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1 year ago

identify the special product by writing the letter of the answer on the space provided ( question in photo number 1 )

Mathematics

1 answer:

Taya2010 [7]1 year ago

8 0

Question:

Solution:

Note that :

$x^2+9x\text{ + 20 = 1(x+4)(x+5)}$

where the left hand of the equation represents a trinomial and the right hand of the equation represents a product of two binomials. Then, we can conclude that the correct answer is:

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(x+3)(x-5)=(x+3)(x−5)=

s344n2d4d5 [400]

Answer:

(x+3)(x-5)=(x+3)(x-5)

x^2-2 = x^2-2

Step-by-step explanation:

3 0

3 years ago

Read 2 more answers

On the unit circle, where I <0<2, when is tan 0 undefined?

Ilya [14]

Answer:

can you please be more specific? Maybe add more info.

Step-by-step explanation:

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3 years ago

What was the median number of posts made?

Svetradugi [14.3K]

Answer: The median number of posts made is 8.5

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2 years ago

4. What are the terms in the expression, 9- 4 + 3b + 7a?

Elan Coil [88]

Answer:

The terms are 9, 4, 3b and 7a.

The terms are 9 – 4 and 3b + 7a.

The terms are 9, -4, 3b, and 7a.

Step-by-step explanation:

7 0

3 years ago

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

Firlakuza [10]

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, $0.9664 \times 100 = 96.64$

6 0

3 years ago