DISCLAIMERThe postings on this blog are my own (except as noted) and do not necessarily represent the positions, strategies or opinions of my current, past, and future employers, cats and other family members, relatives, Facebook friends, real friends, Charlie Sheen, people who sit next to me on public transportation, or myself when I’m in my right mind.
About picturesI decided to start using other peoples' pictures of cats for my blogs for a variety of reasons. It's hard enough for me to get a good picture of my cats let alone one that might go with what I'm writing. I also thought it would improve my blogs by having a much greater variety of images to choose from. I understand enough about creativity and art and photography to know they are both a talent and a skill that should be recognized. I want to give proper attribution to the creators of the images I use in my blogs, but there is a problem. Virtually every image I want to use appears in more than one place on the Internet. I thought using tineye.com, a search site for finding URLs of uploaded images, would help. In fact, I found the opposite. Some of the images I've searched for are found on a hundred different sites, making it impossible to identify the original. So, if I can't identify the original, I'll cite the site I got the image from or if it's an image I don't have a URL for, I'll cite the site that tineye.com indicates has the image that most closely matches the image I use. If I use an image that you created and I didn’t give you credit, I'm sorry. Let me know and I’ll fix the citation or remove the image.
Tag Archives: precision
Five Common Reasons for Doubting a Regression Model Finding a model that fits a set of data is one of the most common goals in data analysis. Least squares regression is the most commonly used tool for achieving this goal. … Continue reading
An aphorism is a pithy saying that reveals some astute observation or popular notion, whether true or fictitious. “Lies, damn lies, and statistics” you’ve undoubtedly heard. If you’ve taken Stats 101, you probably know that “correlation doesn’t imply causation.” Here … Continue reading
Scientists and other theory-driven data analysts focus on eliminating bias and maximizing accuracy so they can find trends and patterns in their data. That’s necessary for any type of data analysis. For statisticians, though, the real enemy in the battle … Continue reading
A confidence interval is the numerical interval around the mean of a sample from a population that has a certain confidence of including the mean of the entire population. “Say what?” OK, let’s take it one point at a time. … Continue reading
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
If you’ve never created a statistical model before, you might be surprised to find that the process involves a lot more than statistics. It’s like traveling. You don’t start by thinking about your transport, the plane, train, or bus you … Continue reading
1. Where’s the Beef? In a way, the worst flaw a data analysis can have is no analysis at all. Instead, you get data lists, sorts and queries, and maybe some simple descriptive statistics but nothing that addresses objectives, answers … Continue reading
You can’t understand your data unless you control extraneous variance attributable to the way you select samples, the way you measure variable values, and any influences of the environment in which you are working. Using the concepts of reference, replication … Continue reading
If you can measure a phenomenon, you can analyze the phenomenon. But if you don’t measure the phenomenon accurately and precisely, you won’t be able to analyze the phenomenon accurately and precisely. So in planning a statistical analysis, once you … Continue reading
You can’t understand data without controlling the variance. You can’t control variance without understanding the data. Variance Doesn’t Go Away By Ignoring It In an ideal universe, your dataset would contain no bias and only the natural variability you want … Continue reading