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>
Let's consider that number to be 'x'..
So, twice the number (2x) is 12 greater than than the half of the number.
So the equation will be like,
2x=12+x/2
Solving this further,
2x=(24+x)/2
=> 4x=24+x
=>4x-x=24
=>3x=24
=>x=8..
So the number is "8".
1% (I'm pretty sure)
Step-by-step explanation:
you would do 9.65 ÷ 9.58 to to get 1.0073 and rounding it would give you 1
810/2= 405
810/3= 270
810/4= 202.5 (not this one)
810/5= 162
810/6= 135
810/7= 115.71 ( not this one)
810/8= 101.25 (not this one)
810/9= 90
810/10= 81
810/11= 73.63 (not this one)
I hope this helps you!