I will attach google sheet that I used to find regression equation.
We can see that linear fit does work, but the polynomial fit is much better.
We can see that R squared for polynomial fit is higher than R squared for the linear fit. This tells us that polynomials fit approximates our dataset better.
This is the polynomial fit equation:
I used h to denote hours. Our prediction of temperature for the sixth hour would be:
Here is a link to the spreadsheet (
<span>https://docs.google.com/spreadsheets/d/17awPz5U8Kr-ZnAAtastV-bnvoKG5zZyL3rRFC9JqVjM/edit?usp=sharing)</span>
Answer:
B. y= -3x2 + 3
Step-by-step explanation:
y= -3^2+3
The 1st term is 60.
Add 50 to this to get the 2nd term, 60 + 50 = 110.
Add 50 to that to get the 3rd term, 110 + 50 = 160.
Add 50 to that to get the 4th term, 160 + 50 = 210.
And so on...
Notice that in the 2nd term, we added 1 copy of 50 to the 1st term.
In the 3rd, we ultimately added 2 copies of 50 to the 1st term.
In the 4th, we added 3 50s.
And so on... If the pattern continues, then the <em>n</em>-th term can be obtained by adding (<em>n</em> - 1) copies of 50 to the first term.
So, the 100th term is
60 + (100 - 1) * 50 = 5010
Because it is the same by the 0 and the 0 represents as a place holder for the 32