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AlexFokin [52]
3 years ago
7

The product of a number and 9, increased by 4, is 58. Find the number.

Mathematics
1 answer:
diamong [38]3 years ago
4 0
9x + 4 = 58
9x = 54
x = 6

The number is 6.
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What is the solution to the equation Negative 3 (h + 5) + 2 = 4 (h + 6) minus 9?
ANTONII [103]

Answer:

h=-4

Step-by-step explanation:

Use the Distributive law:

1. -3(h+5) gives us -3h-15 then you add your two(2).

the whole of your left side of the equation will give you -3h-13

2. 4(h+6) gives us 4h+24 then you subtract nine(9).

the whole of the right side of the equation will give you 4h+15

3. equate the two like this:

-3h-13=4h+15

4. group like terms of each side:

-13-15=4h+3h

5. Add and Subt<u>r</u>act

-28=7h

6. Decide both sides by 7 to make h the subject of the formula

7. Congratulations you got your answer as h=-4

5 0
2 years ago
Lexi is making an orange drink for party. She puts 4 pints of orange drink and 2 pints of water into a punch bowl. Her friend Ol
Nina [5.8K]
I think she can add 5p more of orange which is 9p and 6p more water which is 8p. I'm pretty sure this works because 9 is the next closest multiple of 3 and 8 is one number less than that. Hope this helps!
8 0
3 years ago
Which of the following is closest to 27.8 × 9.6? A. 280 B. 300 C. 2,800 D. 3,000
Alisiya [41]
27.8 × 9.6 is equal to <span>266.88</span><span>
So, the closest would be A. 280.</span>
3 0
3 years ago
Read 2 more answers
A system of linear inequalities is shown below:
Yuki888 [10]
The 4th selection is appropriate:
  The first line is a dashed line which joins ordered pairs (-2, 1) and (3, 1). The second line is a dashed line and joins ordered pairs (-2, -2) and (3, 3). The portion common above the first line and above the second line is shaded

6 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
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