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Leno4ka [110]
3 years ago
9

John wants to represent 30% more than a number he writes 1.30x is he correct

Mathematics
1 answer:
Svetradugi [14.3K]3 years ago
8 0
0.39 I believe is the answer

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Solve the following inequality: -20 &lt; 4 - 2x<br> A. 8 &gt;x<br> B. 8 C. 12 &gt; x<br> D. 12
Mama L [17]

Answer:

-20 < 4 - 2x (subtract 4 from both sides)

-24 < -2x (divide each side by -2)

12> x  (when you divide by a negative number, the inequality flips)

x< 12 ( I always put it so x is first)

so the answer is C

6 0
2 years ago
What is the value of the expression 12.25 + (-17.75)?
Reika [66]

Answer:

-5.5

Step-by-step explanation:

7 0
3 years ago
Determine whether AB ← → and CD ← → − are parallel, perpendicular, or neither. A(-6, 2), B(−3, -4), C(1, -3), D(3, 4)
guapka [62]
Neither they connect but don’t make a 90 degree angle

6 0
2 years ago
Let U1, ..., Un be i.i.d. Unif(0, 1), and X = max(U1, ..., Un). What is the PDF of X? What is EX? Hint: Find the CDF of X first,
Kryger [21]

Answer:

E(X)= n \int_{0}^1 x^n dx = n [\frac{1}{n+1}- \frac{0}{n+1}]=\frac{n}{n+1}

Step-by-step explanation:

A uniform distribution, "sometimes also known as a rectangular distribution, is a distribution that has constant probability".

We need to take in count that our random variable just take values between 0 and 1 since is uniform distribution (0,1). The maximum of the finite set of elements in (0,1) needs to be present in (0,1).

If we select a value x \in (0,1) we want this:

max(U_1, ....,U_n) \leq x

And we can express this like that:

u_i \leq x for each possible i

We assume that the random variable u_i are independent and P)U_i \leq x) =x from the definition of an uniform random variable between 0 and 1. So we can find the cumulative distribution like this:

P(X \leq x) = P(U_1 \leq 1, ...., U_n \leq x) \prod P(U_i \leq x) =\prod x = x^n

And then cumulative distribution would be expressed like this:

0, x \leq 0

x^n, x \in (0,1)

1, x \geq 1

For each value x\in (0,1) we can find the dendity function like this:

f_X (x) = \frac{d}{dx} F_X (x) = nx^{n-1}

So then we have the pdf defined, and given by:

f_X (x) = n x^{n-1} , x \in (0,1)  and 0 for other case

And now we can find the expected value for the random variable X like this:

E(X) =\int_{0}^1 s f_X (x) dx = \int_{0}^1 x n x^{n-1}

E(X)= n \int_{0}^1 x^n dx = n [\frac{1}{n+1}- \frac{0}{n+1}]=\frac{n}{n+1}

6 0
3 years ago
A 12-foot ladder is leaning against a building with the base of the ladder 5 feet from the building. How can you find how high o
8090 [49]

Answer:

use the therom of pythagoras

5 0
3 years ago
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