Answer:
2710 is the answer
Step-by-step explanation:
1800+130(7)
Answer:
1/2
Step-by-step explanation:
The "Pythagorean relation" between trig functions can be used to find the sine.
<h3>Pythagorean relation</h3>
The relation between sine and cosine is the identity ...
sin(x)² +cos(x)² = 1
This can be solved for sin(x) in terms of cos(x):
sin(x) = √(1 -cos(x)²)
<h3>Application</h3>
For the present case, using the given cosine value, we find ...
sin(x) = √(1 -(√3/2)²) = √(1 -3/4) = √(1/4)
sin(x) = 1/2
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<em>Additional comment</em>
The sine and cosine of an angle are the y and x coordinates (respectively) of the corresponding point on the unit circle. The right triangle with these legs will satisfy the Pythagorean theorem with ...
sin(x)² + cos(x)² = 1 . . . . . . where 1 is the hypotenuse (radius of unit circle)
A calculator can always be used to verify the result.
Answer
Cluster sampling. See explanation below.
Step-by-step explanation:
For this case they not use random sampling since we are selecting people from flights. Because we select just 5 random flights.
Is not stratified sampling since we don't have strata clearly defined on this case, and other important thing is that in order to apply this method we need homogeneous strata groups and that's not satisfied on this case.
Is not convenience sampling because they NOT use a non probability method in order to select the people from the flights.
So then the only possible method is cluster sampling since we have clusters clearly defined (Passengers from the airlines), and we satisfy the condition of homogeneous characteristics on the clusters and an equal chance of being a part of the sample, since we are selecting RANDOMLY, the 5 flights to take the information.
Answer:
the greatest common factor is 6
Answer:
Slope of the regression line
Step-by-step explanation:
The slope of the regression line including the intercept shows the linear relationship between two variables, and can also therefore be utilized in estimating an average rate of change.
The slope of a regression line represents the rate of change in the dependent variable as the independent variable changes because y- the dependent variable is dependent on x- the independent variable.
