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Tag Archives: Normal distribution
DARE TO COMPARE – PART 3
Parts 1 and 2 of Dare to Compare summarized fundamental topics about simple statistical comparisons. Part 3 shows how those concepts play a role in conducting statistical tests. The importance of these concept are highlighted in the following table. Test … Continue reading
Dare to Compare – Part 1
In school, you probably had to line up by height now and then. That wasn’t too difficult. There weren’t too many individuals being lined up and they were all in the same place at the same time. An individual’s place … Continue reading
Posted in Uncategorized
Tagged blogs, cats, Normal distribution, population, statistical comparisons, statistical tests
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Five Things You Should Know Before Taking Statistics 101
Of the over two million college degrees that are granted in the U.S. every year, including those earned at accredited online colleges nationwide, probably twothirds require completion of a statistics class. That’s over a million and a half students taking … Continue reading
A Picture Worth 140,000 Words
Even if it’s been a while since your last statistics class, when you read Stats with Cats: The Domesticated Guide to Statistics, Models, Graphs, and Other Breeds of Data Analysis you’ll figure out that there’s much more to data analysis … Continue reading
Grasping at Flaws
Even if you’re not a statistician, you may one day find yourself in the position of reviewing a statistical analysis that was done by someone else. It may be an associate, someone who works for you, or even a competitor. … Continue reading
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Tagged cats, correlation coefficient, criticism, dependent variable, jargon, math, mean, Normal distribution, number of samples, objectives, population, precision, probability, rule of thumb, sample size, samples, software, statistical analysis, statistical tests, statistics, stats with cats, uncertainty, variability
8 Comments
You’re Off to Be a Wizard
The process of developing a statistical model (https://statswithcats.wordpress.com/2010/12/04/manypathsleadtomodels/) involves finding the mathematical equation of a line, curve, or other pattern that faithfully represents the data with the least amount of error (i.e., variability). Variability and pattern are the yin and … Continue reading
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Tagged AIC, BIC, cats, coeffiient of determination, Cook’s Distance, dependent variable, DFBETAs, Ftest, jargon, model, multicollinearity, Normal distribution, probability, regression coefficients, residuals, standard error of estimate, statistical analysis, statistical leverage, statistical tests, statistics, stats with cats, ttest, trend, uncertainty, variability, variance inflation factor
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Fifty Ways to Fix your Data
Fifty Ways to Fix your Data (Sing to the tune of “Fifty Ways to Leave Your Lover” by Paul Simon) The problem is all about your scales, she said to me The Rsquares will be better if you’ve matched ’em … Continue reading
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Tagged BoxCox, cats, correlation coefficient, differencing, information, lags, math, measurement, measurement scales, model, Normal distribution, recoding, rescaling, smoothing, software, standardization, statistical analysis, statistics, stats with cats, transformations, trend
31 Comments
Assuming the Worst
If you’re going to be poking around data looking for patterns and anomalies, you should be aware of the fundamental requirements you need to fulfill, or at least assume you fulfill. Consider this. All models make assumptions, an evil necessity … Continue reading
The Zen of Modeling
What’s the first thing you think of when you hear the word model? The plastic model airplanes you used to build? A fashion model? The model of the car you drive? The person who is your role model? But what … Continue reading
Posted in Uncategorized
Tagged cats, drivers, jargon, math, model, modeling, Normal distribution, population, probability, statistical analysis, statistics, stats with cats, t distribution, uncertainty, workload
16 Comments
30 Samples. Standard, Suggestion, or Superstition?
If you’ve ever taken any applied statistics courses in college, you may have been exposed to the mystique of 30 samples. Too many times I’ve heard statistician doityourselfers tell me that “you need 30 samples for statistical significance.” Maybe that’s … Continue reading