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3 years ago

Which inequality is true?

Mathematics

2 answers:

djyliett [7]3 years ago

7 0

Answer:

B is correct

Step-by-step explanation:

These are Absolute Value expressions. (How far from 0 on the number line) So disregard negative or positive signs. The only true statement would be 2<4.

Alja [10]3 years ago

3 0

Answer:

Step-by-step explanation:

B because the little lines that the numbers are between means absolute vale which means even if its negative its still positive as long as the negative sign is in the little lines. b= 2<4 which is correct

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Sonbull [250]

it is actually $3,694.48

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2 years ago

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Vlad [161]

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6 0

3 years ago

What is the area of the composite figure

lara [203]

Answer:

Area of composite figure = 216 cm²

Hence, option A is correct.

Step-by-step explanation:

The composite figure consists of two figures.

1) Rectangle

2) Right-angled Triangle

We need to determine the area of the composite figure, so we need to find the area of an individual figure.

Determining the area of the rectangle:

Given

Length l = 14 cm

Width w = 12 cm

Using the formula to determine the area of the rectangle:

A = wl

substituting l = 14 and w = 12

A = (12)(14)

A = 168 cm²

Determining the area of the right-triangle:

Given

Base b = 8 cm

Height h = 12 cm

Using the formula to determine the area of the right-triangle:

A = 1/2 × b × h

A = 1/2 × 8 × 12

A = 4 × 12

A = 48 cm²

Thus, the area of the figure is:

Area of composite figure = Rectangle Area + Right-triangle Area

= 168 cm² + 48 cm²

= 216 cm²

Therefore,

Area of composite figure = 216 cm²

Hence, option A is correct.

4 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

How many 1/3 are in 5 2/3?

tiny-mole [99]

Answer

I think that there's 5 but i'm probably wrong so I wouldn't use my answer if I were you

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3 years ago