A polynomial function has a zeros - 1, 2, and 7 (all multiplicity 1). Write a function in standard

What expression has a negative value? 7+3(-4)(2) -2[12/(-3)] (15-7)-(9/3) -5[7+(-14)]-30

Soloha48 [4]

Answer:

7+3(-4)(2)= -17

Step-by-step explanation:

7+3(-4)(2)

(-4)*2=-8

∴ 7+3(-8)= 7-24

= -17

8 0

3 years ago

Mary and her family ordered a large pizza for dinner. Half of the pizza is plain and half of the pizza has pepperoni. Mary loves

Julli [10]

Answer:

3/10 of the renaming full pizza has pepperoni

Step-by-step explanation:

Here, we want to calculate how much of the remaining pizza had pepperoni.

From the question, 1/2 has pepperoni and 1/2 is plain without pepperoni

she eats 1/5 of the pizza having pepperoni, this means that she is actually left with some parts is the half having pepperoni

The actual of the half remaining with pepperoni will be 1/2 - 1/5 = 3/10

This means that 3/10 of the remaining full pizza has pepperoni

7 0

3 years ago

Rick deposited $1500 in a savings account that earns 6% interest for 4 years. How much money is in his account at the end of the

AlladinOne [14]

Answer:

$5820

Step-by-step explanation:

1500*6%=90 per month

90*(48months=4yrs)=4320

1500+4320=5820

5 0

3 years ago

Simplify the expression by combining like terms 14g-6-13g-7

frozen [14]

Answer:

g-13

Step-by-step explanation:

subtract 14g from 13 making it g and add 6+7

Hope this helps :P

7 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

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