If you are like I was for a long time, you have avoided learning R.
You’ve probably heard that there’s a steep learning curve. Or noticed that the available documentation is not necessarily user-friendly.
Frankly, both things are true, to some extent.
The best and worst thing about R is that it is open-source. So there is no single (more…)
Like many people with graduate degrees, I have used a number of statistical software packages over the years.
Through work and school I have used Eviews, SAS, SPSS, R, and Stata.
Some were more difficult to use than others but if you used them often enough you would become proficient to take on the task at hand (though some packages required greater usage of George Carlin’s 7 dirty words).
There was always one caveat which determined which package I used.
In Part 7, let’s look at further plotting in R. Try entering the following three commands together (the semi-colon allows you to place several commands on the same line).
Let’s take an example with two variables and enhance it.
X <- c(3, 4, 6, 6, 7, 8, 9, 12)
B1 <- c(4, 5, 6, 7, 17, 18, 19, 22)
B2 <- c(3, 5, 8, 10, 19, 21, 22, 26)
In Part 6, let’s look at basic plotting in R. Try entering the following three commands together (the semi-colon allows you to place several commands on the same line).
x <- seq(-4, 4, 0.2) ; y <- 2*x^2 + 4*x - 7
plot(x, y) (more…)
Like any applied skill, mastering statistical analysis requires:
1. building a body of knowledge
2. adeptness of the tools of the trade (aka software package)
3. practice applying the knowledge and using the tools in a realistic, meaningful context.
If you think of other high-level skills you’ve mastered in your life–teaching, survey design, programming, sailing, landscaping, anything–you’ll realize the same three requirements apply.
These three requirements need to be developed over time–over many years to attain mastery. And they need to be developed together. Having more background knowledge improves understanding of how the tools work, and helps the practice go better. Likewise, practice in a real context (not perfect textbook examples) makes the knowledge make more sense, and improves skills with the tools.
I don’t know if this is true of other applied skills, but from what I’ve seen over many years of working with researchers as they master statistical analysis, the journey seems to have 3 stages. Within each stage, developing all 3 requirements–knowledge, tools, and experience–to a level of mastery sets you up well for the next stage. (more…)
In addition to the five listed in this title, there are quite a few other options, so how do you choose which statistical software to use?
The default is to use whatever software they used in your statistics class–at least you know the basics.
And this might turn out pretty well, but chances are it will fail you at some point. Many times the stat package used in a class is chosen for its shallow learning curve, (more…)