The importance of completeness of linear regressions is an often-discussed issue. By leaving out relevant variables the coefficients might be inconsistent.
But why on earth?!
Assuming a linear complete model of the form:
z = a + bx + cy + ε.
Where z is supposed to be dependent, x and y are independent and ε is the error term.
Now we drop y to check…Continue
Added by Frank Raulf on November 13, 2019 at 2:00am — No Comments
StackOverflow’s annual developer survey concluded earlier this year, and they have graciously published the (anonymized) 2019 results for analysis. They’re a rich view into the experience of software developers around the world — what’s their favorite editor? how many years of experience? tabs or spaces? and crucially, salary. Software engineers’ salaries are good, and sometimes both eye-watering and news-worthy.
The tech industry is also painfully aware that it does not always live…Continue
Added by Sean Owen on August 8, 2019 at 8:00am — No Comments
Summary: There is a great hue and cry about the danger of bias in our predictive models when applied to high significance events like who gets a loan, insurance, a good school assignment, or bail. It’s not as simple as it seems and here we try to take a more nuanced look. The result is not as threatening as many headlines make it seem.
Added by William Vorhies on June 5, 2018 at 8:00am — No Comments
Summary: Flawed data analysis leads to faulty conclusions and bad business outcomes. Beware of these seven types of bias that commonly challenge organizations' ability to make smart decisions.
This is a great article by Lisa Morgan originally published on InformationWeek.com. See the original article…Continue
Data scientists must always remember that data sets are not objective - they are selected, collected, filtered, structured and analyzed by human design. Naked and hidden biases in selecting,…Continue
Added by Michael Walker on October 7, 2013 at 9:14pm — No Comments