Show me the data! In science, medicine, business, education, journalism, and many other fields, this is the coin of the realm. raw data without interpretation is useless. They mean to show the statistics, well-founded, persuasive data that supports a claim.

One of the most useful feats of applied mathematics is the ability of statistics to extract insights from a random collection of facts. The statistical programming language R, a free, open-source computer language with millions of users worldwide, is all the more accessible because of that power.

In this era of big data, with a solid understanding of statistics and the tools for interpreting data, you don’t have to trust someone else’s analysis of medical treatments, financial returns, crop yields, voting trends, home prices, or any other interpretation of data. You can do it on your own.

Designed for people who like math and want an introduction to an essential toolkit for thinking about the uncertainty inherent in all sorts of information. Learning Statistics: Concepts and Applications The user interface of RStudio makes it even more powerful when combined with the instruction on how to apply elementary statistical methods in R. R and RStudio are free and can be downloaded for multiple platforms.

In 24 challenging and in-depth half-hour lectures, award-winning Professor Talithia Williams of Harvey Mudd College walks you through major concepts of an introductory college-level statistics course, and beyond, using examples developed and presented in R.

Dr. Williams is an Associate Professor of Mathematics and the Associate Dean for Research and Experiential Learning at Harvey Mudd. NOVA Wonders. .

Are you ready for a new approach to statistics?

In a course that repays multiple viewings, Professor Williams presents the most widely-used statistical measures, concepts, and techniques: how and when to use them, what they mean, and how to recognize when arguments or conclusions based on statistical data are suspect or wrong.

Statistics for learning. Those who want to go beyond a beginner’s level will benefit from this. The updated experience of what is going on in the field today and how user access to the R programming language is transforming the everyday practice of statistics is given in this course.

This video-only course has special advantages.

• Statistics concepts combined with R examples : Viewers get a two-for-one combination of thorough grounding in statistical concepts with ground-up demonstrations of how problems are solved with the R programming languge
• A guided tour of R in action : Viewers get a gentle introduction to R in use—from how to download R and RStudio, to importing and exporting data, writing code, and generating plots. All examples in the course are conducted in R.
• Enhanced graphics : On-screen graphics are based on outputs from RStudio, but with frequent enhancements to make the visuals even easier to read and understand.
• Large screen or handheld : The presentation has been optimized for everything from TVs and computers to mobile devices, meaning you can watch it on a handheld device with the same comfort and clarity as on a television screen.
• Links to the R community : When you finish these lectures, you are not on your own. Professor Williams helps you join the worldwide community of R users, who have been advising the novice and expert alike for two decades.

Professor Williams has organized the course so that it can be taken straight through, from elementary statistics to standard and advanced techniques in statistical inference. Students seeking help in specific areas can jump in and out of the course at any point, while those with a background in other statistics software will find the progression very helpful.

There is a powerful set of statistical tools.

Statistics for learning. An overview of the field includes how to calculate and display summaries of data. Professor Williams talks about the advantages of R over other statistical analysis packages. R and RStudio are completely free, unlike other such products. You are running R code before the end of the lecture.

The next six lectures cover descriptive statistics and probability, in which you learn to draw conclusions from a given sample of data by using visual aids such as histograms, scatterplots, and box plots. You can use a variety of probability distributions and graphical analysis techniques. The formulas for these operations as well as the simple R commands that run them automatically are introduced to you.

The power of statistics is explored in lecture 8. You learn how to frame a hypothesis, build a model and deduce a proposition from the data. Simple linear regression, multiple linear regression, ANOVA, and other cornerstone techniques are studied while also using R to run simulations of many different scenarios from the R Datasets Package.

In the last third of the course, you will learn how statisticians go beyond what beginners are taught, developing branches of applied statistics that have spun off to form their own immensely productive specialties. These include:

• Experimental design : While there are many techniques for analyzing data you already have, even more powerful is designing an experiment to decide how data is collected from the start. Consider such elements of good design as blocking, randomization, and replication to ensure that your experiment produces sound statistical results.
• Spatial statistics : Maps have always been information-rich artifacts, but they are now more useful than ever thanks to the advent of GPS-enabled data-gathering devices and powerful computers, combined with a panoply of statistical tools for treating spatial autocorrelation as a rich new source of information.
• Time series analysis : Just as fascinating as spatial data is information collected sequentially over time—in finance, meteorology, biology, agriculture, and other fields. One of the most important goals of time series analysis is forecasting, which extracts short- and longer-term patterns in the data.
• Bayesian inference : Textbook statistics is often based on a “frequentist” paradigm, in which sampling is theoretically unlimited. But for many real-life situations, your information is almost always incomplete, and likely to be revised. This is the forte of Bayesian inference.

A lecture at the end of the course will show you how to combine information in a way that best serves your needs.

Dr. Williams has made it her life’s work to get students, parents, educators, and the community at large excited about mathematics and especially statistics, which she describes as a powerful framework for thinking. When you hear a statistical argument that affects your health, livelihood, or your firmly held beliefs, you should say that Dr. Williams has a gift for demystifying statistics and making it relevant to everyone. You will be able to do that with this course.