The data set represents a progression of hourly temperature measurements.Use the regression equation to predict the temperature
during the sixth hour. x 0 1 2 3 4 5 y 20 16 10 0 -7 -20
2 answers:
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>
You might be interested in
Answer:
8
Step-by-step explanation:
The given expression is "three less than the quotient of ten and a number, increased by six”
The quotient of ten and a number increased by six is translated into mathematical expression as:
Three less than the quotient of ten and a number, increased by six is
When we substitute n=2, we get:
Divide to get:
Add to get:
Subtract