Writing , , and , the fitted normal linear model is
The R formula lstat * age includes both main effects and their interaction term.
Three notable features are:
  • The fitted model is highly significant overall: its statistic is on degrees of freedom with . Its , however, means that about of the observed response variation remains unexplained, and the residual standard deviation is about house-value units.
  • At fixed age, the fitted slope with respect to lstat is
    The large negative main coefficient is strongly significant. The positive interaction is significant at the level (), indicating that this negative association becomes slightly weaker for older housing tracts.
  • The age main-effect estimate is essentially zero with , but it is the age effect specifically at and should not be interpreted separately from the significant interaction. The diagnostic plots also show curved residual structure, increasing spread, a heavy upper tail with observations 215, 372, and 373, and substantial influence from observation 215. These features cast doubt on linearity, constant variance, and Gaussian residual assumptions.
A context-free grammar is in Chomsky normal form when every production has one of the forms
where are nonterminals and is a terminal. One may additionally allow when the empty word belongs to the language, usually with the restriction that the start symbol does not occur on a right-hand side.
An -production has the form . A unit production has the form for nonterminals .
In , the productions for give
Consequently
In , and generate and , while again gives
Since , the two start productions generate exactly
Thus
Every production of has the required binary-nonterminal or single-terminal form, so is the Chomsky-normal-form grammar for the nonempty part of .
A general binary feedback shift register of length has state
and a feedback function . One update outputs the oldest bit, shifts the state, and inserts
The initial fill is . It is a linear-feedback shift register when
for fixed .
The Berlekamp-Massey algorithm reads an intercepted sequence from left to right while maintaining the shortest connection polynomial that reproduces the prefix. At each new symbol it computes the discrepancy between the observed bit and that predicted by the current recurrence. A zero discrepancy leaves the polynomial unchanged; a nonzero discrepancy adds a suitably shifted copy of the connection polynomial saved at the previous increase in linear complexity. After at least twice the unknown register length, it recovers the shortest recurrence, after which the entire keystream can be predicted.
Applying those discrepancy updates to
returns the connection polynomial
Equivalently, the sequence obeys
Indeed this predicts successively . No recurrence of length one or two fits the prefix, so its linear complexity is three.

Pinned article: Introduction to the OurBigBook Project

Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
We have two killer features:
  1. topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculus
    Articles of different users are sorted by upvote within each article page. This feature is a bit like:
    • a Wikipedia where each user can have their own version of each article
    • a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
    This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
    Figure 1.
    Screenshot of the "Derivative" topic page
    . View it live at: ourbigbook.com/go/topic/derivative
  2. local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:
    This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
    Figure 5. . You can also edit articles on the Web editor without installing anything locally.
    Video 3.
    Edit locally and publish demo
    . Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.
  3. https://raw.githubusercontent.com/ourbigbook/ourbigbook-media/master/feature/x/hilbert-space-arrow.png
  4. Infinitely deep tables of contents:
    Figure 6.
    Dynamic article tree with infinitely deep table of contents
    .
    Descendant pages can also show up as toplevel e.g.: ourbigbook.com/cirosantilli/chordate-subclade
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact