True or false, the fractions ½ and ¾ are equivalent.<br><br> True<br><br> False

Find the first six terms of the sequence. a1=-7, an=2*an-1

AlladinOne [14]

The first term of the sequence is $a_{1} = -7$ .

The sequences is recursive, every next term of the sequence is obtained by multiplying the previous term by 2.

So, we can find the next 6 terms as shown below:

$a_{2} = 2*a_{1}=2(-7)=-14$
$a_{3} = 2(-14)=-28$
$a_{4} = 2(-28)=-56$
$a_{5} = 2(-56)=-112$
$a_{6} = 2(-112)=-224$
$a_{7} = 2(-224)=-448$

3 0

3 years ago

What is the value of 166

Lyrx [107]

what is the equation?

6 0

3 years ago

Find the number of ordered pairs $(m,n)$ of integers that satisfy \[mn = 2m + 4n.\] <img src="https://tex.z-dn.net/?f=Find%20the

Anna35 [415]

There are four pairs (m,n) that work: (5,10), (6,6), (8,4), and (12,3).

5 0

3 years ago

Solve for the missing length

Fofino [41]

Answer:

2 * sqrt(13)

Step-by-step explanation:

Use pythagoras' theorem.

4^2 + 6^2 = x^2 = 16 + 36 = 52 = 4*13

x = sqrt(4*13)=2 * sqrt(13)

6 0

2 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: $P(A)+P(A') =1$