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
Step-by-step explanation:
if BU and BN weren't congruent, then the two triangles couldn't be proved to be congruent
% means per 100
so when figuring out percents it's good to use 100
so
on $100 7% would mean $7
on $50 7% would mean $3.5
in $200 7% would mean $14
Answer:
r = - 7
Step-by-step explanation:
The common ratio r is the ratio of a term to the preceding term, that is
r = 126 ÷ - 18 = - 882 ÷ 126 = - 7
Answer:
c) y= -3
Step-by-step explanation: