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IrinaVladis [17]
3 years ago
15

Which sequence could be partially defined by the recursive formula f (n + 1) = f(n) + 2.5 for n ≥ 1?

Mathematics
1 answer:
joja [24]3 years ago
3 0

Options:

a. 2.5, 6.25, 15.625, 39.0625, …

b. 2.5, 5, 10, 20

c. -10, -7.5, -5, -2.5, …

d. -10, -25, 62.5, 156.25

Answer:

C. –10, –7.5, –5, –2.5, …

Step-by-step explanation:

Given

f(n+1) = f(n) + 2.5 where n ≥ 1

Required

Which sequence partially defines the function

This question will be solved using trial by error method (we'll check out all given options)

We'll take f(1) as the first time of each sequence because n >= 1.

A.

2.5, 6.25, 15.625, 39.0625, …

f(1) = 2.5

f(n+1) = f(n) + 2.5

So, when n = 1

f(1 + 1) = f(1) + 2.5

f(2) = 2.5 + 2.5 = 5

This doesn't define the sequence

B.

2.5, 5, 10, 20

f(1) = 2.5

So, when n = 1

f(1 + 1) = f(1) + 2.5

f(2) = 2.5 + 2.5 = 5

When n = 2

f(2 + 1) = f(2) + 2.5

f(3) = 5 + 2.5

f(3) = 7.5

This also doesn't define the sequence

C.

-10, -7.5, -5, -2.5, …

f(1) = -10

So, when n = 1

f(1 + 1) = f(1) + 2.5

f(2) = -10 + 2.5 = -7.5

When n = 2

f(2 + 1) = f(2) + 2.5

f(3) = -7.5 + 2.5 = -5

When n = 3

f(3 + 1) = f(3) + 2.5

f(4) = -5 + 2.5 = -2.5

This defines the function; so, there's no need to check D

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Answer:

a) \bar X \sim N(88,\frac{14}{\sqrt{49}}=2)  

b) P(\bar X >91.2)=1-P(\bar X

Step-by-step explanation:

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

Let X the random variable that represent the variable of interest on this case, and for this case we know the distribution for X is given by:  

X \sim N(\mu=88,\sigma=14)  

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\bar X \sim N(\mu,\frac{\sigma}{\sqrt{n}})  

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On this last step we use the complement rule.

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