Joanna bought 1 1/2 pounds of apples, 3/4 pound of strawberries, 1/2 pound of grapes, 1 3/4pounds of oranges, and 1 1/4 pounds o
2 answers:
You might be interested in
A rational number is any number that can be written as the ratio between two other numbers i.e. in the form 
Part A:
An easy choice that makes sense is 7.8, right in the middle. To prove that it's rational we need to write it as a ratio. In this case we have
Part B:
We need a number that can't be written as a ratio (because it neither terminates nor repeats). Some common ones are
, 
, 
and 
so it makes sense to try and use those to build our number. In this case 
works nicely.
Part A:
An easy choice that makes sense is 7.8, right in the middle. To prove that it's rational we need to write it as a ratio. In this case we have
Part B:
We need a number that can't be written as a ratio (because it neither terminates nor repeats). Some common ones are
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>
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>