The amount of quality free online education that exists today is truly staggering. Sites have popped up all over including ITunes-U, Coursera, Khan Academy, MITx, and so many more. Most of these classes are taught by full University Professors, complete with homework, quizzes, midterms, finals and books that you either purchase or in some cases are available free online.

I was recently drawn to one such course taught by Professor Andrew Conway of Princeton and offered through Coursera. This was the inauguralsemester for Statistics One. Statistics is like the new language of our time. If you don’t understand it, you can hardly comprehend the mix of propaganda, “facts”, and information overload being twisted and perverted by the media and others. More importantly, if you don’t understand it, you can’t make use of all the data being gathered and available out there.

The approach of Statistics One was very non-traditional as far as Statistics courses go. Professor Conway is a Psychology professor. His approach to teaching Statistics is very much an applied approach. He does take the time to explain all of the key details of Statistics such as the Central Limit Theory, but does so in a way that is much more practical than mathematical. This allows much more time to go over the “why” behind why you would want to use the many methods presented as opposed to the “how”.

In today’s world, there is very little reason to work out a full regression analysis of a data frame by hand. Everything from Microsoft Excel, Minitab, SAS, R and more is available to do most of this for you. In Statistics One, the weapon of choice was R, a full featured, open-source, free statistical package. The class made heavy use of R. Many people who took the class who did not have a programming background found themselves on unfamiliar territory. Fortunately for me, in addition to having a substantial programming background, I also have used R before in doing elementary data analysis. I chose to use R Studio, which is a more feature rich IDE for the R programming language.

Here is an example of an R program:

# Statistics One, Lecture 6, example script

# Read data, plot histograms, get descriptives, examine scatterplots, run correlations

cpairs(impact, impact.o, panel.colors = impact.col, gap = .5,rder.single(impact.r)

- Correlation and Measurement
- Mediation andModeration
- Regression and Hypothesis Testing
- Student’s t-test and Analysis of Variance (ANOVA)
- Factoral ANOVA and Model Comparisons

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Learning R Online with Professor Andrew Conway of Princeton