Basic Question please help

What is an Algorithm? *

barxatty [35]

Answer:

a

Explanation:

4 0

4 years ago

A centimeter is Viooth of a meter, while a kilo-

tatuchka [14]

Answer:

b. False

Explanation:

A centimeter is a hundredth of a meter. This means 1/100 ,1 cm = 0.01 m

A kilo is 1000 grams.

The first answer is false because a hundredth isnot written well.

The second statement is false because the comparison given is of different units of measure. For distance is meters where as for weight is kilograms.

8 0

4 years ago

Find: factor of safety (n)for point A and B by using both MSS and DE (you can neglect shear stress due to shear force and also n

gladu [14]

Answer:

Hello your question is incomplete attached below is the complete question

Answer : Factor of safety for point A :

i) using MSS

(Fos)MSS = 3.22

ii) using DE

(Fos)DE = 3.27

Factor of safety for point B

i) using MSS

(Fos)MSS = 3.04

ii) using DE

(Fos)DE = 3.604

Explanation:

Factor of safety for point A :

i) using MSS

(Fos)MSS = 3.22

ii) using DE

(Fos)DE = 3.27

Factor of safety for point B

i) using MSS

(Fos)MSS = 3.04

ii) using DE

(Fos)DE = 3.604

Attached below is the detailed solution

8 0

3 years ago

Steam at 1 MPa, 300 C flows through a 30 cm diameter pipe with an average velocity of 10 m/s. The mass flow rate of this steam i

stealth61 [152]

Answer:

$\dot m = 2.74 kg/s$

Explanation:

given data:

pressure 1 MPa

diameter of pipe = 30 cm

average velocity = 10 m/s

area of pipe $= \frac[\pi}{4}d^2$

$= \frac{\pi}{4} 0.3^2$

A = 0.070 m2

WE KNOW THAT mass flow rate is given as

$\dot m = \rho A v$

for pressure 1 MPa, the density of steam is = 4.068 kg/m3

therefore we have

$\dot m = 4.068 * 0.070* 10$

$\dot m = 2.74 kg/s$

7 0

4 years ago

A continuous random variable, X, whose probability density function is given by f(x) = ( λe−λx , if x ≥ 0 0, otherwise is said t

Ganezh [65]

Answer:

a) $F(x) = \lambda \int_0^{\infty} e^{-\lambda x} dx= -e^{-\lambda x} \Big|_0^{\infty} = 1- e^{-\lambda x} \$

b) $P(10 < X$

Explanation:

Previous concepts

The cumulative distribution function (CDF) F(x),"describes the probability that a random variableX with a given probability distribution will be found at a value less than or equal to x".

The exponential distribution is "the probability distribution of the time between events in a Poisson process (a process in which events occur continuously and independently at a constant average rate). It is a particular case of the gamma distribution".

Part a

Let X the random variable of interest. We know on this case that $X\sim Exp(\lambda)$