Helpppp please will give brainliest
1 answer:
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The complete question in the attached figure
we know that
<span>The measure of the external angle is the semidifference of the arcs that it covers.
so
</span><span>m∠BED =(1/2)*[mAC-mBD]-------> solve for mBD
mBD=mAC-2</span>m∠BED
mBD=80-2*25--------> mBD=30°
the answer is
mBD=30°
we know that
<span>The measure of the external angle is the semidifference of the arcs that it covers.
so
</span><span>m∠BED =(1/2)*[mAC-mBD]-------> solve for mBD
mBD=mAC-2</span>m∠BED
mBD=80-2*25--------> mBD=30°
the answer is
mBD=30°
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>