\nDegrees of freedom (df) is a concern in calculating statistics, eg., the t or Chi-squared, since the higher the df, the more precisely the data can describe its corresponding population. One gets more df from more data points; hence, the reason more data is generally seen as better.<\/p>\n
\nYet, explaining degrees of freedom and its importance may sometimes get less priority than it deserves. Academia to the rescue: today I watched a video by Quant Psych on YouTube<\/a> which gives a refreshing way to understand degrees of freedom. The source paper, by Joseph Lee Rodgers, is here<\/a>.<\/p>\n \nPut simply, the idea is that every parameter used to model the data, but calculated from the data itself, “costs” a degree of freedom. Therefore, a two-parameter model, such as a regression line (y=a +bx), costs two degrees of freedom, and so on. Yet, it seems a parameter that is wholly calculated from already-calculated parameters won’t cost any df, since it doesn’t derive further information from the data.<\/p>\nJack of Oracle Tutoring by Jack and Diane,<\/a> Campbell River, BC.\n","protected":false},"excerpt":{"rendered":" Self-tutoring about statistics: the tutor mentions some intriguing ideas (and resources they came from) about degrees of freedom. Degrees of freedom (df) is a concern in calculating statistics, eg., the t or Chi-squared, since the higher the df, the more …<\/p>\n