Calc help!

[ga14]

Hey Guys,

I need help with following question:

"Suppose you are given a set of ten data points {(x1, y1), (x2,y2), (x3, y3)...(x10,y10)} and you want to find the "best fit" line to the data. In statistics the best fit line (often called the least squares line) is defined to be the line y=mx+b that minimizes the sum of the squares of the differences between the line and the data points. More precisely, the line of best fit is the line y=mx+b that minimizes the Rieman sum from k=1 to n of (y_k-(mx_k+b))^2
where _k denotes "sub k".

In this problem, you can assume n=10. Use partial differentiation to show that the line of best fit has slope _______
and y intercept ________."

Thanks for any help at all!

 

[Rob9874]

2 and 1.

 

[bleeb]

The freakin problem tells you what to do.. USE PARTIAL DIFFERENTIATION!! MORON!!

QUICK SOMEONE GET THE "JESUS AGREES...GET THE FVCK OUT!!!" picture!!! =) (jus kiddin) 😀

 

[UncleWai]

THere

 

[bleeb]

Originally posted by: UncleWai
THere

Thanks buddy...

 

[ga14]

use partial differentiation and do what? take the partials of that equation with respect to m and b and set them equal to zero?

 

[PowerMacG5]

Originally posted by: ga14
use partial differentiation and do what? take the partials of that equation with respect to m and b and set them equal to zero?

m and b are constants, you don't differentiate with respect to a constant.

 

[ga14]

In this case m and b are the variables because those are the variables you're trying to optimize. You already know x and y because they give you a set of 10 points, so they are constants.