ΣX | ΣY | Σ(x-x̅)² | Σ(y-ȳ)² | Σ(x-x̅)(y-ȳ) | |
total sum | 1613 | 281.9 | 3756.96 | 465.34 | -757.64 |
mean | 62.03846154 | 10.8423077 | SSxx | SSyy | SSxy |
sample size , n = 26
here, x̅ = 62.03846154
ȳ = 10.84230769
SSxx = Σ(x-x̅)² = 3756.96
SSxy= Σ(x-x̅)(y-ȳ) = -757.64
A)
slope , ß1 = SSxy/SSxx =
-0.201663047
intercept, ß0 = y̅-ß1* x̄ = 23.35317286
B)
SSE= (Sx*Sy - S²xy)/Sx = 312.55
std error ,Se = √(SSE/(n-2)) = 3.6087
estimated std error of slope =Se(ß1) = s/√Sxx = 0.0589
estimated std error of INTERCEPT =Se(ßo) = Se*√(1/n+x̅²/Sxx)= 3.72050
C)
confidence interval for slope
t critical value= t (α/2) =t(0.025,24) =
2.0638 [ excel formula =T.INV(0.025,24) ]
margin of error ,E= t*std error =
0.121513796
lower confidence limit = ß̂1-E
= -0.323176843
upper confidence limit= ß̂1+E
= -0.08014925
confidence interval for intercept
t critical value= t α/2 = 2.0638
estimated std error of slope =Se(ßo) =
Se*√(1/n+x̅²/Sxx)=
3.7205
margin of error ,E= t*std error =
7.67874
lower confidence limit = ß̂o-E
= 15.6744
upper confidence limit= ß̂o+E
= 31.03191
D) since, confidence interval of slope is negative ,so slope is significantly less than zero
and confidence interval of intercept is positive ,so intercept
is significantly greater than zero
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