Determine whether each set of numbers is a Phythagorean triple.
1 answer:
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Answer:
The answer is A
Step-by-step explanation:
Step 1: Use Cosine to find ∅
Cos∅=
∅=Cos(
)
Therefore the answer is A
Answer:
0.8594
Step-by-step explanation:
Let a denote the event of forgetting to shut off machine and b be the event of being defective.
-A foreman forgets to shut off machine 55% of the time.
-If he forgets, 15% of molds are defective.
-If he does not, 3% of molds are defective.
#The probability that he forgot to shut off the machine is calculated as:
P(a and ~b)=0.55(1-0.15)=0.4675
P(~a and b) = (1-0.55)*0.03=0.0135
P(~a and ~b) = (1-0.55)*(1-0.03)=0.4365
#Conditional probability is defined as:
Hence, the probability that the foreman forgot to shut off the machine the previous night is 0.8594
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>