Based on the data shown below, calculate the regression line
(each value to two decimal places)
y = x +
x | y |
---|---|
3 | 17.28 |
4 | 17.04 |
5 | 17.3 |
6 | 21.46 |
7 | 21.52 |
8 | 19.38 |
9 | 22.14 |
10 | 21.7 |
11 | 22.16 |
12 | 22.92 |
13 | 21.38 |
14 | 21.54 |
15 | 25 |
16 | 23.56 |
17 | 23.32 |
18 | 24.18 |
The regression equation will be of the form,
y=β0+β1x+e ,
where β0 is the shift from the origin or the intercept and β1is the slope
of the regression equation and e is the error
Now, we have to estimate the coefficients of the equation using least square method.
Applying least square method,
β1=covx,ySx2 and β0=y-β1x
X=1ni=1nxi, Y=1ni=1nyi
X |
10.5 |
Y |
21.368 |
Sxx |
22.667 |
Syy |
5.9641 |
Sxy |
9.96 |
Now, we have the estimated values as,
β0 |
16.75368 |
β1 |
0.439412 |
Therefore the required regression equation is,
y=16.75368+0.439412x
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