Ten minus one-third of number is 4
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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>
The functions are illustrations of composite functions.
<em>The soil temperature at 2:00pm is 67</em>
The given parameters are:
---- the function for sun intensity
-- the function for temperature
At 2:00pm, the value of h (number of hours) is:
Substitute 8 for h in , to calculate the sun intensity
Substitute 8/9 for I in , to calculate the temperature of the soil
Approximate
Hence, the soil temperature at 2:00pm is 67
Read more about composite functions at: