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3 years ago

What is m∠8 if m∠4 = 50°?

Mathematics

1 answer:

r-ruslan [8.4K]3 years ago

6 0

Answer:

D) 50°. You got parallel lines cut by a traversal, so angles 4 and 8 are congruent because they're corresponding angles.

Step-by-step explanation:

You might be interested in

Every month, $50 is withdrawn from Tom's savings account to pay for his gym membership. He has enough savings to withdraw no mor

ella [17]

Answer:

200 divided by 50= 4 so 4 months

Step-by-step explanation:

4 0

3 years ago

How much must you add to -12 to get greater than 5

pav-90 [236]

At least 15 b/c is you add QWERTY to -12, you get zero and adding 5 to get 15 in total.

7 0

3 years ago

Calculate the number of ways to form a set of three distinct items such that no two of the selected items are in the same row or

Eddi Din [679]

Answer:

1200

Explanation:

Order does not matter, if we said xyz order, it would still not make a difference if it was zyx or yzx hence we use the combination formula:

nCr = n! / r! * (n - r)!

where n= total number of items

r= number of items chosen at a time

Combinations are used when the order of events do not matter in calculating the outcome.

We calculate using the formula:

(30×20×12)÷3!=1200

There are therefore 1200 ways for the three distinct items to not be in same row or column

8 0

3 years ago

Mrs Lee used 6 Meters of material to make 3 dresses. She used 4 ties as much material for a curtain as for a dress. How much mat

bulgar [2K]

Answer:

for each dress she used 6/3 of material

then for a curtain =2x4=8 materials

3 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

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, $0.9664 \times 100 = 96.64$

8 0

3 years ago