pass the Week 1 Graded Quiz in order to access the Week 2 Starter Files and Cheat Sheet. You will need access to these items in order to complete Module 2. You must also pass Assignment 1, which counts towards the final grade in the course.

Descriptive Statistics and Graphical Presentation of Data

In Week 2, you'll learn how to calculate common descriptive statistics in R, how to calculate conditional statistics, and how to present data in a graphical manner (scatter plots, column plots, and pie plots). You'll also learn how to create boxplots and probability plots in R and how to analyze the normality of the data using the Anderson-Darling statistic. Week 2 has 9 screencasts with many in-video questions to test your understanding of the material and help you learn. The week ends with a hands-on Assignment 2, which you will complete in a Jupyter notebook in the programming language R and that counts towards your final grade in the course. When you are ready, you must pass the Week 2 Graded Quiz in order to access the Week 3 Starter Files and Cheat Sheet. You will need access to these items in order to complete Module 3. Best of luck to you this week! As always, if you have questions or issues, please initiate a discussion thread and either myself or someone else will chime in with some help.

Counting Techniques and Probability Distribution Functions

In Week 3, you'll learn all about probability and counting rules in R, including how to calculate combinations and permutations, how to calculate probabilities associated with common discrete probability distributions (binomial, geometric, negative binomial, hypergeometric, Poisson distributions), and how to calculate probabilities associated with common continuous probability distributions (uniform, normal, T, chi-squared, and F distributions) in R. You will also perform inverse normal distribution calculations and their associated z-values (standardization). Week 3 has 14 screencasts with many in-video questions to test your understanding of the material and help you learn. The week ends with Assignment 3 in which you will perform several calculations in a Jupyter notebook. Assignment 3 counts towards your final grade in the course. When you are ready, you must pass the Week 3 Graded Quiz in order to access the Week 4 Starter Files and Cheat Sheet. You will need access to these items in order to complete Module 4. Best of luck to you this week! As always, if you have questions or issues, please initiate a discussion thread and either myself or someone else will chime in with some help.

One-Sample Hypothesis Testing

In Week 4, you'll learn all about how to calculate one-sample statistics in R. You will begin the week by learning how to calculate confidence and prediction intervals on the mean, variance, and binomial proportion. Then, you will learn how to perform hypothesis tests on the mean, variance, and a binomial proportion. You will also learn how to calculate the power and probability of a type II error in R, which is related to sample size considerations, which you will also explore. Week 4 has 10 screencasts with many in-video questions to test your understanding of the material and help you learn. I encourage you to download and make use of the Week 4 Cheat Sheet (for those who purchase a Course Certificate) as this will help distill the challenging concepts and R functions that are found in this week's material. Week 4 concludes with Assignment 4, which you will complete in the R programming language in a Jupyter notebook and that counts towards your final grade in the course. When you are ready, you must pass the Week 4 Graded Quiz in order to access the Week 5 Starter Files and Cheat Sheet. You will need access to these items in order to complete Module 5. Quiz 4 requires you to perform statistical calculations in R, so be sure to prepare accordingly.

Two-Sample Hypothesis Tests

In Week 5, you'll learn all about two-sample comparisons. You will calculate confidence intervals related to and hypothesis tests involving the comparison of means, comparison of variances, and comparison of binomial proportions. The type of test that is performed depends on whether variance is known or unknown, which you will also explore. Week 5 has 7 screencasts with many in-video questions to test your understanding of the material and help you learn. The week concludes with Assignment 5. When you are ready, you must pass Quiz 5 in order to continue in the course. You will also want to pay close attention to the Week 5 Cheat Sheet (available to learners who purchase a Course Certificate) as this will serve as a great reference for Assignment 5 and Quiz 5. When you are ready, you must pass the Week 5 Graded Quiz in order to access the Week 6 Starter Files and Cheat Sheet. You will need access to these items in order to complete Module 6. Quiz 5 requires you to perform statistical calculations in R, so be sure to prepare accordingly.

Regression and ANOVA

In Week 6, you'll learn all about creating simple linear, polynomial, and multilinear regression models, which basically are mathematical relationships between input variables (regressor variables) and an output variable (response). You will learn how to calculate confidence intervals on and perform hypothesis tests on model parameters and you will learn how to select the best possible regression model from several candidate models using backward elimination. Finally, you will learn how to perform analysis of variance (ANOVA) when you have more than two groups to compare. Week 6 has 9 screencasts with many in-video questions to test your understanding of the material and help you learn. The week concludes with Assignment 6. When you are ready, you must pass Quiz 6 in order to continue in the course. You will also want to pay close attention to the Week 6 Cheat Sheet (available to learners who purchase a Course Certificate) as this will serve as a great reference for Assignment 6 and Quiz 6. Quiz 6 requires you to perform statistical calculations in R, so be sure to prepare accordingly. Once you've completed Week 6, you'll be done with the course!