Answer:
A ( -d , e )
B ( - d , -e )
C ( d , - e )
Step-by-step explanation:
The centroid of the circle is on the origin.
This means that coordinates are an equal distance away from each other
Ex. Coordinate A and coordinate B are an equal distance away.
The only difference between the coordinates is the signs of the x and y values
This is determined by what quadrant the specific coordinate is in
Ex. Coordinate A is in Quadrant 2.
Coordinates in Quadrant 2 have a negative x value and a positive x value
Because Coordinate A and the given coordinates ( d , e ) have an equal distance between them all we have to do to find the missing coordinate is add a negative sign in front of the x value (d)
That being said, the coordinate of A is (-d,e).
This process is used to find the rest of the missing coordinates
for B
B lies in Quadrant 3
Quadrant 3 = ( -x , -y )
* add negative signs to the x and y value of the given coordinate *
B = ( -d , - e)
For C
C lies in Quadrant 4
Quadrant 4 = ( x , - y )
* add a negative sign to the y value of the given coordinate *
C = ( d, -e)
During the holiday sale it costs $1560 to buy three laptop computers
Answer:
Center: (1,−4)
Radius: 2
Step-by-step explanation:
Answer:
15 cups of flour
Step-by-step explanation:
if the vanilla is going from 2 - 6 then it is being multipled by 3. Therfore you should multiply the flour (5) by 3, which makes the total flour to be 15 cups.
Answer:
<em>A normal distribution</em> is a general distribution that represents any normally distributed data with any possible value for its parameters, that is, the mean and the standard deviation. Conversely, <em>the standard normal distribution</em> is a specific case where the mean equals zero and the standard deviation is the unit. That is why we can refer to <em>a normal distribution</em> and <em>the standard normal distribution</em>.
Step-by-step explanation:
We have to remember that <em>a normal distribution</em> has two parameters that define it, namely, <em>the mean</em> and <em>the standard deviation</em>, and there are, theoretically, infinite possible means and standard deviations, so we tell about <em>a </em>normal distribution in general.
Conversely, <em>the</em> standard normal distribution is <em>a normal distribution</em> with a mean = 0 and a standard deviation = 1, and we also have to remember that is possible to 'convert' or 'transform' any raw score from any normally distributed data into a z-score to find its probability using <em>the</em> standard normal distribution. The formula for a z-score is as follows:
Where
.
.
.
In other words,<em> the standard normal distribution</em> is a specific case for normally distributed data whose values are standardized or represent distances from the mean in standard deviations units, and thanks to this, we can find any associated probability with these values for any possible normal distribution (see the graph below).