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How Much Data Do You Need?

Introduction A common question in analytics, statistics, and data science projects is: how much data do you need? This question actually has very specific and clear answers! A first good answer is “it is good to have a lot.” Let’s dig deeper and get some additional more detailed quantitative answers. […]

Thinking About Linear Regression

Introduction I want to spend some time thinking out loud about linear regression. As a data science consultant and teacher I spend a lot of time using linear regression and teaching linear regression. I have found each of these pursuits can degenerate into mere doctrine or instructions. “do this,” “expect […]

Can we try to make an adjustment?

In most of our data science teaching (including our book Practical Data Science with R) we emphasize the deliberately easy problem of “exchangeable prediction.” We define exchangeable prediction as: given a series of observations with two distinguished classes of variables/observations denoted “x”s (denoting control variables, independent variables, experimental variables, or […]