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3 years ago

If I flip a coin, what is the probability of getting both heads?

Mathematics

2 answers:

Andrew [12]3 years ago

4 0

50% chance of getting heads or 25%

Alexus [3.1K]3 years ago

3 0

The answer depends on how many times you flipped it.
If you flipped the coin once, the probability is zero. No coin has 2 heads.

If you flipped the coin twice, the probability is 1 out of 4. This is because there are four possible outcomes when you flip a coin twice (both heads, both tails, first time head second time tail, first time tail second time head). Hope this helps!

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What is 1% of 3,000?

gladu [14]

Answer:

Step-by-step explanation:

As 1 percent is equal to 1/100

1/100 of 3000 (1/100 x 3000)

equals to...

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2 years ago

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Solve the inequality.<br><br> 2(4x – 3) ≥ –3(3x) + 5x?

slega [8]

2(4x-3) ≥ - 3(3x) + 5x

2* 4x - 2*3 ≥ - 9x + 5x

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x = 6/12 divide 6/6 = 1 and 12 / 6 = 2

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hope this helps!

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3 years ago

How many U.S. quarts are contained in 12 L? A. 13.638 qt B. 11.352 qt C. 10.56 qt D. 12.684 qt

adoni [48]

(12)1.057=12.684

The answer is D. 12.684qt

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3 years ago

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Pythagorean theorem digital escape can you find the length and area and type the correct code?

lara31 [8.8K]

Answer:

13825

Step-by-step explanation:

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2 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