A system of linear inequalities in two variables consists of at least two linear inequalities in the same variables. The solution of a linear inequality is the ordered pair that is a solution to all inequalities in the system and the graph of the linear inequality is the graph of all solutions of the system.
Answer: The area is 268 cm squared (approximately)
Step-by-step explanation: The area of a parallelogram is given as ;
Area = b x h
Where b is the base and h is the vertical height. However the vertical height is not given, so we shall apply the trigonometric ratio to determine the vertical height. If we draw a straight line from the the top right vertex down to the base, we would have constructed a right angled triangle with hypotenuse 13cm, and reference angle 79 degrees. We can now calculate the vertical height as follows;
Sin 79 = opposite/hypotenuse
Sin 79 = h/13
By cross multiplication we now have
Sin 79 x 13 = h
0.9816 x 13 = h
12.76 = h
With the vertical height now known, we can calculate the area as follows
Area = b x h
Area = 21 x 12.76
Area = 267.96 cm squared
Answer:
The minimum point is (4,-3)
Step-by-step explanation:
we know that
If the new equation is
y=f(x-5)
then
The Rule of the translation is
(x,y) -----> (x+5,y)
That means ----> The translation is 5 units at right
so
(−1,−3) ----> (-1+5,-3)
(−1,−3) ----> (4,-3)
ANSWER

EXPLANATION
The given expression is

This is the same as:

In index notation, we write this as

To write this as a negative index, we use the property:

This implies that;

Answer:
See the proof below.
Step-by-step explanation:
For this case we just need to apply properties of expected value. We know that the estimator is given by:

And we want to proof that 
So we can begin with this:

And we can distribute the expected value into the temrs like this:

And we know that the expected value for the estimator of the variance s is
, or in other way
so if we apply this property here we have:

And we know that
so using this we can take common factor like this:

And then we see that the pooled variance is an unbiased estimator for the population variance when we have two population with the same variance.