A solution can be found using substitution by substituting the ordered pair into both of the original equations.
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:
1. 22
2. 8
3. 31
4. 20
5. 38
6. 22
7. 26
8. 16
9. 14
10. 28
Step-by-step explanation:
USE PEMDAS
P: Parantheses
E: exponents
D: division
A: addition
S: subtraction
and ywww
Answer:
2/5
Step-by-step explanation:
Answer:
(2)/(3) =( 4)/( 3+x)
Cross multiply
2*(3+x)= 4*3
Solve bracket
6+2x=12
Subtract 6 from both sides
2x=6
Divide both sides by 2
x=3
Hope it helps :-)