Answer:
A) 63.36 years.
B) 100.42 years.
Step-by-step explanation:
We have been given that the population of the world was 7.1 billion in 2013, and the observed relative growth rate was 1.1% per year.
A) Since we know that population increases exponentially, therefore we will use our given information to form an exponential model for population increase and then we will solve for the time by which our population will be double.


Now let us solve for t using logarithm.



Therefore, it will take 63.36 years the population to be double.
B) Now we will find the number of years it will take the population to be triple of its size.


Now let us solve for t using logarithm.



Therefore, it will take 100.42 years the population to triple of its size.
Answer:
you doing k12 geometry too?
Step-by-step explanation:
Answer:
It is not a function.
Step-by-step explanation:
This is what we call a mapping;
Functions do not give multiple output values for a given input value;
As we can see, the input 2 produces an output of 20 and 40, therefore we can tell this is not a function.
we know that
The scalar magnitude of the velocity vector is the speed. The speed is equal to

in this problem we have

substitute in the formula


therefore
<u>the answer Part a) is</u>
the speed is equal to 
<u>Part b) </u>Find the velocity
we know that
<u>Velocity </u>is a vector quantity; both magnitude and direction are needed to define it
in this problem we have
the magnitude is equal to the speed


therefore
<u>the answer Part b) is</u>
the velocity is 
Part c)
we know that
the acceleration is equal to the formula

in this problem we have


substitute in the formula



therefore
<u>the answer Part c) is</u>
the acceleration is 
This is an example of negative acceleration
Answer:
1. Data point A
4. Data point D
Step-by-step explanation:
In a scatter plot, the closer the clustered data points are close to the best line of fit, the greater the correlation that would exist between the two variables.
If we are to draw a best line of fit in the scatter plot that is shown above, the closest data points amongst data points A, B, C, D, and E, that would be close to the best line of fit are data points A and D.
Therefore, removing data point A and point D would cause the correlation to decrease the most.