Estimate vs Multicollinearity - What's the difference?

estimate | multicollinearity |


As nouns the difference between estimate and multicollinearity

is that estimate is a rough calculation or guess while multicollinearity is (statistics) a phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, so that the coefficient estimates may change erratically in response to small changes in the model or data.

As a verb estimate

is to calculate roughly, often from imperfect data.

estimate

English

Alternative forms

* (archaic)

Noun

(en noun)
  • A rough calculation or guess.
  • (construction and business) A document (or verbal notification) specifying how much a job will probably cost.
  • * {{quote-book, year=1928, author=Lawrence R. Bourne
  • , title=Well Tackled! , chapter=3 citation , passage=“They know our boats will stand up to their work,” said Willison, “and that counts for a good deal. A low estimate from us doesn't mean scamped work, but just that we want to keep the yard busy over a slack time.”}}

    Synonyms

    * estimation * appraisal

    Derived terms

    * ballpark estimate

    Verb

  • To calculate roughly, often from imperfect data.
  • * {{quote-book, year=1965, author=Ian Hacking, title=Logic of Statistical Inference, passage=I estimate that I need 400 board feet of lumber to complete a job, and then order 350 because I do not want a surplus, or perhaps order 450 because I do not want to make any subsequent orders.
  • citation
  • * '>citation
  • To judge and form an opinion of the value of, from imperfect data.
  • * John Locke
  • It is by the weight of silver, and not the name of the piece, that men estimate commodities and exchange them.
  • * J. C. Shairp
  • It is always very difficult to estimate the age in which you are living.

    Synonyms

    * appraise * guess

    Derived terms

    * estimable * underestimate * overestimate

    multicollinearity

    English

    Noun

    (wikipedia multicollinearity)
  • (statistics) A phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, so that the coefficient estimates may change erratically in response to small changes in the model or data.