Neil McGuigan
• Member for 11 years, 6 months
• Last seen this week

This isn't technically a cartoon, but close enough:

He uses statistics like a drunken man uses a lamp post, more for support than illumination. -- Andrew Lang

Reporting p-values when you did data-mining (hypothesis discovery) instead of statistics (hypothesis testing).

John Tukey for Fast Fourier Transforms, exploratory data analysis (EDA), box plots, projection pursuit, jackknife (along with Quenouille). Coined the words "software" and "bit".

Reverend Thomas Bayes for discovering Bayes' theorem

Carl Gauss for least squares estimation.

Standard deviation is a number that represents the "spread" or "dispersion" of a set of data. There are other measures for spread, such as range and variance. Here are some example sets of data, and ...

"The first time I was in a statistics course, I was there to teach it" John Tukey (link)

Francis Galton for discovering statistical correlation and promoting regression.

A frequency table is a good place to start. You can do the count, and relative frequency for each level. Also, the total count, and number of missing values may be of use. You can also use a ...

Andrey Markov for stochastic processes and markov chains.

Sometimes outliers are bad data, and should be excluded, such as typos. Sometimes they are Wayne Gretzky or Michael Jordan, and should be kept. Outlier detection methods include: Univariate -> ...

Sometimes correlation is enough. For example, in car insurance, male drivers are correlated with more accidents, so insurance companies charge them more. There is no way you could actually test this ...

If you have large data sets - ones that make Excel or Notepad load slowly, then a database is a good way to go. Postgres is open-source and very well-made, and it's easy to connect with JMP, SPSS and ...

There's no need to call it Predictive Analytics :) It already has two names: statistics, and data mining. Beginner Stats Book: Statistics in Plain English Advanced Stats Book: Multivariate Analysis, ...

I recommend these books - they are highly rated on Amazon too: "Text Mining" by Weiss "Text Mining Application Programming", by Konchady For software, I recommend RapidMiner (with the text plugin), ...

It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so. Mark Twain (okay, so he's not a statistician)

If you carved your distribution (histogram) out of wood, and tried to balance it on your finger, the balance point would be the mean, no matter the shape of the distribution. If you put a stick in the ...

Sample size doesn't much depend on the population size, which is counter-intuitive to many. Most polling companies use 400 or 1000 people in their samples. There is a reason for this: A sample size ...

Temperatures are continuous. It can be 23 degrees, 23.1 degrees, 23.100004 degrees. Sex is discrete. You can only be male or female (in classical thinking anyways). Something you could represent ...

My old stats prof had a "rule of thumb" for dealing with outliers: If you see an outlier on your scatterplot, cover it up with your thumb :)

RapidMiner for data and text mining

A principal component is a weighted linear combination of all your factors (X's). example: PC1 = 0.1X1 + 0.3X2 There will be one component for each factor (though in general a small number are ...

There isn't really an answer. It's somewhere between 1 and N. However, you can think about it from a profit perspective. For example, in marketing one uses segmentation, which is much like ...

Here is a big data set. What is your plan for dealing with outliers? How about missing values? How about transformations? Can they deal with real-world data?

W. Edwards Deming for promoting statistical process control

How do you prevent over-fitting when you are creating a statistical model? Good answer: cross-validation

Statistics in Plain English is pretty good. 4.5 on Amazon, 11 reviews. Explains ANOVA pretty well too.