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>
Using the distributive property we know we distribute by multiplying the outside values to those inside of the parentheses
outside values: 2
inside values: 5 and r
now multiply out inside values by the outside values
2x5 = 10
2xr = 2r
now plug these values back in for the inside values and take away the outside value and the parentheses
so 2(5+r) using the distribution property is 10+2r
Answer:
106°
Step-by-step explanation:
Supplementary angles total 180°, so the one supplementary to 74° is ...
180° -74° = 106°
Shoe=10
Girl=5
Belt=8
1 shoe+girl+belt=23