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wariber [46]
3 years ago
11

The first four terms of a sequence are shown below: 9, 5, 1, â’3 Which of the following functions best defines this sequence? f(

1) = 9, f(n 1) = f(n) ⒠4; for n ≥ 1 f(1) = 9, f(n 1) = f(n) 4; for n ≥ 1 f(1) = 9, f(n 1) = f(n) ⒠5; for n ≥ 1 f(1) = 9, f(n 1) = f(n) 5; for n ≥ 1.
Mathematics
2 answers:
matrenka [14]3 years ago
5 0

The function that best defines this arithmetic sequence is:

  • f(n) = f(n - 1) - 4, f(1) = 9

<h3>What is an arithmetic sequence?</h3>
  • In an <em>arithmetic sequence,</em> the <u>difference between consecutive terms is always the same</u>, called common difference d.

The recursive function that defines the sequence is:

f(n) = f(n - 1) + d

f(1) = f_0

  • In which f_0 is the first term.

In this problem, the sequence is: {9, 5, 1, -3}

  • Each term is the previous term subtracted by 4, hence d = -4.
  • The first term is 9, hence f_0 = 9.

Hence, the function is:

  • f(n) = f(n - 1) - 4, f(1) = 9

You can learn more about arithmetic sequences at brainly.com/question/6561461

dem82 [27]3 years ago
5 0

To identify the functions which best define the given sequence, we have to identify the number pattern.

The correct option is (a).

Given:

The given sequence is as follows,

9,5,1,-3

<h3>What is an arithmetic sequence?</h3>

The arithmetic sequence define as the difference between two consecutive number would be same.

If we subtract 4 form each digit we will get next digit.

9-4=5\\5-4=1\\1-4=-3

So the function would be,

f(1) = 9, f(n + 1) = f(n) -4; \rm for \:n\geq 1

The option (b) and (d) show the increasing function whereas we have decreasing sequence.

Thus, the correct option is (a).

Learn more about arithmetic sequence here

:brainly.com/question/12637273

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Answer:

(a) See below

(b) r = 0.9879  

(c) y = -12.629 + 0.0654x

(d) See below

(e) No.

Step-by-step explanation:

(a) Plot the data

I used Excel to plot your data and got the graph in Fig 1 below.

(b) Correlation coefficient

One formula for the correlation coefficient is  

r = \dfrac{\sum{xy} - \sum{x} \sum{y}}{\sqrt{\left [n\sum{x}^{2}-\left (\sum{x}\right )^{2}\right]\left [n\sum{y}^{2} -\left (\sum{y}\right )^{2}\right]}}

The calculation is not difficult, but it is tedious.

(i) Calculate the intermediate numbers

We can display them in a table.

<u>    x   </u>    <u>      y     </u>   <u>       xy     </u>    <u>              x²    </u>   <u>       y²    </u>

   36       0.22              7.92               1296           0.05

   67        0.62            42.21              4489           0.40

   93         1.00            93.00           20164           3.46

 433        11.8          5699.4          233289        139.24

 887      29.3         25989.1          786769       858.49

1785      82.0        146370          3186225      6724

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9965   537.81     1545776.75  25569715   95799.63

(ii) Calculate the correlation coefficient

r = \dfrac{\sum{xy} - \sum{x} \sum{y}}{\sqrt{\left [n\sum{x}^{2}-\left (\sum{x}\right )^{2}\right]\left [n\sum{y}^{2} -\left (\sum{y}\right )^{2}\right]}}\\\\= \dfrac{9\times 1545776.75 - 9965\times 537.81}{\sqrt{[9\times 25569715 -9965^{2}][9\times 95799.63 - 537.81^{2}]}} \approx \mathbf{0.9879}

(c) Regression line

The equation for the regression line is

y = a + bx where

a = \dfrac{\sum y \sum x^{2} - \sum x \sum xy}{n\sum x^{2}- \left (\sum x\right )^{2}}\\\\= \dfrac{537.81\times 25569715 - 9965 \times 1545776.75}{9\times 25569715 - 9965^{2}} \approx \mathbf{-12.629}\\\\b = \dfrac{n \sum xy  - \sum x \sum y}{n\sum x^{2}- \left (\sum x\right )^{2}} -  \dfrac{9\times 1545776.75  - 9965 \times 537.81}{9\times 25569715 - 9965^{2}} \approx\mathbf{0.0654}\\\\\\\text{The equation for the regression line is $\large \boxed{\mathbf{y = -12.629 + 0.0654x}}$}

(d) Residuals

Insert the values of x into the regression equation to get the estimated values of y.

Then take the difference between the actual and estimated values to get the residuals.

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    36        0.22        -10                 10

    67        0.62          -8                  9

    93        1.00           -7                  8

   142        1.86           -3                  5

  433       11.8             19               -  7

  887     29.3             45               -16  

 1785     82.0            104              -22

2797    163.0            170               -  7

3675   248.0            228               20

(e) Suitability of regression line

A linear model would have the residuals scattered randomly above and below a horizontal line.

Instead, they appear to lie along a parabola (Fig. 2).

This suggests that linear regression is not a good model for the data.

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