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3 years ago

Can you please help me!!

Mathematics

1 answer:

STALIN [3.7K]3 years ago

7 0

Answer:
1. -8,
2. 58
3. 0
4.
5. -14/8 or -1.75

Explanation:
1. You just plug in 8 for x into the g(x) equation
So, -(8)^2+7(8)= -8

2. You do the same thing you did for number one.
Plug in 13 for the h(x) equation
|2-4(13)|= 50
Then plug in -1 for the f(x) equation
-(-1)^2+7(-1)= -8
You then do as it says- subtract h(x) from f(x)
Which is 50-(-8) and you get 58.
(That subtraction sign turns into addition/ 2 negatives make a positive)

3. Same thing again.
Plug in what they gave you (3y-1) for X
8(3y-1)-9
You get 24y-17
You then subtract off the -17 and get 24y=17
Divide the 24 off and get y=17/24
Plug the Y back into the equation
8(3(17/24)-1)-9 and you get 0

*Im not a hundred percent sure I did this one right*

4. This is the same as number 3. Didn’t have time to solve this, but it’s the same steps as number 3 :)

5. This time they didn’t give you x so you set it up differently
Now you set the f(x) equation equal to -23
8x-9=-23
Add off the to the 23 to isolate the variable
8x= -14
Divide off the 8
X= -14/8 or -1.75

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5/6 - 1/5 = 19/30 = 0.633

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3 years ago

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Please help.........

Alex

Answer:

- $\frac{61}{11}$

Step-by-step explanation:

$\frac{x}{y}$ = $\frac{5}{6}$ ( cross- multiply )

6x = 5y ( divide both sides by 6 )

x = $\frac{5}{6}$ y

Then

$\frac{5x+6y}{5x-6y}$ ← substitute x = $\frac{5}{6}$ y into the expression

= $\frac{\frac{25}{6}y+6y }{\frac{25}{6}y-6y }$

= $\frac{\frac{61}{6}y }{-\frac{11}{6}y }$

= $\frac{\frac{61}{6} }{-\frac{11}{6} }$

= $\frac{61}{6}$ × - $\frac{6}{11}$

= - $\frac{61}{11}$

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3 years ago

How many ways can 7 books be arranged on a shelf 5 at a time?

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C
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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, $0.9664 \times 100 = 96.64$

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3 years ago