Understanding a Correlation Coefficient 92000
Data
Each data pair shown is of the form below
х
y
28000
(years since purchase, value of car)
24000
Based on the correlation coefficient, there is a(n)
decreasing = strong X linear correlation
between the data
Xo
X 1
X2
X 3
X 4
X 5
X 6
X 7
31000
29373
26810
22375
17290
14970
11453
10921
20000
COMPLETE
16000
12000
Use the regression equation to estimate the
value of the car 8 years after
purchase. Round to
the nearest dollar. $
6
DONE
Linear Regression
y-3223.31x + 31805.583; -0.988
-0.7000 SXS 7.7
Window:
8913.1 Sys 330079
Resize window
to fit data

sorry man i accidentally pressed yes my bad

step-by-step explanation:

< xcy ≅ < xby

step-by-step explanation:

in δcax and δbax,

so, δcax ≅ δbax (side-angle-side or sas)

since δcax ≅ δbax, we can conclude < acx ≅ < abx

in δacy and δaby,

so, δacy ≅ δaby (side-angle-side or sas)

since δacy ≅ δaby, we can conclude < acy ≅ < aby

now,

< acy ≅ < aby

=> < acx + < xcy ≅ < abx + < xby

=> < acx + < xcy ≅ < acx + < xby   (since < acx ≅ < abx already proved above)

subtracting < acx from both sides, we get

< acx + < xcy -< acx ≅ < acx + < xby -< acx

cancelling out < acx and -< acx from both sides, we get

< xcy ≅ < xby (proved)