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What are two factors of the composite number 46 other than 1 and 46? A. 3 and 7 B. 2 and 13 C. 2 and 23 D. 3 and 33

riadik2000 [5.3K]

Answer:

2 and 23 are the factors of the composite number 46 .

Option (C) is correct.

Step-by-step explanation:

As given in the question 46 is a composite number .

Definition of factors

"Factors" are the numbers you multiply to get another number.

As 46 is written as

46 = 2 × 23

Therefore the factors of the composite numbers 46 are 2 and 23 .

6 0

3 years ago

Read 2 more answers

What is the slope-intercept form of the equation of the line with a slope of 3 and y-intercept at the origin (0,0)?

Viktor [21]

Answer:

Step-by-step explanation:

y=3x

8 0

2 years ago

A scientist needs 4.8 liters of a 12% alcohol solution. She has available a 22% and a 10% solution. How many liters of the 22% a

yanalaym [24]

Answer:

4.0 L of 10%
0.8 L of 22%

Step-by-step explanation:

For mixture problems, it is convenient to define a variable to represent the amount of the greatest contributor. Let x represent the amount of 22% solution in the mix. Then 4.8-x is the amount of 10% solution.

The amount of alcohol in the mix is ...

0.22x +0.10(4.8-x) = 0.12(4.8)

Eliminating parentheses, we have ...

0.22x -0.10x +0.10(4.8) = 0.12(4.8)

Subtracting (0.10)(4.8) and combining x-terms gives ...

0.12x = 0.02(4.8)

x = (0.02/0.12)(4.8) = 0.8 . . . . . divide by the x-coefficient

The scientist needs 0.8 L of 22% solution and 4.0 L of 10% solution.

5 0

3 years ago

Given a population with a mean of muμequals=100100 and a variance of sigma squaredσ2equals=3636, the central limit theorem appl

lakkis [162]

Answer:

a) $\bar X \sim N(100,\frac{6}{\sqrt{25}}=1.2)$

$\mu_{\bar X}=100$ $\sigma^2_{\bar X}=1$

b) $P(\bar X >101)=1-P(\bar X$

c) $P(\bar X$

Step-by-step explanation:

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".

The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".

The complement rule is a theorem that provides a connection between the probability of an event and the probability of the complement of the event. Lat A the event of interest and A' the complement. The rule is defined by: $P(A)+P(A') =1$