Transcript
Page 1: Least-Squared Error Curve Fits for Sweetwater Rel. Humidity vs Temp, Week of Aug 03, 2014

0102030405060708090

60 70 80 90 100 110

Y (S

wee

twat

er R

H%

)

X (Sweetwater Temp F)

Polynomial Order = 1, R = 0.949

(x,y)

y-hat

0102030405060708090

60 70 80 90 100 110

Y (S

wee

twat

er R

H%

)

X (Sweetwater Temp F)

Polynomial Order = 2, R = 0.956

(x,y)

y-hat

0102030405060708090

60 70 80 90 100 110

Y (S

wee

twat

er R

H%

)

X (Sweetwater Temp F)

Polynomial Order = 3, R = 0.957

(x,y)

y-hat

0102030405060708090

60 70 80 90 100 110

Y (S

wee

twat

er R

H%

)

X (Sweetwater Temp F)

Polynomial Order = 4, R = 0.958

(x,y)

y-hat

Least-Squared Error Curve Fits for Sweetwater Rel. Humidity vs Temp, Week of Aug 03, 2014

Page 2: Least-Squared Error Curve Fits for Sweetwater Rel. Humidity vs Temp, Week of Aug 03, 2014

0123456789

6 7 8 9 10 11

Y (S

wee

twat

er R

H%

)

X (Sweetwater Temp F)

Polynomial Order = 7, R = 0.959

(x,y)

y-hat

0123456789

6 7 8 9 10 11

Y (S

wee

twat

er R

H%

)

X (Sweetwater Temp F)

Polynomial Order = 5, R = 0.958

(x,y)

y-hat

0123456789

6 7 8 9 10 11

Y (S

wee

twat

er R

H%

)

X (Sweetwater Temp F)

Polynomial Order = 6, R = 0.959

(x,y)

y-hat

Dividing Temp and RH by 10 overcame the numerical precision problem caused by having huge pivot values, so that the process could continue through the 7th order