Answer:
It is only parallel
Step-by-step explanation:
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Answer:
Step-by-step explanation:
All triangle must have angle values adding up to 180 degrees.First solve for y;
110+33+y=180
y=37 degrees
x is the opposite inverse angle of 33 degrees, it has an angle measure of 33 degrees.
z is equal to 110 degrees because it is opposite to that angle measure because all rhombi have 2 way symmetry.
Answer:
(C) -6
Step-by-step explanation:
Given the following data;
Points on the graph (x1, y1) = (1, -9).
Mathematically, the equation of a straight line is given by the formula;
y = mx + c
Where;
m is the slope.
x and y are the points
c is the intercept.
To find the zero of the linear function f, we would use the following formula;
y - y1 = m(x - x1)
Substituting into the formula, we have;
y - (-9) = -3(x - 1)
y + 9 = -3x + 3
y = -3x + 3 - 9
y = -3x -6 = mx + c
Intercept (c) or zero of a function = -6
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).