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tamaranim1 [39]

3 years ago

14

Amanda brought a 6-cup of bag of shredded cheese for $6.89. She used 2.25 cups to make lasagna and 1.25 cups to make pizza. How

much cheese is left?

1 answer:

valina [46]3 years ago

5 0

1 piece of cheese is left because 2.25-1.25 equals 1

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Zolol [24]

The Most Logical Answer is B

8 0

3 years ago

Which of the following best describes these estimates

Zolol [24]

H. they are rounded to yhe nearest 5,000 years

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3 years ago

If a certain machine makes electrical resistors having a mean resistance of 40 ohms and a standard deviation of 2 ohms, what is

V125BC [204]

Answer:

The probability is $P(\= X < 40.4) = 0.84134$

Step-by-step explanation:

From the question we are told that

The mean is $\mu =40 \ \Omega$

The standard deviation is $\sigma = 2 \ \Omega$

The sample size is n = 25

The combined resistance is $\sum x_i = 1010 \ \Omega$

Generally the sample mean is mathematically represented as

$\= x = \frac{\sum x_i }{n}$

=> $\= x = \frac{1010 }{25}$

=> $\= x = 40.4 \ \Omega$

Generally the standard error of the mean is mathematically represented as

$\sigma_{x} = \frac{\sigma }{\sqrt{n} }$

=> $\sigma_{x} = \frac{ 2 }{\sqrt{25} }$

=> $\sigma_{x} = 0.4$

Generally the probability that a random sample of 25 of these resistors will have a combined resistance of less than 1010 ohms is mathematically represented as

$P(\= X < 40.4) = P( \frac{\= X - \mu }{\sigma_{x }} < \frac{ 40.4 - 40 }{0.4} )$

$\frac{\= X -\mu}{\sigma } = Z (The \ standardized \ value\ of \ \= X )$

$P(\= X < 40.4) = P( Z < 1 )$

From the z table the area under the normal curve to the left corresponding to 1 is

$P(\= X < 40.4) = P( Z < 1 ) = 0.84134$

$P(\= X < 40.4) = 0.84134$

3 0

3 years ago

g 3. Suppose X and Y are independent, Binomial random variables, respectively, with parameters (n1, p) and (n2, p). Calculate th

solong [7]

Answer and Explanation:

Solution:

Let x and y are independent, ariables.

The parameters of x and y are (n1, p) and (n2, p), respectively.

It means the sum of the independent binomial variable is itself a binomial random variable.

Consider probability of the event [ x = n1],

Denoted by: p(x=n1)

The function:

P(n1) = p(x = n1)

Over the possible value of x say, n1, n2, n3, …, is called frequency function.

The frequency function must satisfy.

∑I p (ni) = 1,

Where the sum is possible values of x.

Similarly,

Consider probability of the event [ y = n2],

Denoted by: p(y=n2)

The function:

P (n2) = p(y = n2)

Over the possible value of y say, n1, n2, n3, …, is called frequency function.

The frequency function completely describes the probabilistic nature of the random variable.

7 0

3 years ago

Sam bought brushes for 8$ a palette for 5$ and oil paints for 15$ he paid 29.82$ all together what sale tax rate did Sam pay

Sonja [21]

Answer:

$1.82

Step-by-step explanation:

5 0

4 years ago

Read 2 more answers