Also known as: mamasworld
Matthew Secor lives in Kaukauna, WI. He works at I Work In A Warehouse.
Mattydale, NY | Rochester, NY | Syracuse, NY
Matthew Secor lives in Mattydale, NY. Other places in which Matthew has lived are Rochester, NY; Syracuse, NY and Macedon, NY. Matthew is related to Joseph W Lauzonis, Sharon A Secor, Jamie F Secor, Colleen M Lauzonis and Maura Secor.
Florence, SC | Poughkeepsie, NY | Fayetteville, NC
Matthew Secor lives in Florence, SC. He has also lived in Poughkeepsie, NY and Fayetteville, NC. Matthew is related to Melissa L Secor, Andrea L Martone, Deborah S Hasterlik, Gerald E Secor and Betty R Secor.
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What is Matthew Secor's address?
Matthew Secor's address is ***** S 40th St, Milwaukee, WI.
What is Matthew Secor's age?
Matthew Secor's age is 72.
What is Matthew Secor's phone number?
Matthew Secor's phone number is (414) 421-****.
What is Matthew Secor's Instagram?
We've discovered several social media accounts associated with Matthew Secor, including @matthewsecor.32, @matthewsecor, @msecord, @secordmatt and others. To explore more of Matthew Secor's online presence, click here.
What is Matthew Secor's Facebook?
We've discovered several social media accounts associated with Matthew Secor, including @WFSenior, @matthew.lively.739, @matthew.secore.5, @matthew.secord.7 and others. To explore more of Matthew Secor's online presence, click here.
What is Matthew Secor's famous for?
In statistics, the phi coefficient is a measure of association for two binary variables. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975. Introduced by Karl Pearson, and also known as the Yule phi coefficient from its introduction by Udny Yule in 1912 this measure is similar to the Pearson correlation coefficient in its interpretation. In fact, a Pearson correlation coefficient estimated for two binary variables will return the phi coefficient. Two binary variables are considered positively associated if most of the data falls along the diagonal cells. In contrast, two binary variables are considered negatively associated if most of the data falls off the diagonal. If we have a 2×2 table for two random variables x and y. You can find more here.
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