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3 years ago

Each of the 6 cats in a pet store was weighed. Here are their weights (in pounds):

Mathematics

2 answers:

loris [4]3 years ago

4 0

Answer:

mean 10 median 20

Step-by-step explanation:

cluponka [151]3 years ago

4 0

Answer:

12.5

Step-by-step explanation:

start by putting them in order smallest to largest and then find the middle number if there a even amount of numbers then find the middle between the middle two numbers.

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Which prism has 12 vertices? rectangular pentagonal hexagonal octagonal

lara [203]

C- Hexagonal. Basically you just double the sides of the 2d shape to find out the amount of vertices.

3 0

3 years ago

Read 2 more answers

Write a rule for each pattern. Then use your rule to find the next two terms in the pattern. The numbers are: 1, 3, 6, 10, 15, 2

Leya [2.2K]

Triangular Number Sequence

This is the Triangular Number Sequence:

1, 3, 6, 10, 15, 21, 28, 36, 45, ...

This sequence comes from a pattern of dots that form a triangle:

By adding another row of dots and counting all the dots we can
find the next number of the sequence.

A Rule

We can make a "Rule" so we can calculate any triangular number.

First, rearrange the dots (and give each pattern a number n), like this:

Then double the number of dots, and form them into a rectangle:

The rectangles are n high and n+1 wideand xn is how many dots in the triangle (the value of the Triangular Number n)

And we get (remembering we doubled the dots):

2xn = n(n+1)xn = n(n+1)/2

Rule: xn = n(n+1)/2

Example: the 5th Triangular Number is

x5 = 5(5+1)/2 = 15

Example: the 60th is

x60 = 60(60+1)/2 = 1830

Wasn't it much easier to use the formula than to add up all those dots?

5 0

3 years ago

Read 2 more answers

Find the slope of the points: (-5,10)(-3,0)(0,1)(4,-4)

klasskru [66]

Answer:

figure it out

Step-by-step explanation:

Pick two point and do 10-0 over -5+3 and you get your slope

3 0

2 years ago

Read 2 more answers

Can someone help me please, thanks.

mars1129 [50]

Answer: 2.19 pounds

Step-by-step explanation:

35 / 16 is 2.1875, if you round that you get 2.19

6 0

3 years ago

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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