Tutoring statistics, definitions are of interest. The tutor mentions endogeneity. endogeneity: when an explanatory variable affects the error term in a predictable way. An example would be a model that measures the amount of daily sunlight received, but assumes (wrongly) …
Self-tutoring about statistics: the tutor mentions rarefaction. I’ve seen the terms rarefy, rarefied, rarefaction, etc, but never knew what they meant. Today I decided to change that, but it was like opening an overfilled closet where everything jumps out at …
Tutoring statistics, so many inroads are available. The tutor continues about the base rate fallacy. Continuing from my post yesterday, I’ll explore a connection to it: believing what you see, vs not. The base rate fallacy means that people believe …
Statistics: base rate fallacy, part 2: belief vs disbelief Read more →
Tutoring statistics, you encounter day-to-day ideas. The tutor mentions the false-positive paradox. false-positive paradox: In a population where condition A is extremely uncommon, most tests for it that yield “positive” may likely be false. The false-positive paradox is easy to …
Statistics: base rate fallacy, part 1: false-positive paradox Read more →
Tutoring statistics, rules of thumb can be key. The tutor mentions a real-life application of the 68-95-99 rule. With hot, dry weather comes also the job of irrigation, for those who choose to do so. My wife wants a nice …
Tutoring statistics, you might imagine everyday situations. The tutor brings up one. Let’s imagine we have two mile runners. Runner 1, called R1, has mean time 4:45, with standard deviation 10s; R2 has mean time 5:00 with standard deviation 12s. …
Statistics: how often can something “better” be expected to perform better? Read more →
Tutoring stats, you deal with p-values. The tutor shows an Excel connection. In my posts here and here I mention p-values. Example: Using Excel, get a two-tailed p-value for z=2.4 Solution: Using symmetry, it’s best to get the cumulative z-probability …
Tutoring statistics, distributions are of constant interest. The tutor brings up ten points about the chi-square distribution. The chi-square distribution may not be discussed much in a first-level stats course. It’s used to estimate or evaluate variance, rather than central …
Statistics: Ten facts about the chi-square distribution Read more →
Tutoring statistics, linear regression is perennial. The tutor mentions an assumption it includes. When appropriate, linear regression models data by the equation y = a + bx + e, e being an error term due to variability. An inherent assumption …
Statistics: an assumption of the linear regression model Read more →
Tutoring statistics, mean is used more than median. The tutor points out a reason why. In my post from November 30, 2014, I write about why the median might be preferred to the mean in some cases. For instance, the …
Estimation of expected value: why sample mean is usually preferred over median Read more →