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fenix001 [56]
3 years ago
5

The first term of a geometric sequence is 4 and the multiplier, or ratio, is 2. what is the sum of the first 5 terms of the sequ

ence?
a. 44
b. 64
c. 44
d. 64
Mathematics
1 answer:
miss Akunina [59]3 years ago
5 0
Hi,
There must have a bug on the site !!!


LoveYourselfFirst deleted your answer to the question 
"Hello,Answer 
=1244+8+16+32+64=124 6 minutes ago"
<span>The first term of a geometric sequence is 4 and the multiplier, or ratio, is 2. what is the sum of the first 5 terms of the sequence?
</span>
a_1=4\\&#10;a_2=2*a_1=8\\&#10;a_3=2*a_2=2^2*a_1=4*4=16\\&#10;a_4=2^3*a_1=8*4=32\\&#10;a_5=2^4*a_1=16*4=64\\&#10;&#10;4+8+16+32+64=124&#10;&#10;

<span />


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Answer:

The standard deviation for the mean weigth of Salmon is 2/3 lbs for restaurants, 2/7 lbs for grocery stores and 1/4 lbs for discount order stores.

Step-by-step explanation:

The mean sample of the sum of n random variables is

\overline{X} = \frac{X_1+X_2+...+X_n}{n}

If X_1, ..., X_n are indentically distributed and independent, like in the situation of the problem, then the variance of X_1 + .... + X_n will be the sum of the variances, in other words, it will be n times the variance of X_1 .

However if we multiply this mean by 1/n (in other words, divide by n), then we have to divide the variance by 1/n², thus \overkine{X} = \frac{V(X_1)}{n} and as a result, the standard deviation of \overline{X} is the standard deviation of X_1 divided by \sqrt{n} .

Since the standard deviation of the weigth of a Salmon is 2 lbs, then the standard deviations for the mean weigth will be:

  • Restaurants: We have boxes with 9 salmon each, so it will be \frac{2}{\sqrt{9}} = \frac{2}{3}
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We conclude that de standard deivation of the mean weigth of salmon of the types of shipment given is: 2/3 lbs for restaurants, 2/7 lbs for grocery stores and 1/4 lbs for discount outlet stores.

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The measures of two sides of ΔOPQ are proportional to the measure of two side of ΔRST and their included angles are congruent.  The triangles are similar by SAS Similarity.

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