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3 years ago

8

An isosceles triangle has an angle that measures 48°. Which other angles could be in that isosceles triangle? Choose all that ap

ply.

1 answer:

WITCHER [35]3 years ago

7 0

48°, 84°, 66°
i think lol

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PLEASEEE HELP ASAP ITS IMPORTANT !!!!

Orlov [11]

Answer:

The rate of change in the table is 78 students per year

Step-by-step explanation:

Here, we want to get the rate of change of the function

Mathematically, we can get this by using the formula;

m = (y2-y1)/(x2-x1)

(x1,y1) and (x2,y2) are points we are to select from the table

Let us select the point (4,704) and (17,1718) from the table

The slope is thus;

m = (1718-704)/(17-4) = 78 students per year

8 0

3 years ago

In a set of real numbers, the first number less then -3 is -4<br> true <br> or<br> false

Annette [7]

Answer:

true

Step-by-step explanation:

the bigger the number of a negative integer, the smaller the value become

8 0

4 years ago

Read 2 more answers

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notsponge [240]

Answer:

c

Step-by-step explanation:

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3 years ago

Factor 7a^2 + 26a + 15

sleet_krkn [62]

Answer:

(7a + 5) (a + 3)

Step-by-step explanation:

We can factor by grouping which leaves us with (7a + 5) (a + 3) as the answer.

7 0

2 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, $0.9664 \times 100 = 96.64$

8 0

3 years ago