| ΣX | ΣY | Σ(x-x̅)² | Σ(y-ȳ)² | Σ(x-x̅)(y-ȳ) | |
| total sum | 82.00 | 502.00 | 63.60 | 4413.60 | -406.40 |
| mean | 8.20 | 50.20 | SSxx | SSyy | SSxy |
Sample size, n = 10
here, x̅ = Σx / n= 8.200
ȳ = Σy/n = 50.200
SSxx = Σ(x-x̅)² = 63.6000
SSxy= Σ(x-x̅)(y-ȳ) = -406.4
estimated slope , ß1 = SSxy/SSxx =
-406.4/63.6= -6.3899
intercept,ß0 = y̅-ß1* x̄ = 50.2- (-6.3899
)*8.2= 102.5975
Regression line is, Ŷ= 102.60 +
( -6.390 )*x
..............
SSE= (SSxx * SSyy - SS²xy)/SSxx =
1816.7296
std error ,Se = √(SSE/(n-2)) =
15.0695
..............
correlation coefficient , r = SSxy/√(SSx.SSy)
= -0.7671
THERE IS NEGATIVE RELATIONSHP BETWEEN TWO VARIABLES
MODERATE STRENGTH
................
Ho: β1= 0
H1: β1╪ 0
n= 10
alpha = 0.05
estimated std error of slope =Se(ß1) = Se/√Sxx =
15.0695/√63.6= 1.8896
t stat = estimated slope/std error =ß1 /Se(ß1) =
(-6.3899-0)/1.8896= -3.38
Degree of freedom ,df = n-2= 8
p-value = 0.0096
decison : p-value<α , reject Ho
Conclusion: Reject Ho and conclude that slope is
significantly different from zero
THERE IS SIGNIFCANT RELATIONSHIP BETWEEN TWO VARIABLES
....................
SO, HERE
WE can cconcude that there is signifcant association between two variables
and there is negative relation with moderate strength
.............................
Please let me know in case of any doubt.
Thanks in advance!
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