Talk:Multiple regression: Difference between revisions

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imported>Stevegt
(Clarify the difference between multiple and single regression -- most examples on the page were cargo-culted and incorrect.)
imported>Stevegt
(fix terms in example use case)
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Think of multiple regression as "surface fitting" rather than curve fitting. Some examples:
Think of multiple regression as "surface fitting" rather than curve fitting. Some examples:
* You want to use multiple regression to create a three-dimensional surface so you can estimate option contract price given spot price and implied volatility. For pretty pictures of this, see [https://www.google.com/search?q=+volatility+surface&tbm=isch volatility surface] graphs. In this case, X is a two-dimensional matrix of <code>{spot_price[i], implied_volatility[i]}</code>, and Y is a matching list of <code>option_contract_price[i]</code>.
* You want to use multiple regression to create a three-dimensional surface so you can estimate implied volatility given time to maturity and delta of an options contract. For pretty pictures of this, see [https://www.google.com/search?q=+volatility+surface&tbm=isch volatility surface] graphs. In this case, X is a two-dimensional matrix of <code>{time[i], delta[i]}</code>, and Y is a matching list of <code>implied_volatility[i]</code>.
* As a possible modification to the existing height/weight examples: You want to use height and body-mass index (BMI) to create a three-dimensional surface so you can estimate weight: X is a two-dimensional matrix <code>{height[i], bmi[i]}</code>, Y is <code>weight[i]</code>.
* As a possible modification to the existing height/weight examples: You want to use height and body-mass index (BMI) to create a three-dimensional surface so you can estimate weight: X is a two-dimensional matrix <code>{height[i], bmi[i]}</code>, Y is <code>weight[i]</code>.


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