Page 31 - Intermediate Statistics for Dummies

05_045206 ch01.qxd 2/1/07 9:41 AM Page 10
10
Part I: Data Analysis and Model-Building Basics
then were very complicated to use, requiring a great deal of knowledge about
statistics to set up and carry out. The calculations were tedious and at times
unpredictable and required a thorough understanding of the theories and
methods behind the calculations to get correct and reliable answers.
Today, anyone who wants to analyze data can do it easily. Many user-friendly
statistical software packages are made expressly for that purpose — Microsoft
Excel, Minitab, SAS, and SPSS, just to name a few. Free online programs are
even available, such as Stat Crunch, to help you do just what it says — crunch
your numbers and get an answer. As you see in this section, the modern
easy-to-use statistical packages are good in some ways, and not-so-good in
other ways.
The most important idea when applying statistical techniques to analyze data
is to know what’s going on behind the number crunching, so you (not the
computer) are in control of the analysis. That’s why knowledge of intermedi-
ate statistics is so critical.
Remembering the old days
In the old days, in order to determine whether methods gave different
results, you had to write a computer program to do it, using code that you
had to take a class to learn. You had to type in your data in a specific way
that the computer program demanded, and you had to submit your program
to a mainframe computer and wait for the printer to print out your results.
This method was time consuming and a general all-around pain.
I remember the day in college when I reached bottom. I was just learning to
write those sophisticated programs you needed to do the simplest analysis.
No matter how hard I tried to write the perfect program, the computer kept
spitting my work back at me without doing my analysis, noting error after
error in the way I typed the commands. The last straw came when I gave my
program to the computer for the umpteenth time: At the end of the printout,
the computer told me on the very last line: “Error #34410: Too many errors.”
Now, don’t get the idea that your author doesn’t know what she’s doing. I had
all the statistical methods right; I just wasn’t very good at writing computer
programs. So for anyone out there who’s ever been frustrated by a computer,
I feel your pain, and I try to minimize your troubles throughout this book.
Enough lamenting about having to walk to school uphill both ways in the
snow with plastic bags on my feet instead of boots. The point is, statistical
software packages have undergone an incredible evolution in the last 10 to 15
years, to the point where you can now enter your data quickly and easily in
almost any format. Moreover, the choices for data analysis are well organized