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Shalnov [3]
3 years ago
15

Determine whether the conditional and its converse are both true. If both are true, combine them as a biconditional. If either i

s false, provide a counterexample. If an angle is a right angle, its measure is 90. If an angle measure is 90, the angle is a right angle. Both statements are true. An angle is a right angle if and only if its measure is 90. Both statements are true. The measure of an angle is 90 if and only if it is not a right angle. One statement is false. If an angle is a right angle, its measure may be 180. One statement is false. If an angle measure is 90, the angle may be an obtuse angle.
Mathematics
2 answers:
Marina CMI [18]3 years ago
6 0
If an angle is a right angle, its measure is 90.
If an angle measure is 90, the angle is a right angle.

The answer is:
Both statements are true.

This is the biconditional.
An angle is a right angle if and only if its measure is 90°.

Other types of angles: Acute angle - less than 90° Obtuse angle - more than 90° but less than 180°. Straight angle - exactly 180° Reflex angle - more than 180°
Nimfa-mama [501]3 years ago
5 0

Answer: The answer is (B) "both statements are true. An angle is a right angle if and only if it's measure is 90°."

Step-by-step explanation: The given statements are

If an angle is right angle, it's measure is 90°.

If the measure of an angle is 90°, then it is a right angle.

Both the statements are true. We can combine them as a unconditional as follows:

An angle is a right angle if an only if it's measure is 90°.

Hence, (B) is the correct option.

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You solved a linear system with two equations and two variables and got the equation -6=-6. How many solutions does the systems
elixir [45]

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The system of equations will have an infinite number of solutions.

Step-by-step explanation:

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3 years ago
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melamori03 [73]

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58 ft squared.

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A broker has calculated the expected values of two different financial instruments X and Y. Suppose that E(x)= $100, E(y)=$90 SD
Sveta_85 [38]

Expectation is linear, meaning

E(<em>a X</em> + <em>b Y</em>) = E(<em>a X</em>) + E(<em>b Y</em>)

= <em>a </em>E(<em>X</em>) + <em>b</em> E(<em>Y</em>)

If <em>X</em> = 1 and <em>Y</em> = 0, we see that the expectation of a constant, E(<em>a</em>), is equal to the constant, <em>a</em>.

Use this property to compute the expectations:

E(<em>X</em> + 10) = E(<em>X</em>) + E(10) = $110

E(5<em>Y</em>) = 5 E(<em>Y</em>) = $450

E(<em>X</em> + <em>Y</em>) = E(<em>X</em>) + E(<em>Y</em>) = $190

Variance has a similar property:

V(<em>a X</em> + <em>b Y</em>) = V(<em>a X</em>) + V(<em>b Y</em>) + Cov(<em>X</em>, <em>Y</em>)

= <em>a</em>^2<em> </em>V(<em>X</em>) + <em>b</em>^2 V(<em>Y</em>) + Cov(<em>X</em>, <em>Y</em>)

where "Cov" denotes covariance, defined by

E[(<em>X</em> - E(<em>X</em>))(<em>Y</em> - E(<em>Y</em>))] = E(<em>X Y</em>) - E(<em>X</em>) E(<em>Y</em>)

Without knowing the expectation of <em>X Y</em>, we can't determine the covariance and thus variance of the expression <em>a X</em> + <em>b Y</em>.

However, if <em>X</em> and <em>Y</em> are independent, then E(<em>X Y</em>) = E(<em>X</em>) E(<em>Y</em>), which makes the covariance vanish, so that

V(<em>a X</em> + <em>b Y</em>) = <em>a</em>^2<em> </em>V(<em>X</em>) + <em>b</em>^2 V(<em>Y</em>)

and this is the assumption we have to make to find the standard deviations (which is the square root of the variance).

Also, variance is defined as

V(<em>X</em>) = E[(<em>X</em> - E(<em>X</em>))^2] = E(<em>X</em>^2) - E(<em>X</em>)^2

and it follows from this that, if <em>X</em> is a constant, say <em>a</em>, then

V(<em>a</em>) = E(<em>a</em>^2) - E(<em>a</em>)^2 = <em>a</em>^2 - <em>a</em>^2 = 0

Use this property, and the assumption of independence, to compute the variances, and hence the standard deviations:

V(<em>X</em> + 10) = V(<em>X</em>)  ==>  SD(<em>X</em> + 10) = SD(<em>X</em>) = $90

V(5<em>Y</em>) = 5^2 V(<em>Y</em>) = 25 V(<em>Y</em>)  ==>  SD(5<em>Y</em>) = 5 SD(<em>Y</em>) = $40

V(<em>X</em> + <em>Y</em>) = V(<em>X</em>) + V(<em>Y</em>)  ==>  SD(<em>X</em> + <em>Y</em>) = √[SD(<em>X</em>)^2 + SD(<em>Y</em>)^2] = √8164 ≈ $90.35

8 0
4 years ago
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