On average how fast did he drive in hours per mile

Mr. O'Connor had $90 to spend at a football game. He spent 31/45 on his ticket. Then, he spent 3/7 of his remaining money at the

Kobotan [32]

He has $90 in total to spend

He spent (31/45) of this money on ticket

so money spent on ticket =(31/45) *90 =2790/45 =62

Now money left with him is 90-62 =28 $

Now it says that he spent (3/7) of this remaining money at concession stand

so money spent on concession stand =(3/7)*28 =84/7 =12

Now money left with him = 28-12 =16

So money left with him is $16 but hat costs for $18

So Mr. O'Connor didnt have enough moeny to buy the hat

4 0

3 years ago

1) A bag contains six red marbles and eight blue marbles. You randomly pick a marble and then pick a second marble without retur

Elena-2011 [213]

Answer:

You would have 4 red marbles and 8 blue marbles since you didn't return the ones you took out to the bag.

Step-by-step explanation:

6 - 2 = 4

The 8 would stay the same

There for you would have 4 red marbles and 8 blue marbles left in the bag. Totalled you have 12 marbles in the bag and 2 out of the bag. (8 + 4 = 12)

Including the marbles in and out of the bag you have 14 marbles. The 12 inside the bag + the 2 outside of the bag = 14 marbles totalled.

5 0

3 years ago

Apply the equation found in question 1 to compute the time it would take to cook a 12-pound beef roast. from USAtestprep

Advocard [28]

Step-by-step explanation:

Ermm you're gonna have to write the entire question here for assistance

7 0

3 years ago

Evaluate the expression. 5 - (4 - 2 - (6-6)) 8 2 5 3

Doss [256]

Answer: 3

Step-by-step explanation:

5−(4−2−(6−6))

=3

3 0

3 years ago

Read 2 more answers

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