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r-ruslan [8.4K]
3 years ago
13

For the following​ sequence, determine if it is an arithmetic​sequence, a geometric​ sequence, or neither. If it is either arith

metic or​ geometric, give the next term in the sequence.
5​, 8​,13​, 21​, 34​, 55,...
Mathematics
1 answer:
uysha [10]3 years ago
4 0

Answer: An arithmetic sequence is of the form:

aₙ = a₀ + (n -1)*d

So the difference between a term and the nextone is always the same, and in our case the numbers are 5,8,13,21,34,55.

The distance between the numbers is not constant, so this is not a arithmetic sequence.

a geometric sequence is of the form: aₙ = a*rⁿ

So the therms grow exponentially.

here a₀ = a*r⁰ = a = 5.

a₁ = a*r¹ = 5*r = 13, then r = 13/5 = 2.6

a₂ = a*r² = 5*2.6*2.6 = 33.8, so here we can see that this is not our series, so our series isn't geometric.

So the answer for the first part is neither.

Now, the sequence is  5,8,13,21,34,55, ...

you can see that

5 + 8 = 13

13 + 8 = 21,

21 + 34 = 55

So our sequence is of the form: aₙ = aₙ₋₁ + aₙ₋₂.

then, the next term will be: 55 + 21= 76.

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Answer:

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Step-by-step explanation:

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Always start with you X coordinate. Here is a tip to help you (X, Y)

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A set of bivariate data was used to create a least squares regression line. Which of the following is minimized by the line
77julia77 [94]

Answer:

B) The sum of the squared residuals

Step-by-step explanation:

Least Square Regression Line is drawn through a bivariate data(Data in two variables) plotted on a graph to explain the relation between the explanatory variable(x) and the response variable(y).

Not all the points will lie on the Least Square Regression Line in all cases. Some points will be above line and some points will be below the line. The vertical distance between the points and the line is known as residual. Since, some points are above the line and some are below, the sum of residuals is always zero for a Least Square Regression Line.

Since, we want to minimize the overall error(residual) so that our line is as close to the points as possible, considering the sum of residuals wont be helpful as it will always be zero. So we square the residuals first and them sum them. This always gives a positive value. The Least Square Regression Line minimizes this sum of residuals and the result is a line of Best Fit for the bivariate data.

Therefore, option B gives the correct answer.

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9514 1404 393

Answer:

  G

Step-by-step explanation:

The one point with a y-value of 0 is the one on the x-axis: G.

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