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>
2 * M = X
M = to minutes
x = to how many tables washes
Answer:
$15 per hour
Step-by-step explanation:
30 hours per week x 52 weeks in a year = 1560 hours per year
23,400 / 1560 = $15 per hour
Answer:
1) 0.024
2) 0.033
3) 4.8
4) 0.16
5) 0.084
6) 0.14
Step-by-step explanation:
1. 3 x 0.008 = 0.024
2. 11 x 0.003 = 0.033
3. 12 x 0.4 = 4.8
4. 4 x 0.04 = 0.16
5. 12 x 0.007 = 0.084
6. 7 x 0.02 = 0.14
You will need 6/4 or 1 1/2 cups of blueberries to make 48 muffins (2 dozen),