What is the y-intercept?

Could someone please help me with this?

Artyom0805 [142]

Answer:

22.5 batches

Step-by-step explanation:

6 0

3 years ago

Read 2 more answers

One day you bike for 30 miles 10 cool down at a rate of 0.25 miles/minute. The total distance you bike is 35 miles. How many min

tatyana61 [14]

To determine the length of time it would took for the biker to cool down, we need the rate that would relate the distance he traveled to cool down per units of time. For this problem, the rate is given as 0.25 miles per minute. So, we simply divide the total distance he traveled with this rate. We calculate as follows:

time to cool down = distance / rate
time to cool down = 35 miles / 0.25 miles / minute
time to cool down = 140 minutes or 2 hrs and 20 minutes

Therefore, the biker would need to cool down for about 2 hrs and 20 minutes if he traveled for 35 miles.

8 0

3 years ago

Solve for x<br> Show work

SashulF [63]

Answer:

x = 7

Step-by-step explanation:

123 = 17x + 4

123 - 4 = 17x

119 = 17x

119 / 17 = x

7 = x

3 0

2 years ago

Solve the inequality for x. Show each step of the solution.10x > 2(8x – 3) – 18

Luden [163]

Answer: x<4

Given the inequality:

$10x>2(8x-3)-18$

We want to solve the inequality for x.

First, distribute the bracket on the right side of the inequality.

$\begin{gathered} 10x>2(8x)-2(3)-18 \\ 10x>16x-6-18 \\ 10x>16x-24 \end{gathered}$

Next, subtract 10x from both sides of the inequality.

$\begin{gathered} 10x-10x>16x-10x-24 \\ 0>6x-24 \end{gathered}$

Add 24 to both sides of the inequality.

$\begin{gathered} 0+24>6x-24+24 \\ 24>6x \end{gathered}$

Divide both sides of the inequality by 6.

$\begin{gathered} \frac{24}{6}>\frac{6x}{6} \\ 4>x \\ \implies x$

The solution to the inequality is x<4.

6 0

1 year 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

2 years ago