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3 years ago

5(x+1)=3x-9 find x (please it’s due today)

Mathematics

2 answers:

saveliy_v [14]3 years ago

7 0

Answer:

Step-by-step explanation:

$5(x+1)=3x-9$

<u>Expand the terms in the bracket</u>

That's

$5x + 5 = 3x - 9$

Subtract 3x from both sides of the equation

$5x - 3x + 5 = 3x - 3x - 9 \\ 2x + 5 = - 9$

Subtract 5 from both sides of the equation

$2x + 5 - 5 = - 9 - 5 \\ 2x = - 14$

Divide both sides by 2

$\frac{2x}{2} = - \frac{14}{2} \\$

We have the final answer as

Hope this helps you

Yanka [14]3 years ago

5 0

5(x+1)=3x−9

Step 1: Simplify both sides of the equation.

5(x+1)=3x−9

(5)(x)+(5)(1)=3x+−9(Distribute)

5x+5=3x+−9

5x+5=3x−9

Step 2: Subtract 3x from both sides.

5x+5−3x=3x−9−3x

2x+5=−9

Step 3: Subtract 5 from both sides.

2x+5−5=−9−5

2x=−14

Step 4: Divide both sides by 2.

−14

x=−7

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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 $0.0865 \times 100 = 8.65$

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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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