We will have to find the equation that follows the form:
![a+bx=y](https://img.qammunity.org/2023/formulas/mathematics/college/7txcl0uml0ysj2wce1q43cfektrx2q43vg.png)
That is:
*The value of a is:
![a=547.4666667](https://img.qammunity.org/2023/formulas/mathematics/college/x7cigci34dg505yujf06u4itowu6dlhgo9.png)
*The value of b is:
![b=29.91428571](https://img.qammunity.org/2023/formulas/mathematics/college/ehh5syan6nqr0gj7hq8ecvqxth5shxauy0.png)
So, the linear regression is:
![y=29.91428571x+547.4666667](https://img.qammunity.org/2023/formulas/mathematics/college/n87isp3tke88zd0s9oxj3hh9ejqyl0el6i.png)
We can see it graphically as follows:
Here we can see the linear regression line and the points used.
***How to manually determine linear regressions***
We will start as follows:
We will have to use the following expressions in order to manually find the linear regression:
![b=\frac{n(\sum^{}_{}xy)-(\sum^{}_{}x)(\sum^{}_{}y)}{n(\sum^{}_{}x^2)-(\sum^{}_{}x)^2}](https://img.qammunity.org/2023/formulas/mathematics/college/9p2ucz264gvwao6q6t1a256tgm0g9zoek2.png)
And:
![a=\frac{\sum ^{}_{}y-b(\sum ^{}_{}x)}{n}](https://img.qammunity.org/2023/formulas/mathematics/college/uropeclgyeehm4p59ga3hnaxob7jgwjh0g.png)
Here we have that n is the number of values on the dataset. So, we solve for b as follows [Based on the problem given]:
*We will determine the values of n, the sum of x, the sum of y, xy, x^2 & y^2 as follows:
Sum of x:
![\sum ^{}_{}x=1+2+3+4+5+6\Rightarrow\sum ^{}_{}x=21](https://img.qammunity.org/2023/formulas/mathematics/college/htyasfqgmyxt8fmeo9d89tziy79mgdh8ra.png)
Sum of y:
![\sum ^{}_{}y=588+298+640+650+707+730\Rightarrow\sum ^{}_{}y=3913](https://img.qammunity.org/2023/formulas/mathematics/college/1e5lkc1ksh51dtbyyop85kkhaoij6r7bx8.png)
Value of n: We have that the number of datasets given is 6 [6 pairs of values on the table].
![n=6](https://img.qammunity.org/2023/formulas/mathematics/college/o7iwtqrm2mbn6a11qav92k1mbcmfp9jb6e.png)
*The sum of xy:
![\sum ^{}_{}xy=(1)(588)+(2)(598)+(3)(640)+(4)(650)+(5)(707)+(6)(730)\Rightarrow\sum ^{}_{}xy=14219](https://img.qammunity.org/2023/formulas/mathematics/college/abkot3pwzvtegjsvegnurhafadbqk6h0l9.png)
*The sum of X^2:
![\sum ^{}_{}x^2=1^2+2^2+3^2+4^2+5^2+6^2\Rightarrow\sum ^{}_{}x^2=91](https://img.qammunity.org/2023/formulas/mathematics/college/om26u7il4tkr8q0gqkaet27ekj2ou2rowm.png)
*The square of the sum of x:
![(\sum ^{}_{}x)^2=21^2\Rightarrow(\sum ^{}_{}x)^2=441](https://img.qammunity.org/2023/formulas/mathematics/college/ygapq3e7rhnabnsaa57ue41psni7w71qzj.png)
Now that we have all the values, we replace in the first equation and solve for b:
![b=((6)(14219)-(21)(3913))/((6)(91)-(441))\Rightarrow b=29.91428571](https://img.qammunity.org/2023/formulas/mathematics/college/z77pzu8bj7bog3jq1wsqjd8ybmx1zkvgun.png)
And we solve now for a:
![a=((3913)-(29.91428571)(21))/((6))\Rightarrow a=548.4666667](https://img.qammunity.org/2023/formulas/mathematics/college/k8ub1yk2s2jrfsuti1m1qif8gdxl1vjn1m.png)
And then we simply replace on the expression:
![y=bx+a](https://img.qammunity.org/2023/formulas/mathematics/college/6hkh6sbbwj4pv7wj8px9pl1vux49el11nm.png)
And the expression after the replacement of a & b is the linear regression.