The regression line is S = α + β P + E S=\alpha+\beta P+E S = α + βP + E
E is random error
and E ( s ) = α + β P E(s)=\alpha+\beta P E ( s ) = α + βP
S S S = ∑ S 2 = ∑ i = 1 8 ( S i − S ) 2 = 1205 S_{SS}=\sum S^2=\displaystyle\sum_{i=1}^8(S_i-S)^2=1205 S SS = ∑ S 2 = i = 1 ∑ 8 ( S i − S ) 2 = 1205
S P P = ∑ P 2 = ∑ i = 1 8 ( P i − P ) 2 = 55.9 S_{PP}=\sum P^2= \displaystyle\sum_{i=1}^8(P_i-P)^2=55.9 S PP = ∑ P 2 = i = 1 ∑ 8 ( P i − P ) 2 = 55.9
S S P = ∑ ( S P ) = ∑ i = 1 8 ( S i − S ) ( P i − P ) = 22.4 S_{SP}=\sum(SP)=\displaystyle\sum_{i=1}^8(S_i-S)(P_i-P)=22.4 S SP = ∑ ( SP ) = i = 1 ∑ 8 ( S i − S ) ( P i − P ) = 22.4
α = S − P β \alpha=S-P\beta α = S − Pβ and β = ( ∑ S P ) ( ∑ S 2 ) 1 / 2 × ( ∑ P 2 ) 1 / 2 \beta=\frac{(\sum SP)}{(\sum S^2)^{1/2}\times (\sum P^2)^{1/2}} β = ( ∑ S 2 ) 1/2 × ( ∑ P 2 ) 1/2 ( ∑ SP ) = ( 225.4 ) ( 1205 ) 1 / 2 × ( 55.9 ) 1 / 2 = 0.8685 =\frac{(225.4)}{(1205)^{1/2}\times (55.9)^{1/2}}=0.8685 = ( 1205 ) 1/2 × ( 55.9 ) 1/2 ( 225.4 ) = 0.8685
from the table S = ∑ S i / n = 225 / 8 = 28.125 S=\sum S_i/n=225/8=28.125 S = ∑ S i / n = 225/8 = 28.125
P = ∑ P i / n = 37 / 8 = 4.625 P=\sum P_i/n=37/8=4.625 P = ∑ P i / n = 37/8 = 4.625
α = 28.125 − 4.625 × 0.8685 = 24.1082 \alpha=28.125-4.625\times0.8685=24.1082 α = 28.125 − 4.625 × 0.8685 = 24.1082
a) the estimated regression line is, S = 24.1082 + 0.8685 P i S=24.1082+0.8685P_i S = 24.1082 + 0.8685 P i
b) the standard error (SE) of α \alpha α and β \beta β are
S E ( α ) = σ 1 / n + P 2 / S p p SE(\alpha)=\sigma \sqrt{1/n+P^2/S_{pp}} SE ( α ) = σ 1/ n + P 2 / S pp
and S E ( α ) = σ / S p p SE(\alpha)=\sigma/\sqrt{S_{pp}} SE ( α ) = σ / S pp
σ 2 = 1 / ( n − 2 ) S S E = 1 / ( n − 2 ) [ S s s − β 2 S p p = 1 / ( 8 − 2 ) [ 1205 − 0.8685 ) 2 × ] \sigma^2=1/(n-2)SSE=1/(n-2)[S_{ss}-\beta^2S_{pp}=1/(8-2)[1205-0.8685)^2\times] σ 2 = 1/ ( n − 2 ) SSE = 1/ ( n − 2 ) [ S ss − β 2 S pp = 1/ ( 8 − 2 ) [ 1205 − 0.8685 ) 2 × ]
= 1 / 6 × 1162.8351 = 193.8058 =1/6\times1162.8351=193.8058 = 1/6 × 1162.8351 = 193.8058
σ = 1 93.8058 = 13.9214 \sigma=\sqrt193.8058=13.9214 σ = 1 93.8058 = 13.9214
S E ( α ) = σ 1 / n + P 2 / S p p = 13.9214 / ( 55.9 ) = 13.9214 / 7.4766 = 1.86199 SE(\alpha)=\sigma \sqrt{1/n+P^2/S_{pp}}=13.9214/(\sqrt{55.9})=13.9214/7.4766=1.86199 SE ( α ) = σ 1/ n + P 2 / S pp = 13.9214/ ( 55.9 ) = 13.9214/7.4766 = 1.86199
c) testing for hypothesis H 0 : β = 0 v e r s u s H 1 : β ≠ 0 H_0:\beta=0 \space versus \space H_1:\beta \not= 0 H 0 : β = 0 v ers u s H 1 : β = 0
at α = 0.05 \alpha=0.05 α = 0.05
t = ( β − 0 ) / ( S E ( β ) t=(\beta-0)/(SE(\beta) t = ( β − 0 ) / ( SE ( β )
t = 0.8685 / 1.86199 = 0.4664 t=0.8685/1.86199=0.4664 t = 0.8685/1.86199 = 0.4664
t c r i t i c a l = 2.4469 t_{critical}=2.4469 t cr i t i c a l = 2.4469
∣ t ∣ < 2.4469 |t|\lt2.4469 ∣ t ∣ < 2.4469
we fail to reject H 0 H_0 H 0 , that means at α = 0.05 , P d o e s n ′ t a f f e c t S \alpha=0.05, P\space doesn't\space affect \space S α = 0.05 , P d oes n ′ t a ff ec t S
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