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:
6 cups of sugar
Step-by-step explanation:
3/4x2=1.5
1.5x4=6
Answer:
See explanation below
Step-by-step explanation:
The best explanation is noticing that in order to get from the point R (12, 1) to the point Q (7, 4) we move 5 units to the left and 3 units up. And to go from point Q (7, 4) to point P (2, 7) we do exactly the same: move 5 units to the left and 3 units up. That means that these points are all connected via the same rate of change: - 3/5, which is in fact the slope of the line the three points belong to.
Answer:
x = - 71
Step-by-step explanation:
x - 12 = - 83
+12 +12 (adding 12 to both sides)
x = - 71