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Liono4ka [1.6K]
3 years ago
9

Which formula can be used to find the nth term of the following geometric sequence? − 2/9 , 2/3 , −2, 6,…

Mathematics
2 answers:
Alika [10]3 years ago
8 0

Answer:

aₙ = (-1)ⁿ2(3)ⁿ⁻³

Step-by-step explanation:

The formula for the nth term of a geometric sequence is

aₙ = a₁rⁿ⁻¹

We can get the value for r by dividing aₙ by aₙ₋₁.

a₄/a₃ = 6 ÷ (-2)

a₄/a₃ = -3

r = -3

=====

a₁ = -2/9

aₙ = (-2/9)(-3)ⁿ⁻¹

aₙ = (-2/3²)(-3)ⁿ⁻¹              Factor out the 3s from the 9

aₙ = -2(3²)(-1)ⁿ⁻¹(3)ⁿ⁻¹        Factor -1 from  and -3

aₙ  = (-1)(2)(-1)ⁿ⁻¹(3)ⁿ⁻³      Combine like terms

aₙ = 2(-1)ⁿ⁻²(3)ⁿ⁻³             Multiply the -1 term by (-1)² (=1)

aₙ = (-1)ⁿ2(3)ⁿ⁻³

The (-1)ⁿ term makes the terms alternately positive and negative.

alexandr1967 [171]3 years ago
3 0

Answer:

a_n=-\frac{2}{9}(-3)^{n-1}

Step-by-step explanation:

Since, the nth term of a geometric sequence is,

a_n=ar^{n-1}

Where, a is the first term of the sequence,

r is the common ratio,

Here, the given geometric sequence is,

-\frac{2}{9},\frac{2}{3},-2,6........

Thus, the first term of the sequence is,

a=-\frac{2}{9}

And, the common ratio,

r=\frac{2/3}{-2/9}=-3

Hence, the nth term of the sequence is,

a_n=-\frac{2}{9}(-3)^{n-1}

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A data mining routine has been applied to a transaction dataset and has classified 88 records as fraudulent (30 correctly so) an
chubhunter [2.5K]

Answer:

The classification matrix is attached below

Part a

The classification error rate for the records those are truly fraudulent is 65.91%.

Part b

The classification error rate for records that are truly non-fraudulent is 96.64%

Step-by-step explanation:

The classification matrix is obtained as shown below:

The transaction dataset has 30 fraudulent correctly classified records out of 88 records, that is, 30 records are correctly predicted given that an instance is negative.

Also, there would be 88 - 30 = 58 non-fraudulent incorrectly classified records, that is, 58 records are incorrectly predicted given that an instance is positive.

The transaction dataset has 920 non-fraudulent correctly classified records out of 952 records, that is, 920 records are correctly predicted given that an instance is positive.

Also, there would be 952 - 920 = 32 fraudulent incorrectly classified records, that is, 32 records incorrectly predicted given that an instance is negative.

That is,

                                                                            Predicted value

                           Active value                 Fraudulent       Non-fraudulent

                              Fraudlent                         30                       58

                          non-fraudulent                   32                     920

The classification matrix is obtained by using the information related to the transaction data, which is classified into fraudulent records and non-fraudulent records.

The error rate is obtained as shown below:

The error rate is obtained by taking the ratio of \left( {b + c} \right)(b+c) and the total number of records.

The classification matrix is, shown above

The total number of records is, 30 + 58 + 32 + 920 = 1,040

The error rate is,

\begin{array}{c}\\{\rm{Error}}\,{\rm{rate}} = \frac{{b + c}}{{{\rm{Total}}}}\\\\ = \frac{{58 + 32}}{{1,040}}\\\\ = \frac{{90}}{{1,040}}\\\\ = 0.0865\\\end{array}  

The percentage is 0.0865 \times 100 = 8.65

(a)

The classification error rate for the records those are truly fraudulent is obtained by taking the rate ratio of b and \left( {a + b} \right)(a+b) .

The classification error rate for the records those are truly fraudulent is obtained as shown below:

The classification matrix is, shown above and in the attachment

The error rate for truly fraudulent is,

\begin{array}{c}\\FP = \frac{b}{{a + b}}\\\\ = \frac{{58}}{{30 + 58}}\\\\ = \frac{{58}}{{88}}\\\\ = 0.6591\\\end{array}  

The percentage is, 0.6591 \times 100 = 65.91

(b)

The classification error rate for records that are truly non-fraudulent is obtained by taking the ratio of d and \left( {c + d} \right)(c+d) .

The classification error rate for records that are truly non-fraudulent is obtained as shown below:

The classification matrix is, shown in the attachment

The error rate for truly non-fraudulent is,

\begin{array}{c}\\TP = \frac{d}{{c + d}}\\\\ = \frac{{920}}{{32 + 920}}\\\\ = \frac{{920}}{{952}}\\\\ = 0.9664\\\end{array}

The percentage is, 0.9664 \times 100 = 96.64

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sladkih [1.3K]

Answer:

C. Events E and A are independent

Step-by-step explanation:

we will verify each options

(a)

We can use independent events formula

P(B∩C)=P(B)*P(C)

we are given

P(B)=0.4

P(C)=0.25

P(B∩C)=0.05

now, we can plug these values into formula

and we get

0.05=0.4*0.25

0.05=0.1

we can see that left side is not equal to right side

so, this is FALSE

(b)

We can use independent events formula

P(D∩A)=P(D)*P(A)

we are given

P(D)=0.25

P(A)=0.6

P(D∩A)=0.1

now, we can plug these values into formula

and we get

0.1=0.25*0.6

0.1=0.15

we can see that left side is not equal to right side

so, this is FALSE

(c)

We can use independent events formula

P(E∩A)=P(E)*P(A)

we are given

P(E)=0.5

P(A)=0.6

P(E∩A)=0.3

now, we can plug these values into formula

and we get

0.3=0.5*0.6

0.3=0.3

we can see that both sides are equal

so, this is TRUE

(d)

We can use independent events formula

P(D∩B)=P(D)*P(B)

we are given

P(D)=0.25

P(B)=0.4

P(D∩A)=0.15

now, we can plug these values into formula

and we get

0.15=0.25*0.4

0.15=0.1

we can see that left side is not equal to right side

so, this is FALSE

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3 years ago
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