Anchors
| Anchor text | Ref. domains ▾ | Top DR | Ref. pages | Links to target | Dofollow links |
|---|---|---|---|---|---|
| Philip Stark | 3 | — | 0 | 3 | 3 100% |
| https://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf | 3 | — | 0 | 3 | 3 100% |
| Statistics Department | 2 | — | 0 | 3 | 3 100% |
| Statistics | 2 | — | 0 | 50 | 50 100% |
| http://statistics.berkeley.edu/computing/r-bootcamp | 1 | — | 0 | 1 | 1 100% |
| non-parametric | 1 | — | 0 | 1 | 1 100% |
| A thoughtful collection of links. | 1 | — | 0 | 1 | 1 100% |
| Alice Cima | 1 | — | 0 | 1 | 1 100% |
| Faculty Site | 1 | — | 0 | 1 | 1 100% |
| “Arcing The Edge” | 1 | — | 0 | 1 | 1 100% |
| Using Random Forest to Learn Imbalanced Data | 1 | — | 0 | 1 | 1 100% |
| Line and Michel Loève International Prize in Probability | 1 | — | 0 | 1 | 1 100% |
| UC Berkeley | 1 | — | 0 | 1 | 1 100% |
| David Freedman, UC Berkeley | 1 | — | 0 | 1 | 0 0% |
| merge multiple PDF documents without Adobe | 1 | — | 0 | 1 | 0 0% |
| (Web site) | 1 | — | 0 | 1 | 1 100% |
| Berkeley Statistics icon | 1 | — | 0 | 8 | 8 100% |
| Peng Ding | 1 | — | 0 | 1 | 1 100% |
| Breinman et al., Using Random Forest to Learn Imbalanced Data | 1 | — | 0 | 1 | 1 100% |
| générer votre propre paire de clés SSH | 1 | — | 0 | 1 | 1 100% |
| UC Berkeley Statistics Department | 1 | — | 0 | 8 | 8 100% |
| Associate Professor Fernando Pérez | 1 | — | 0 | 2 | 2 100% |
| Privacy | 1 | — | 0 | 4 | 4 100% |
| Michael I. Jordan named Frontiers of Knowledge Laureate | 1 | — | 0 | 189 | 189 100% |
| Reading Web Pages with R | 1 | — | 0 | 1 | 0 0% |
| UC Berkeley, 125 Li Ka Shing Center | 1 | — | 0 | 1 | 1 100% |
| Neyman Seminar | 1 | — | 0 | 1 | 1 100% |
| http://statistics.berkeley.edu/~stark/Vote/#papers | 1 | — | 0 | 1 | 1 100% |
| Using C++, calling C++ from R, and Creating R packages | 1 | — | 0 | 1 | 0 0% |
| Philip B Stark | 1 | — | 0 | 1 | 1 100% |
| Philip B. Stark, Papers and Talks on Voting and Election Auditing | 1 | — | 0 | 1 | 1 100% |
| Visit website | 1 | — | 0 | 1 | 1 100% |
| Berkeley Statistics | 1 | — | 0 | 1 | 1 100% |
| Bagging predictors (Breiman) | 1 | — | 0 | 1 | 1 100% |
| Ryan Tibshirani | 1 | — | 0 | 1 | 1 100% |
| T-test | 1 | — | 0 | 1 | 1 100% |
| statistics | 1 | — | 0 | 1 | 1 100% |
| http://statistics.berkeley.edu/~stark/Seminars/stanford11.pdf | 1 | — | 0 | 1 | 1 100% |
| Read more at: Congratulations to Jason Miller | 1 | — | 0 | 2 | 2 100% |
| statistics.berkeley.edu/~stark | 1 | — | 0 | 1 | 1 100% |
| Statistics department | 1 | — | 0 | 1 | 1 100% |
Frequently Asked Questions
What anchor texts are used to link to statistics.berkeley.edu?
This page shows all anchor texts found in backlinks pointing to statistics.berkeley.edu, sorted by the number of referring domains using each anchor. Anchor texts range from branded terms (like the domain name itself) to keyword-rich phrases that describe the linked content. The distribution of anchor texts reveals how other websites perceive and describe statistics.berkeley.edu.
What is anchor text?
Anchor text is the visible, clickable text in a hyperlink. Search engines use anchor text as a signal to understand what the linked page is about. For example, if many sites link to a page using the anchor text "best running shoes," search engines infer that the page is relevant to that topic. Anchor text appears in several forms: exact-match (contains target keywords), branded (uses the company or domain name), generic (like "click here"), and naked URLs.
Why is anchor text analysis important for SEO?
Anchor text analysis helps identify potential SEO risks and opportunities. A natural backlink profile has diverse anchor texts including branded terms, generic phrases, and topic-relevant keywords. Over-optimization, where too many backlinks use the same exact-match keyword anchor, can trigger search engine penalties. Conversely, understanding which anchors drive the most authority (measured by referring domain count and DR) helps prioritize link building efforts.
How many unique anchor texts does statistics.berkeley.edu have?
The anchor text report for statistics.berkeley.edu displays all distinct anchor texts grouped by their hash. Each row shows how many unique referring domains use that anchor, the total number of links, and the dofollow percentage. A high number of unique anchors generally indicates a healthy, natural backlink profile with diverse link sources.